Alpha and beta diversity of plants and animals along a tropical land-use gradient

Ecological Applications, 19(8), 2009, pp. 2142–2156� 2009 by the Ecological Society of America

Alpha and beta diversity of plants and animalsalong a tropical land-use gradient

MICHAEL KESSLER,1,2,10 STEFAN ABRAHAMCZYK,1,2,3 MERIJN BOS,3,4 DAMAYANTI BUCHORI,5 DADANG DWI PUTRA,6

S. ROBBERT GRADSTEIN,1 PATRICK HOHN,3 JURGEN KLUGE,1 FRIEDERIKE OREND,1 RAMADHANIEL PITOPANG,7

SHAHABUDDIN SALEH,7 CHRISTIAN H. SCHULZE,8 SIMONE G. SPORN,1 INGOLF STEFFAN-DEWENTER,3,9

SRI S. TJITROSOEDIRDJO,5 AND TEJA TSCHARNTKE3

1Albrecht-von-Haller-Institute of Plant Sciences, University of Gottingen, 37073 Gottingen, Germany2Systematic Botany, University of Zurich, Zollikerstrasse 107, CH-8008 Zurich, Switzerland

3Agroecology, University of Gottingen, 37073 Gottingen, Germany4Louis Bolk Institute, Hoofdstraat 24, 3972 LA Driebergen, The Netherlands

5Faculty of Biology, Bogor Agricultural University, Jalan Padjajaran, 16144 Bogor, Indonesia6Celebes Bird Club, c/o Balai Penelitian dan Pengembangan Zoologi, Puslitbang Biologi—LIPI, Jl. Raya Bogor Jakarta Km 46,

16911 Gibinong, Indonesia7Faculty of Agriculture, Tadulako University, Palu, Indonesia

8Department of Population Ecology, Faculty of Life Sciences, University of Vienna, Althanstraße 14, A-1090 Vienna, Austria9Department of Animal Ecology I, University of Bayreuth, Universitatsstrasse 30, 95440 Bayreuth, Germany

Abstract. Assessing the overall biological diversity of tropical rain forests is a seeminglyinsurmountable task for ecologists. Therefore, researchers frequently sample selected taxa thatthey believe reflect general biodiversity patterns. Usually, these studies focus on thecongruence of a diversity (the number of species found per sampling unit) between taxarather than on b diversity (turnover of species assemblages between sampling units). Suchapproaches ignore the potential role of habitat heterogeneity that, depending on thetaxonomic group considered, can greatly enhance b diversity at local and landscape scales. Wecompared a and b diversity of four plant groups (trees, lianas, terrestrial herbs, epiphyticliverworts) and eight animal groups (birds, butterflies, lower canopy ants, lower canopybeetles, dung beetles, bees, wasps, and the parasitoids of the latter two) at 15 sites in Sulawesi,Indonesia, that represented natural rain forest and three types of cacao agroforests differing inmanagement intensity. In total, we recorded 863 species. Patterns of species richness per studysite varied strongly between taxonomic groups. Only 13–17% of the variance in speciesrichness of one taxonomic group could be predicted from the species richness of another, andon average 12–18% of the variance of b diversity of a given group was predicted by that inother groups, although some taxon pairs had higher values (up to 76% for wasps and theirparasitoids). The degree of congruence of patterns of a diversity was not influenced bysampling completeness, whereas the indicator value for b diversity improved when using asimilarity index that accounts for incomplete sampling. The indication potential of a diversityfor b diversity and vice versa was limited within taxa (7–20%) and virtually nil between them(0–4%). We conclude that different taxa can have largely independent patterns of a diversityand that patterns of b diversity can be more congruent. Thus, conservation plans on alandscape scale need to put more emphasis on the high heterogeneity of agroforests and theoverarching role of b diversity shaping overall diversity patterns.

Key words: agroforests; biodiversity indication; community similarity; indicator species; Indonesia;species richness; Sulawesi, Indonesia; tropical rain forest.

INTRODUCTION

The immense diversity of tropical plant and animal

communities faces ever-increasing risk of extinction

(e.g., Jetz et al. 2007), but our inadequate taxonomic

knowledge of tropical taxa continues to limit the scope

and extent of biodiversity assessments. Accordingly,

researchers frequently employ selected surrogate taxa

believed to reflect overall levels of biodiversity (Pearson

1994, Prendergast 1997, Pharo et al. 1999, Rodrigues

and Brooks 2007). Such surrogate taxa can be flagship

species (chosen for their charisma), focal taxa (individ-

ual species of particular conservation concern), keystone

species (with high ecological impact), umbrella species

(requiring large areas of habitat, thereby providing

space for other taxa), or indicator taxa (with the same

habitat requirements as the species or communities that

they indicate) (e.g., Paine 1969, Wilcox 1984, Landres et

al. 1988, Mittermeier 1988, Lambeck 1997, Simberloff

Manuscript received 11 June 2008; revised 5 March 2009;accepted 6 March 2009. Corresponding Editor: H. Hillebrand.

10 E-mail: [email protected]

2142

1998, Leader-Williams and Dublin 2000, Bond 2001).

Recent reviews however, concluded that it is not yet

possible to decide when and where surrogate approaches

are effective, how surrogate species should best be

selected, and how to assess the chances of successful

surrogacy (Favreau et al. 2006, Wolters et al. 2006,

Rodrigues and Brooks 2007).

For selecting indicator taxa, the basic approach is to

compare the degree to which patterns of diversity and

community composition coincide or differ between taxa

(Landres et al. 1988, Lawton et al. 1998, Barlow et al.

2007). The congruence of such patterns can be assessed

at various spatial scales and levels of abstraction

(Whittaker 1960, 1972, Hubbell 2001). The classical

distinction is between a diversity derived from species

richness at a given site or spatial level and b diversity

derived from changes in species composition from one

site or spatial level to another (Whittaker 1960, 1972,

Cody 1986, Crist et al. 2003). Most of the diversity at

larger spatial scales is made up of b diversity (Crist et al.

2003, Crist and Veech 2006, Gabriel et al. 2006).

