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Journal of Tropical Ecology (2010) 26:509–519. Copyright © Cambridge University Press 2010
doi:10.1017/S0266467410000301
Amphibian community structure as a function of forest type
in Amazonian Peru
Rudolf von May
∗,1
, Jennifer M. Jacobs†, Roy Santa-Cruz‡
,
§, Jorge Valdivia§, Jusmell M. Huam
´
an#
and Maureen A. Donnelly
∗
∗
Department of Biological Sciences, Florida International University, 11200 SW 8
th
Street, OE-167, Miami, Florida, USA
† Department of Integrative Biology, University of California, Berkeley, California, USA
‡ Museo de Historia Natural, U niversidad Nacional de San Agust
´
ın de Arequipa, Arequipa, Per
´
u
§ Facultad de Ciencias Biol
´
ogicas y Agropecuarias, Universidad Nacional de San Agust
´
ın de Arequipa, Arequipa, Per
´
u
# Facultad de Ingenier
´
ıa Forestal, Universidad Nacional Amaz
´
onica de Madre de Dios, Puerto Maldonado, Madre de Dios, Per
´
u
(Accepted 15 May 2010)
Abstract: The potential effect of forest type on the structuring of animal communities in western Amazonia remains
poorly understood. In this study,we tested the hypothesis that amphibian species richness, composition and abundance
differ across forest types in the lowland rainforest of south-eastern Peru. By using 320individual transects, we compared
the amphibian assemblages across four major forest types (floodplain, terra firme, bamboo and palm swamp) at each
of four sites separated by 3.5–105 km. We identified 1967 individuals of 65 species in 11 families and found that a
large proportion of the amphibian diversity in this region is attributed to habitat-related beta diversity. Overall, we
found that forest type is more important than site in predicting both s pecies composition and abundance. We also
found that, when analyses are conducted separately for each forest type and include species abundance data, similarity
between assemblages decreases with increasing geographic distance. In contrast to studies that considered species
presence/absence but ignored species abundances, our results highlight the importance of including abundance data
in the assessment of animal diversity patterns in western Amazonia. We conclude that evaluating community structure
across forest types can improve our understanding of diversity patterns in this region.
Key Words: Amazon, beta diversity, frogs, habitat filtering, species sorting, tropical forest
INTRODUCTION
General assessments of biodiversity patterns in western
Amazonia have focused on a variety of taxa, but data
on animal community structure across major forest
types remain scarce (Larsen et al. 2006, Pearson &
Derr 1986, Peres 1997, S
¨
a
¨
aksj
¨
arvi et al. 2006). Most
previous studies focusing on amphibian diversity in
lowland Amazonia have compared assemblages across
different sites without taking into account the effect of
naturally occurring forest types on community structure
(Azevedo-Ramos & Galatti 2002, Blair & Doan 2009,
Dahl et al. 2009, Doan & Ariz
´
abal 2002). In other
studies, researchers have defined habitats based on
the degree of anthropogenic disturbance (e.g. primary
forest, secondary forest, plantation; Gardner et al. 2007a,
1
Corresponding author. Email: rvonmay@gmail.com
Pearman 1997) or focused on only one type of forest
(Aichinger 1987). In some cases, results from a study
conducted in a single site and a single forest type (Allmon
1991) were regarded as representative of entire South
American forests (Vasudevan et al. 2008).
A recent comparison of amphibian assemblages across
four major forest types in south-eastern Peru showed that
these habitats may contribute to the local variation in
amphibian species richness and composition (von May
et al. 2009a). Because this comparison was made at only
one site, and o nly species presence/absence data were
used, the next step is to evaluate whether similar patterns
exist at other sites in the region. A large-scale comparison,
incorporating abundance data (this paper), allows us to
improve our understanding of amphibian diversity in the
region. Moreover, the inclusion of additional sites allows
us to explore the relationship between species composition
and geographic distance. Some studies have illustrated a
negative association between similarity and geographic
510 RUDOLF VON MAY ET AL.
distance (Azevedo-Ramos & Galatti 2002, Duellman &
Thomas 1996), whereas other studies have found no
association between similarity and geographic distance
(Dahl et al. 2009, Doan & Ariz
´
abal 2002).
Here, our primary goal was to test the hypothesis that
amphibian species richness, composition and abundance
differ across forest types in the lowlands of south-eastern
Peru. We focused on four major forest types that are
widespread and cover most of the lowlands of south-
western Amazonia: floodplain forest, terra firme forest,
bamboo forest and palm swamp (Griscom et al. 2007,
Mostacedo et al. 2006, Phillips et al. 1994, Pitman et al.