Moreover, a and b diversity may reveal contrasting

spatiotemporal patterns (Tylianakis et al. 2005), and the

extent to which a diversity can predict b diversity can

differ between taxonomic groups. Hence, comparing aand b diversity at local and landscape scales is an

important yet little-understood area of basic and applied

ecological research.

Although the basic conceptual distinction between aand b diversity is well established in the ecological

literature, the analytical problems that arise from this

distinction are complex and not yet fully explored,

especially in the context of biodiversity conservation.

First, levels of diversity can be measured at different

spatial scales, which may require partitioning of the

diversity (Whittaker 1960, 1972, Crist et al. 2003, Crist

and Veech 2006). Second, a fundamental complication

arises from the fact that a diversity is assessed based on

sampling units whereas b diversity is calculated based on

differences between sampling units (e.g., Legendre et al.

2005, Tuomisto and Ruokolainen 2006, Jost 2007).

Accordingly, the number and quality of data points

differ greatly. For example, in a study of 10 sites, 10 data

points can be obtained for a diversity and 45 data points

(pairwise similarity values) for b diversity. At the level of

a diversity, species identities are irrelevant during data

analysis, whereas at the level of b diversity it is the

difference between the identities that is taken into

account. At the level of a diversity, regression or

correlation analyses as well as canonical analyses may

be adequate (Magurran 2004). In contrast, at the level of

b diversity, methods for comparing matrices have to be

applied (e.g., the Mantel test) (Legendre and Legendre

1998). Thus, variances analyzed at these different levels

of abstraction are not comparable and there is no simple

relationship between them (Legendre et al. 2005),

although numerous previous studies have confused them

(Tuomisto and Ruokolainen 2006).

Within taxonomic groups, the conclusions drawn

from patterns in a and b diversity can be similar

(Clough et al. 2007), but may also differ greatly

(Tylianakis et al. 2005) because two sites with equal

species richness can share between all and none of their

species. Local species richness of mobile taxonomic

groups may approach regional species richness (Oliver et

al. 1998), whereas assemblages of less mobile species are

expected to differ between sites, leading to an increase in

species turnover and, consequently, in regional diversity.

Ultimately, the assumption that patterns for a and bdiversity change in a similar way, which is underlying

many biodiversity assessments, has been rarely tested

and may not hold true (Tylianakis et al. 2005).

In the tropics, the congruence of diversity patterns

between different taxa has mostly been studied across

large geographical regions (e.g., Beccaloni and Gaston

1995, Carroll and Pearson 1998, Oliver et al. 1998,

Myers et al. 2000, Moore et al. 2002, Duque et al. 2005,

Tushabe et al. 2006, Larsen et al. 2007, McKnight et al.

2007). Only four studies have compared small-scale

changes in taxonomically diverse groups along gradients

of land use within tropical landscapes (Lawton et al.

1998, Schulze et al. 2004, Barlow et al. 2007, Noske et al.

2008; see Plate 1). This paucity of studies is largely due

to the difficulty of sampling and identifying the

enormous biodiversity of tropical forests. As in most

assessments of indicator taxa (Wolters et al. 2006), two

of these studies focused only on the congruence of adiversity (Lawton et al. 1998, Schulze et al. 2004).

Additionally, Barlow et al. (2007) and Noske et al.

(2008) also assessed congruencies between patterns of bdiversity of various taxa along land-use gradients and

found higher congruence of b diversity than of adiversity between taxa. However, their analyses were

based on observed data for a diversity and a similarity

matrix for b diversity and are therefore statistically

incomparable. Furthermore, none of these studies

compared a diversity with b diversity within taxa.

In the present study, we linked a and b diversity of

four plant groups (trees, lianas, terrestrial herbs,

epiphytic liverworts) and eight animal groups (birds,

butterflies, lower canopy ants, lower canopy beetles,

dung beetles, bees, wasps, and the parasitoids of the

latter two) at 15 sites in natural rain forest and in three

types of cacao agroforests differing in management in

Sulawesi, Indonesia. For a direct comparison of a and bdiversity, we analyzed a diversity not only with linear

regressions, but also by comparing differences in species

numbers, a level we called Da (Appendix A), an additive

analogue to the factorial similarity in species composi-

tion used in analyses of b diversity. Furthermore,

because sampling is typically incomplete in species-rich

tropical communities (Lawton et al. 1998), we compared

observed species richness and similarity as well as

estimated species richness and similarity indices (Colwell

and Coddington 1994).

December 2009 2143TROPICAL a AND b DIVERSITY

We focused our study on plants and insects as

potential biological indicator taxa because they repre-

sent ;80% of all described species (Herrera and Pellmyr

2002) and determine important ecosystem processes

(Loreau et al. 2001, 2003, Kremen 2005). In particular,

because trees are the main structural elements of forests

and represent crucial food resources for many verte-

brates and insects (Daniels et al. 1992, Davis and Sutton

1998, Fermon et al. 2000, Greenberg et al. 2000, Willott

et al. 2000, Green et al. 2005), they are commonly used

to determine overall forest biodiversity (e.g., Oliver et al.

1998, Williams-Linera et al. 2005). Birds were included

because they are the best-known major group of

organisms and are much-used biodiversity indicators

(Garson et al. 2002, Schulze et al. 2004, Jetz et al. 2007).

By comparing the predictive values of a, Da, and bdiversity of floral and faunal groups, we provide basis

data for the use of indicator taxa in the design of policies

that aim at biodiversity conservation in tropical

landscapes.

METHODS

Study area and site selection

The study took place in an area of ;10 km2 in and

around the village of Toro in the Kulawi Valley, Central

Sulawesi, Indonesia (183002400 S, 1208201100 E, 800–900 m

above sea level; Fig. 1). Toro is located at the western

border of the 231 000-ha Lore Lindu National Park,

;100 km south of Palu, the capital city of Central

Sulawesi. The region has an annual temperature of 24.08

6 0.168C (mean 6 SE) and a monthly rainfall of 143.7

6 22.74 mm. There are no clear seasonal precipitation

fluctuations. The natural vegetation of the National

Park around the village is submontane rain forest.

The agricultural landscape in the region is highly

heterogeneous, consisting of a patchy mosaic of pasture,

hedges, and cacao-dominated agroforests, which is

typical for the region. Cacao production in the region

increased strongly in the 1990s when large areas of

coffee agroforest were converted into cacao agroforests

(Steffan-Dewenter et al. 2007). Cacao agroforests in the

FIG. 1. Location of the study sites in mature forest (MF) and different agroforestry systems (AN, agroforest with natural shadetrees; AD, agroforest with diverse planted shade trees; AF, agroforest with few planted shade tree species) around Toro Village inthe Kulawi Valley, Central Sulawesi, Indonesia. The inset shows the location of the island of Sulawesi and of the study region(star).

MICHAEL KESSLER ET AL.2144 Ecological ApplicationsVol. 19, No. 8

Toro village are owned and managed by small-scale

farmers. Shade tree management in the region is

dynamic and farmers tend to remove shade trees in

mature agroforestry systems to increase cacao produc-

tion (Steffan-Dewenter et al. 2007).

We defined a priori four habitat types with a gradient

of shade tree diversity (Gradstein et al. 2007). (1)

Mature forest sites (MF sites) were selected close to the

village, but within the national park, at least 300 m away

from forest sites where selective logging occurred, and

representative of the submontane forest in the area

(Kessler et al. 2005). In the selected sites minor rattan

extraction occurred. (2) We chose cacao agroforests with

diverse, natural shade trees (AN sites), retained after

thinning of the previous forest cover and underplanted

with cacao trees and few fruit trees. These sites had a

long history of cultivation (approximately 20–40 years,

converted from coffee to cacao agroforest approximate-

ly 10 years ago). These agroforests still had high

numbers of native shade trees, including some endemic

species. (3) We selected cacao agroforests with shade tree

stands dominated by various species of planted shade

trees (AD sites). These sites had a shorter history of

cultivation between 15 and 20 years (cacao cultivation

since approximately 10 years ago, sometimes converted

from coffee agroforests), and the majority of the former

forest canopy trees were replaced by various planted

fruit and timber trees that provided the owners with

non-market products. Among these trees were some

native species, including a few endemics. (4) Cacao

agroforests with a low diversity of planted shade trees

(AF sites) had a history of cultivation after a clearcut

12–30 years ago (converted 4–12 years ago from coffee

plantations, rice, or cornfields). Management of these

agroforests was aimed at maximum cacao productivity.

Shade was provided predominantly by the nonindige-

nous leguminous trees Gliricidia sepium (Jacq.) Walp.

and Erythrina subumbrans Merr., which are nitrogen-

fixing. Few native timber or fruit tree species were also

grown, none of which were endemic.

We selected four replicates of each of the four habitat

types, except for AN, where one plot had to be excluded

from the analysis because the local owner cut many of

the shade trees before our sampling was completed.

Agroforest sites were selected based on the age of the

cacao trees, which was at all sites between 4 and 17

years. At the time of this study, agroforestry was non-

intensive in each site, with little use of fertilizers and

pesticides. Farmers regularly pruned trees and weeded

the plantations (two to four times per year).

The minimum distance between study sites was 300 m

and the maximum distance was ;5 km. All sites were

between 850 m and 1100 m above sea level. The

agroforests did not have sharp borders with other

habitat types, but gradually changed into other forms

of land use and at the landscape scale formed a

continuous band along the forest margin. Boundaries

between agroforests were based on ownership rather

than on any physical boundaries. We marked core areas

of 503 50 m2 in the middle of each site, whose land use

and shade-tree composition was as constant as possible.

Sites belonging to the different habitat types were

geographically interspersed so that none of the individ-

ual habitat types were spatially clustered. Geographical

distance between sites was calculated as the linear

distance between the study plots based on GPS readings

and was log-transformed prior to analysis.

Species collection and determination

Trees.—Each plot was subdivided into 25 subplots of

10 3 10 m2. Within each subplot all trees with diameter

at breast height (dbh; measured at 1.3 m above the

ground surface) �10 cm were mapped and individually

numbered with aluminum tags, their dbh was measured,

and their trunk height and total height were estimated.

Specimens of all recognizable morphospecies of trees

were collected in sets of at least seven duplicates.

Identification of the plant specimens was done by R.

Pitopang, partly in collaboration with Dr. P. J. A.

Keßler (Leiden, Holland), at Herbarium Celebense,

CEB (Universitas Tadulako, Palu) and Herbarium

Bogoriense, BO (Bogor). Vouchers were deposited in

the herbaria BIOT, BO, CEB, GOET, and L. Specimens

that could not reliably be named were grouped into

morphospecies.

Lianas and herbs.—In each study plot of 50350 m2 10

subplots of 23 2 m2 each were randomly placed. Within

these, all herb and liana species were inventoried,

collected, and determined as detailed for the trees.

Epiphytic liverworts from lower canopy trees.—Two

trees with a height up to 8 m, a dbh ranging between 20

and 60 cm, and comparable bark texture were selected in

each study plot. In the agroforest sites these require-

ments for tree structure were fulfilled by cacao trees and

in natural forest sites by trees in the understory. Each

tree was divided into zone 1 (tree base up to the first

ramification), zone 2 (inner crown), and zone 3 (outer

crown), according to modified Johansson zones for

small trees (Johansson 1974). Within subplots of 200

cm2, liverworts were sampled from each cardinal

direction in all three zones. The identification was

carried out by M. Burghardt, S. R. Gradstein, and

S. G. Sporn (Gottingen, Germany). Vouchers were

deposited in CEB, GOET, and L. Specimens that could

not reliably be named were sorted to morphospecies.

Birds.—In February and March 2007, each plot was

visited on two mornings from 05:30 to 10:30. Birds were

recorded visually and acoustically and by systematic

tape recordings (Parker 1991, Abrahamczyk et al. 2008).

For every species we recorded the number of individuals

present simultaneously in the plot. During the second

visit, only additional records (new species or more

individuals of the same species) were considered. Species

flying only above the canopy such as swifts (Apodidae)

and raptors were excluded from the analysis. For

taxonomy we followed Coates and Bishop (1997).


Butterflies.—Butterflies were captured alive in traps

baited with rotten mashed bananas. A detailed descrip-

tion of the trap design can be found in Daily and Ehrlich

(1995). Traps were suspended from tree branches with

strings ;1.5 m above the ground. To prevent ants from

entering the traps, branches touching the traps were

removed and the string was prepared with sticky glue.

At each location four traps were set up on the corners of

each study plot. Trapping was conducted in March

2007, with nine days of trapping per study site. The

majority of specimens could be identified in the field

and, therefore, trapped specimens were released imme-

diately afterwards. To avoid pseudoreplicates all but-

terflies were marked with a number on their forewing.

Butterflies were identified according to Aoki et al.

(1982), D’Abrera (1985), Tsukada et al. (1985), Tsukada

(1991), and Vane-Wright and Fermon (2003). Butterfly

communities are known to vary seasonally (Barlow et al.

2007), but in our study area seasonality appears to affect

only the abundance of individual species and not species

richness and composition (C. H. Schulze, unpublished

data).

Ants and beetles from lower canopy trees.—Within

each study plot, four trees were selected, which were of

similar age and size. These were cacao trees in the

agroforests (n ¼ 48; height, 3.4 6 0.56 m) and small,

shade-dwelling lower canopy trees (n¼ 15; height, 6.3 6

1.90 m) with canopy sizes similar to those of the selected

cacao trees at natural forest sites. At one forest site, ants

and beetles from only three trees could be sampled due

to a technical problem. In order to characterize the

forest insect fauna as completely as possible, we also

sampled insects on a diverse set of trees in the forest

understory. The 15 trees in the forest sites were identified

by R. Pitopang and belonged to 14 species of 10 families.

Only on one occasion were two sampled trees in the

same forest site of the same family. None of the forest

trees were recorded flowering or fruiting at the time of

sampling. At the time of the survey, cacao in the region

was between a main flowering and a harvesting period,

although minor flowering and fruiting occurred

throughout the year. The lower canopy-dwelling ant

and beetle fauna were sampled using canopy knock-

down fogging, which is an effective and widely used

technique for collecting arthropods from tree crowns

(Perfecto et al. 1997, Lawton et al. 1998). With a

SwingFog TF35, a fog of 1% pyrethroid insecticide

(Permethrin; Mitra Envitech, Bogor, Indonesia) was

blown horizontally into the target canopy to avoid

collecting insects from higher canopy layers. Killed

arthropods were collected from a 4-m2 sheet of white

canvas placed directly under each tree. We randomly

selected one site per day and sampled all four trees

between 08:00 and 09:00 at the time of day of lowest

wind speed and rainfall probability between 17 Decem-

ber 2003 and 1 January 2004. The collected beetles and

ants were sorted into units based on external morphol-

ogy (morphospecies). Ant sorting was carried out by A.

Rizali (IPB Bogor, Indonesia), based on literature

(Bolton 1994) and reliable digital resources (available

online).11 Beetles were identified and sorted by C. Bayer,

B. Buche, and A. Rizali. Where necessary, beetle

morphospecies were sorted based on genitalia prepara-

tions. All morphospecies were photographed and posted

on the Internet through which more than 50 specialists

internationally contributed with identifications based on

the photographs and continued further taxonomic work.

Dung beetles.—Dung beetles were collected using

baited pitfall traps as described in Shahabuddin et al.

(2005). Traps were baited with ;20 g of fresh cattle (Bos

taurus) dung. The dung was wrapped in a small square

of textile and fixed with a string at the top of the trap,

which was covered by a metal roof as protection against

sun and rain. Five traps were set up along an 80-m

transect and exposed for two days (Shahabuddin 2007).

After removal from the traps, specimens were preserved

in Scheerpelz solution (75% ethanol, 5% acetic acid, 20%

water) as recommended by F. T. Krell (unpublished

manuscript). Dung beetles were sampled once per month

between March and August 2005. Identification of

specimens was done in close collaboration with B.

Buche (Berlin, Germany) using available keys (e.g.,

Balthasar 1963) and the reference collection of the

Bogor Zoological Museum of LIPI (Bogor, Indonesia).

Unidentified species were sorted to morphospecies.

Bees, wasps, and their parasitoids.—Trap nests offer

standardized nesting sites for aboveground-nesting bees

and wasps and can therefore be used to experimentally

study these insects. They were constructed from PVC

tubes with a length of 28 cm and a diameter of 14 cm.

Internodes of the reed Saccharum spontaneum (Poaceae)

with varying diameter (3–25 mm) and a length of 20 cm

were inserted into these tubes to provide nesting sites

(following Tscharntke et al. 1998). Twelve trap nests

(four in each stratum) were installed from October 2004

until September 2005 in three different heights from

understory (U) and intermediate tree height (I) to the

canopy (C), where we placed the trap nests with a

crossbow and a line. Trap nests were checked every

month and bee and wasp larvae were reared for later

identification. Understory was defined as below the

cacao tree canopy, and trap nests were placed 1.5 m

above ground. Intermediate height trap nests were

placed above the cacao tree canopy and below the

shade tree canopy (4 m above the ground in high-

intensity plots and 7 m in primary forest, depending on

canopy structure). Due to technical constraints we

placed the canopy trap nests in the lower part of the

shade tree canopy. Here, trap nest heights varied

between forest habitat type due to different canopy

heights, with higher nests in primary forests and low-

intensity agroforestry systems (primary forest, 19.13 6

0.438 m; low-intensity AF, 20.89 6 0.746 m, n ¼ 16,

11 hhttp://www.antweb.orgi and hhttp://www.antbase.neti


respectively) and lower nests in medium- and high-

intensity agroforestry systems (medium-intensity AF,

16.36 6 0.619 m; high-intensity AF, 15.29 6 0.844 m, n

¼ 16, respectively). Sticky glue was applied every month

to the edge of the PVC tube to deter ants from

colonizing the trap nests. Individuals from the four trap

nests per plot and stratum and the whole year were

pooled for analysis.

Data analyses

a diversity.—For the analyses of aobs diversity we usedthe number of species recorded in each plot (Appendix

A). Because observed species richness values in field

studies are typically an underestimate of the actual

number of species occurring at a site (Colwell and

Coddington 1994), sampling completeness and estimat-

ed species richness (aest diversity) were also calculated

using the Chao2 richness estimator (Chao 1987) with

study sites as sample units, using the program EstimateS

8 Windows (Colwell 2006). The Chao2 estimator is

recommended by Walther and Moore (2005) and is

analogous to the estimator used for b diversity. To

assess the potential impact of sampling completeness, we

correlated sampling completeness with the correlation

values.

Da diversity.—In order to more directly compare aand b diversity, we calculated differences in species

richness between plots using Mantel analyses. As for adiversity, this was done for the observed and the

estimated richness values. The correlations of sampling

completeness with the correlation values that involved

two taxonomic groups were done using the lower of the

two values of sampling completeness.

b diversity.—Similarities in the species composition of

site pairs were quantified with the quantitative Sørensen

similarity index (also known as Bray-Curtis index),

which takes into account species abundances (Magurran

2004). To correct for incomplete sampling, we further

used the similarity index of Chao et al. (2005), which is

based on the above index but includes an estimation of

incompleteness. As for a diversity, this was done for the

observed and the estimated richness values, as well as

correlating sampling completeness with the Mantel

values.

Correlation analyses.—The correlation of diversity

values between taxonomic groups was calculated using

Spearman correlations (a diversity) and Mantel analyses

(Da and b diversity). Mantel analyses are correlation

tests between matrices consisting of pairwise similarities

or dissimilarities (Legendre and Legendre 1998). Prob-

abilities are assigned by repeatedly randomizing the

arrangement of similarity matrices, each time recalcu-

lating correlation coefficients and comparing the ob-

served correlation value to the randomly generated

ones. All Mantel analyses were conducted with PCOrd

4.5 (McCune and Mefford 1999), applying 9999

randomizations. Mantel analyses were also used to

assess (1) the relationship between turnover in species

composition (b diversity) and geographical distance

between sites and (2) the relationship between Dadiversity and b diversity. Correspondences of the three

measures of diversity (a, Da, and b diversity) withinstudy groups were assessed using Spearman correla-tions. When averaging R2 values, two different ap-

proaches were used because many original R values hadnegative signs that were lost when squaring them. First,

we did not consider negative values. Second, for thoseR2 values based on negative R values, we maintained thesigns. This was done because negative signs only can be

considered to have high indication value (R2 values) ifthe negative richness relationship between taxa is knowna priori. Usually, however, the implicit assumtion of

biodiversity indication is that the diversity patterns ofthe taxa are positively correlated. In this case, R2 values

resulting from squaring negative R values would bemisleading.

RESULTS

Species richness per site: a and Da diversity

In total, we recorded 863 species, with total speciesrichness per taxonomic group ranging from nine cavity-

nesting bee species to 198 canopy beetle species (Table1). Estimated sampling completeness ranged from 29%

for canopy beetles to 89% for cavity-nesting bees (Table

1).Patterns of species richness per sample site using the

observed species numbers (aobs diversity) varied betweentaxonomic groups along the land-use gradient (Fig. 2).The aobs diversity of trees, lianas, liverworts, and dung

beetles was highest in either mature forest stands oragroforest stands with natural shade trees and declined

with decreasing shade-tree diversity, whereas that ofherbs and canopy beetles showed the opposite pattern.With highest richness recorded in agroforests with

diverse natural or planted shade trees, aobs diversity ofbutterflies, bees, wasps, and their parasitoids showed a

hump-shaped pattern. Birds had an inversely hump-

TABLE 1. Number of observed species in the 15 study sites(Sobs), total number of species in the study region estimatedthrough the Chao2 estimator (Sest), sampling completeness(SC; %Sobs of Sest) for the study region in the Kulawi Valley,Central Sulawesi, Indonesia, and range of SC for theindividual plots, for the 12 study groups.

Group Sobs Sest

SC study region(%)

SC plots(%)

Trees 185 248 75 32–54Lianas 35 76 46 27–47Herbs 163 261 62 41–87Liverworts 37 58 64 32–62Birds 87 108 81 55–67Butterflies 38 78 49 18–60Ants 44 69 64 43–64Canopy beetles 198 679 29 11–23Dung beetles 25 29 86 67–88Bees 9 11 89 72–100Wasps 24 27 82 73–96Parasitoids 18 30 60 39–67


shaped pattern, and ants were the only group with no

clear response.

Linear correlation coefficients of aobs diversity per site

between the different taxa varied enormously, ranging

from�0.92 to 0.77 (Figs. 2 and 3, Appendix B). Only 15

of the 66 pairwise comparisons were statistically

significant and 10 of these significant correlations were

negative. The same analysis based on richness values

corrected for incomplete sampling with the Chao

estimator for species richness (aest diversity) resulted in

similar overall results, with the correlation values of aobsand aest within study groups ranging from R ¼ 0.64 for

butterflies to 0.98 for parasitoids (mean for all groups, R

¼ 0.90; Table 2).

A comparison of Daobs diversity among taxa using

Mantel correlation analyses gave similar results to those

obtained based on aobs diversity (correlation between

aobs and Da, R ¼ 0.99, P , 0.001), although R values

were lower and the number of significant relationships

increased to 30 (15 negative; Fig. 3, Appendix B).

Similar results were obtained comparing Daest and aestdiversity.

For none of the a diversity parameters were correla-

tion coefficients between taxa significantly correlated

with sampling completeness (aobs, R ¼ 0.09, P ¼ 0.63;

Daobs, R¼ 0.08, P¼ 0.69; aest, R¼ 0.03, P¼ 0.86; Daest,R¼ 0.04, P¼ 0.76), indicating that differential sampling

completeness did not directionally bias the analyses.

Regional species richness: b diversity

Mantel tests of correlations of bobs diversity between

taxonomic groups recovered 37 significant relationships,

all of which were positive (Fig. 3, Appendix B).

Repeating the same analysis with values corrected for

incomplete sampling (best diversity) led to similar results

(Table 2), although the correlation values were some-

what higher (Fig. 3, Appendix B). With the exception of

liverworts, ants, and bees, which mostly showed low,

nonsignificant correlation values of assemblage similar-

ity with the other groups, turnover in the species

composition of most study groups was significantly

correlated.

Geographical distance between the study plots was

significantly correlated with turnover in species compo-

sition only for birds, wasps, and parasitoids, in terms of

bobs and best diversity (Appendix B).

a and Da vs. b diversity

Within the study groups, patterns of a and Dadiversity were highly correlated, both for observed and

FIG. 2. Species richness (aobs) of 12 study groups in mature forest (MF) and three land-use systems (AN, agroforest withnatural shade trees; AD, agroforest with diverse planted shade trees; AF, agroforest with few planted shade tree species). The boxplots show medians (horizontal line in box), 50% (the box limits), and 95% of all values (brackets). Estimated values of a diversity(aest) show roughly similar patterns and are therefore not shown.


estimator-corrected values, with values ranging from R

¼ 0.62 to R ¼ 1.00 and averaging R ¼ 0.96 (Table 2).

Similarly, bobs and best diversity were highly correlated

(R¼ 0.94). In contrast, all comparisons of patterns of aand b diversity resulted in low correlation values,

ranging from R ¼�0.35 to R ¼ 0.56 and averaging R

¼ 0.13 (Table 2; Appendix B).

DISCUSSION

a diversity

Taxonomic groups differed greatly in their response to

the change from natural forests to shaded cacao agro-

forests and the subsequent loss of shade-tree diversity.

Species richness of trees, lianas, liverworts, and dung

beetles declined with increasing land-use intensity,

whereas species richness of the other taxa either did not

respond at all (ants), had various hump-shaped re-

sponses (birds, butterflies, bees, wasps, and their

parasitoids), or even increased (herbs and canopy

beetles). This variability of a diversity patterns resulted

in low congruence and predictability between the

taxonomic groups (Figs. 1–3). The patterns of species

richness were roughly similar when analyzed with the

different measures (aobs, Daobs, aest, and Daest), whichsuggests that the method of analysis and sampling

incompleteness did not strongly influence results (Fig.

3, Table 2).

One of the main questions of our study was the degree

to which patterns of species richness of a given

taxonomic group can be predicted by those of another.

In our comparisons, only between 13% and 17% of the

variance of species richness of one taxonomic group

could be predicted by the species richness of another

(Table 3). These values are in the range of those found

by Lawton et al. (1998) for eight animal groups along a

land-use gradient in Cameroon, but less than the 48%

found by Schulze et al. (2004) for five groups in

Indonesia and the 21–52% found by Barlow et al.

(2007) for 15 groups in Brazil. These numerical

differences likely reflect differences in the studied taxa

as well as the extent of the land-use gradient. The

FIG. 3. Box plot representation of the correlation (R) values of a diversity (the number of species per study plot), Da diversity(the difference of species numbers between plots), and b diversity (the difference of species composition between plots) calculatedwith the observed species numbers (obs) and the numbers corrected for sampling incompleteness (est) of each of the 12 study groupsrelative to the other 11 groups. The box plots show medians (vertical line in box), 50% (the box limits), and 95% of all values(brackets), outliers (circles), and far outliers (triangles).


habitats studied by Schulze et al. (2004) ranged from

natural forests to annual crop fields devoid of trees, and

those of Barlow et al. (2007) ranged from natural forests

to Eucalyptus plantations. Our study focused on a

comparatively limited range of natural forests and

agroforestry systems. However, our R2 values do not

reflect the negative signs of many of the individual

correlation (R) values. If these signs are maintained,

then our average R2 values range from �0.01 to 0.02

(Table 3). Thus, unless the nature of the diversity

relationship between two given study groups was known

a priori, the predictive value of patterns of species

richness between study groups in our study was

essentially zero. Thus, the differences between compar-

isons of taxonomic groups along tropical land-use

gradients likely reflect differences in the study taxa as

well as the extent of the land-use gradient.

The independent patterns that we recorded for the adiversity of a wide range of taxonomic groups are

comparable with those reported by studies that com-

pared taxa along natural ecotones. For example, along

an elevational gradient in Costa Rica, richness of palms

is highest at 100 m (Chazdon 1987), of trees at 300 m

(Lieberman et al. 1996), of moths and vascular

epiphytes at 1000 m (Cardelus et al. 2006, Brehm et al.

2007), and of ferns at 1800 m (Kluge et al. 2006). This

difficulty of predicting patterns of species richness by

indicator taxa has as well been documented for forests

on different soil types in Amazonia (Duque et al. 2005,

Tuomisto and Ruokolainen 2005) and for various taxa

in Europe and North America (e.g., Prendergast 1997,

Su et al. 2004, Wolters et al. 2006, Billeter et al. 2008).

Despite the fact that trees are the main structural

elements of forests, tree species richness was in our study

only positively correlated with the species richness of

birds, dung beetles, and lianas. This result is in support

of the idea that large groups of species depend on food

resources that are not directly related to tree species

richness, as has been suggested for canopy beetles

(Wagner 2001), social bees (Klein et al. 2003), and

canopy ants (Philpott and Foster 2005). For example,

canopy beetle assemblages in the study area were

dominated by species associated with dead wood and

related fungi (M. M. Bos and B. Buche, unpublished

data), while communities of canopy ants on cacao were

mostly affected by microclimatic changes that were

largely independent of changes in species richness of

shade trees (Bos et al. 2007).

The observation of decreased species richness in five

of seven studied insect groups in mature forest could be

related to pronounced vertical stratification reported for

tropical forest insects (e.g., Rodgers and Kitching 1998,

DeVries et al. 1999, Schulze et al. 2001, Fermon et al.

2005, Diwakar and Balakrishnan 2007). While in some

taxa species richness is more pronounced in one

vegetation layer, others contribute equally to lower

strata as well as the upper canopy (e.g., DeVries et al.

1997, Schulze et al. 2001, Stork and Grimbacher 2006).

Natural and anthropogenic forest disturbance can cause

a breakdown of vertical stratification as documented for

butterflies in selectively logged forest (Dumbrell and Hill

2005, Fermon et al. 2005), at tree-fall gaps (Hill et al.

2001), and forest edges (DeVries et al. 1997). Conse-

quently, lower vegetation strata at agroforestry sites,

particularly sites at the forest edge, may be characterized

by a rich mixture of canopy and understory species,

while lower vegetation strata of forest interior sites are

characterized predominantly by relatively few forest

understory species.

b diversity

Congruence of b diversity patterns between study

groups was higher than between a diversity patterns

(Figs. 2 and 3, Table 3). Unlike the results from the adiversity comparisons, the only negative correlations

were found for liverworts. On average, 12–18% of the

variance in b diversity could be predicted by that in

another taxonomic group, although certain pairs of taxa

TABLE 2. Linear correlations (R values) between different diversity values within the 12 study groups.

Groupaobs–aest

aobs–Daobs

aobs–Daest

aobs–bobs

aobs–best

aest–Daobs

aest–Daest

aest–bobs

aest–best

Daobs–Daest

Trees 0.97 1.00 0.95 �0.26 �0.18 0.97 0.99 �0.35 �0.27 0.96Lianas 0.83 0.99 0.79 0.16 0.08 0.82 0.98 0.11 0.11 0.79Herbs 0.98 1.00 0.98 0.22 0.30 0.98 1.00 0.25 0.34 0.98Liverworts 0.90 0.96 0.92 �0.11 0.18 0.86 0.97 �0.03 0.23 0.91Birds 0.95 0.99 0.95 0.04 0.08 0.94 0.99 0.07 0.12 0.94Butterflies 0.64 0.99 0.62 0.56 0.57 0.70 0.96 0.24 0.47 0.66Ants 0.90 0.99 0.90 �0.06 �0.06 0.91 0.99 0.00 �0.05 0.90Canopy beetles 0.96 0.99 0.96 0.29 0.27 0.94 0.99 0.13 0.12 0.94Dung beetles 0.94 0.99 0.95 �0.14 �0.19 0.91 0.98 �0.06 �0.07 0.93Bees 0.84 1.00 0.83 0.02 0.40 0.84 0.99 �0.11 0.24 0.82Wasps 0.96 0.99 0.95 0.15 0.23 0.94 0.99 0.16 0.24 0.95Parasitoids 0.98 1.00 0.98 0.39 0.37 0.98 1.00 0.44 0.42 0.98Means 0.90 0.99 0.90 0.11 0.17 0.90 0.99 0.07 0.16 0.90

Note: Abbreviations are: aobs, observed alpha diversity, i.e., the counted species number per plot; aest, estimated alpha diversity,i.e., the estimated total species number per plot; Daobs, the difference between the aobs values of two plots; Daest, the differencebetween the aest values of two plots; bobs, observed beta diversity, i.e., the observed similarity in species composition between twoplots; best, estimated beta diversity, i.e., the estimated similarity in species composition between two plots.


had much higher values, as exemplified by wasps and

their parasitoids (76%) and wasps and canopy beetles

(49%).

The low correlation coefficients between b diversity of

bees and other study groups (even with their parasitoids)

probably relate to the comparably even distribution of

the nine aboveground nesting bee species in the study

area, with most species present in all habitat types. In

contrast, the other two ‘‘independent’’ study taxa,

namely liverworts and ants, showed marked turnover

between plots and habitats. Their low correlations to

other study groups are presumably based on taxon-

specific ecological requirements. Intriguingly, in Britain

hot spots of liverwort diversity corresponded to cold

spots of birds and butterflies (Prendergast et al. 1993). In

the other taxonomic groups, variability in patterns of bdiversity did not seem to relate to general taxonomic

(e.g., plants vs. animals) or ecological (e.g., relative to

trophic level, mobility) differences, but were rather

group specific. For example, the extremely high turnover

in beetle species relates to the fact that the majority of

the recorded species aggregated on resources that were

little related to habitat type, such as deadwood and fungi

(M. M. Bos and B. Buche, unpublished data).

a vs. b diversity

In accordance with previous studies (e.g., Su et al.

2004, Tuomisto and Ruokolainen 2005, Barlow et al.

2007, McKnight et al. 2007, Noske et al. 2008) our

results show that among the 12 investigated groups, the

use of indicator taxa is most valuable when taking into

account the patterns in b diversity rather than adiversity. The higher congruence of patterns of bdiversity than of a diversity is readily explained

biologically (Su et al. 2004, McKnight et al. 2007,

Rodrigues and Brooks 2007). Along an ecological

gradient (land use, elevation, climate, soil fertility,

etc.), b diversity of all taxa will tend to shift more

strongly the more divergent the ecological conditions

are. In contrast, species richness can be similar even

under strikingly different conditions (Fig. 4).

Within the studied taxonomic groups, the congruence

of patterns of a and b diversity was fairly low, with R2

values averaging 0.07–0.20 (Table 3). Across the

different taxa, only 1–4% of the variance of b diversity

of a given group could be predicted by the patterns of adiversity of another group, and vice versa. If the sign of

negative correlations is maintained in calculating these

values, the values are further reduced to 0–1%. The

main conclusion to be drawn from this is that indication

of patterns of b diversity through patterns of a diversity

of another taxon is practically impossible within our

study system and difficult using the same taxon. These

results are not surprising considering the low correlation

values within each of the diversity levels and the fact

TABLE 3. Mean determination coefficients (R2 values) of a given study group relative to the other 11 study groups, for sixmeasures of diversity.

Group

aobs aest Daobs Daest bobs best

� þ � þ � þ � þ � þ � þ

Trees 0.33 �0.14 0.25 �0.07 0.28 �0.14 0.19 �0.03 0.15 0.15 0.21 0.20Lianas 0.07 0.01 0.06 0.00 0.05 0.01 0.04 �0.01 0.11 0.11 0.15 0.15Herbs 0.29 �0.02 0.30 0.00 0.25 �0.02 0.26 0.00 0.17 0.17 0.25 0.25Liverworts 0.05 �0.02 0.05 0.00 0.05 �0.02 0.05 0.00 0.01 0.00 0.02 0.01Birds 0.21 �0.09 0.20 �0.05 0.18 �0.09 0.19 �0.07 0.14 0.13 0.21 0.20Butterflies 0.05 0.00 0.05 0.03 0.04 0.00 0.04 0.03 0.07 0.07 0.12 0.12Ants 0.07 0.04 0.09 0.05 0.07 0.04 0.08 0.05 0.03 0.03 0.08 0.08Canopy beetles 0.15 0.04 0.18 0.06 0.12 0.04 0.13 0.05 0.19 0.19 0.28 0.28Dung beetles 0.17 �0.04 0.14 �0.02 0.12 �0.02 0.11 0.01 0.10 0.10 0.17 0.17Bees 0.07 0.05 0.10 0.09 0.08 0.06 0.08 0.07 0.04 0.04 0.09 0.09Wasps 0.29 �0.01 0.27 0.05 0.25 0.01 0.21 0.04 0.27 0.27 0.31 0.31Parasitoids 0.27 0.02 0.21 0.08 0.26 0.03 0.18 0.06 0.22 0.22 0.31 0.31Means 0.17 �0.01 0.16 0.02 0.15 0.00 0.13 0.02 0.13 0.12 0.18 0.18

Notes: Because the directions of the relationships (negative, positive) are lost when R values are squared, R2 values werecalculated both not maintaining the original signs (�) and maintaining them (þ). Abbreviations are: aobs, observed alpha diversity,i.e., the counted species number per plot; aest, estimated alpha diversity, i.e., the estimated total species number per plot; Daobs, thedifference between the aobsvalues of two plots; Daest, the difference between the aest values of two plots; bobs, observed betadiversity, i.e., the observed similarity in species composition between two plots; best, estimated beta diversity, i.e., the estimatedsimilarity in species composition between two plots.

TABLE 2. Extended.

Daobs–bobs

Daobs–best

Daest–bobs

Daest–best

bobs–best

�0.24 �0.14 �0.28 �0.21 0.950.13 0.08 0.09 0.11 0.960.18 0.26 0.26 0.34 0.990.09 0.40 �0.06 0.26 0.810.08 0.15 0.10 0.16 0.970.54 0.58 0.16 0.38 0.92�0.01 �0.02 �0.04 �0.12 0.930.27 0.26 0.16 0.15 0.99�0.09 �0.13 �0.11 �0.11 0.980.02 0.41 �0.03 0.24 0.760.10 0.20 0.14 0.22 0.980.37 0.35 0.44 0.43 0.990.12 0.20 0.07 0.15 0.94


that concordances across levels of diversity are neces-

sarily less tight (Tylianakis et al. 2005, Clough et al.

2007).

Implications for biodiversity sampling

Our study shows that indication of diversity patterns

of a given taxon by another taxon remains a difficult

task (Prendergast 1997, Favreau et al. 2006, Wolters et

al. 2006, Billeter et al. 2008). This has previously been

shown for patterns of a diversity both at the local level

(Lawton et al. 1998, Schulze et al. 2004, Favreau et al.

2006, Wolters et al. 2006) and on regional to continental

scales (Beccaloni and Gaston 1995, Carroll and Pearson

1998, Myers et al. 2000, Moore et al. 2002, Tushabe et

al. 2006, Billeter et al. 2008). We also found that

different approaches to calculating a diversity (species

numbers vs. differences between species numbers;

observed vs. estimated values) resulted in roughly

similar values. In particular, the sampling completeness

of the different study groups did not appear to influence

the observed patterns of a diversity directionally or in a

predictable way.

Our study further showed that b diversity is more

consistent across the study taxa, and although overall

levels of congruence were rather low (see also Tuomisto

and Ruokolainen 2005, Noske et al. 2008), negative

correlation values were very rare and indication is

possible, if on a low level. Furthermore, in contrast to adiversity, the correction for sampling incompleteness

increased the indicative value of b diversity, showing

that this approach should be preferred when comparing

b diversity of incomplete samples.

Implications for biodiversity conservation

in tropical landscapes

Because the potential conservation value of an area

depends more on which species occur there, rather than

how many species (Gaston 1996), the reasonably good

FIG. 4. Examples of the relationships of a diversity (the number of species found per sampling unit) and b diversity (theturnover of species assemblages between sampling units, also known as the Bray-Curtis similarity) between different taxa. (A) Thespecies richness of trees and herbs are closely inversely correlated due to the high abundance of herbs in open plantations with fewshade trees. This high species richness of herbs in turn correlates positively with the species richness of butterflies (C), many ofwhich use the herbs as food plants. Species numbers of trees and butterflies are not significantly correlated (B). In contrast to adiversity, b diversity is positively correlated between trees and herbs (D) as well as between herbs and butterflies (F), and slightly,but not significantly so, between trees and butterflies (E). Thus, changes in land use affect b diversity of different taxa in roughly thesame way, whereas trends in a diversity can be completely unrelated or even opposite. Trend lines in nonsignificant relationships areshown by dotted lines for clarity (B and E).

* P , 0.05; ** P , 0.01; *** P , 0.001.


indication of b diversity, in contrast to a diversity, is of

considerable practical interest (McKnight et al. 2007).

Due to contrasting patterns, indication of b diversity

through a diversity is also not possible, suggesting that

diversity assessments based exclusively on patterns of adiversity miss the important b diversity component.

Perhaps most importantly, our study shows that, at

least at the scale of our study, trees, despite being the

main structural components of forests and agroforests

and providing resources to many other organisms, are

not better suited as indicators than other taxa. Whether

the relatively high correlation values obtained for both

a and b diversity patterns for some taxon pairs such as

trees and birds (a diversity) or wasps and their

parasitoids (b diversity) represent general relationships

that can be used for biodiversity indication remains to

be confirmed by further studies. Our results thus

support those from other multi-taxa studies of the

consequences of land-use change and suggest that the

impact of management changes on the diversity and

composition of a given taxon in tropical agroforests

cannot be predicted reliably from other taxa, although

changes in species composition (b diversity) appear to

be more consistent than those of species richness (adiversity).

ACKNOWLEDGMENTS

This study was funded by the German Research Foundation(DFG), grant SFB-552 STORMA (Stability of Tropical Rain-forest Margins). We thank Pak Mann, Arifin, Daniel Stieten-roth, Adam Malik, Wolfram Lorenz, Surya Tarigan, and allplantation owners for their help in this work. We also thankMirkka Jones and Hanna Tuomisto for comments on themanuscript.

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APPENDIX A

An example of the calculation of the six different diversity parameters used in the study (Ecological Archives A019-090-A1).

APPENDIX B

Linear and Mantel correlations between study groups (Ecological Archives A019-090-A2).


Alpha and beta diversity of plants and animals along a tropical land-use gradient

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