1999). Given that other animal assemblages have been
shown to vary according to the type of forest (Larsen
et al. 2006, Pearson & Derr 1986, Peres 1997), we
predicted that amphibian species richness and abundance
distribution patterns would also differ across forest types.
Additionally, because geographic distance may influence
the patterns of community structure (Ernst & R
¨
odel 2005,
2008; Parris 2004), we tested the hypothesis that sites
close to each other are more similar than sites farther
away from each other.
MATERIALS AND METHODS
Study sites
We surveyed four major forest types at each of four
sites in the Madre de Dios region of south-eastern Peru:
Los Amigos Research Center (CICRA is the Spanish
acronym), 12
◦
34
07
S, 70
◦
05
57
W, 270 m asl; Centro
de Monitoreo 1 (CM1), 12
◦
34
17
S, 70
◦
04
29
W, c.
250 m asl; Centro de Monitoreo 2 (CM2), 12
◦
26
57
S,
70
◦
15
06
W, 260 m asl; Tambopata Research Center
(TRC), 13
◦
08
30
S, 69
◦
36
24
W, 350 m asl. The first
three sites are 3.5–25 km away from each other and the
fourth site (TRC) is 80–105 km away from the other
three sites. At CICRA, annual rainfall is variable and
ranges between 2700 and 3000 mm (http://atrium-
biodiversity.org). The dry season (June–September) has
less rainfall and is slightly cooler than the wet season.
The mean annual temperature ranges between 21
◦
Cand
26
◦
C (N. Pitman, pers. comm.). Details about our study
sites can be found in Kratter (1997), Doan & Ariz
´
abal
(2002) and maps are available at the Atrium Biodiversity
Information System site (http://atrium-biodiversity.
org).
Forest types
We followed the general categories of forest types
recognized by plant ecologists working in Madre de Dios
and nearby regions (Griscom & Ashton 2006, Griscom
et al. 2007, Mostacedo et al. 2006, Olivier 2007, Pitman
et al. 1999, Silman et al. 2003). We did not follow t he
categories proposed by Phillips (1993) and Phillips et al.
(1994) because their classification was limited to a small
area (112.4 km
2
) that represents only 0.13% of Madre de
Dios (85 300 km
2
) and does not include our study sites.
The floodplain forest can be classified in two
general categories: mature floodplain forest and primary
successional floodplain forest (Pitman et al. 1999). We
sampled only in mature floodplain forest (hereafter
referred to as floodplain), which exhibits high plant
diversity, a 25–35-m-tall canopy (except for gaps),
numerous lianas and emergent tree species. F looding may
occur once a year or once every few years depending on
river level fluctuations; inundation varies from > 1.0 m
near the river to 0.1 m on more elevated terraces
(Hamilton et al. 2007). Temporary bodies of water are
common during the wet season as a result of rainwater
accumulation.
The terra firme forest (hereafter referred to as terra
firme) is found on higher terrain that is never flooded by
the river (Pitman et al. 1999). The terra firme at our sites
is 20–40 m above the floodplain and is primarily found
on flat upland terraces dissected by small permanent or
temporary streams. We sampled on these terraces and
avoided streams and ravines bordering streams. The terra
firme has fewer temporary ponds than the floodplain
because little rainwater is retained in the upper soil layers.
The terra firme also exhibits high plant diversity, > 32 m
tall canopy (except for gaps) and many species of emergent
trees (Griscom & Ashton 2006).
The bamboo forest (hereafter referred to as bamboo) is
patchily distributed and covers extensive areas dominated
by two native bamboo species, Guadua sarcocarpa and
G. weberbaueri (Griscom et al. 2007, Olivier 2007). At
our sites, bamboo forms patches of variable size (c.1ha
to 100+ ha), is interspersed within the terra firme and its
canopy is lower (up to 25 m) than the terra firme canopy
(Griscom & Ashton 2006).
The palm swamp forms patches of variable size,
typically between tens to hundreds of hectares, dominated
by the native palm Mauritia flexuosa. Palm swamp soils
can be permanently or seasonally flooded, are nitrogen-
limited and have abundant organic matter (Householder
2007, Kahn 1991). Slow decomposition results in acidic
soil and water (pH 4.5–5.5; J. Janovec, pers. comm.). More
than 50% of the palm swamps were flooded (0.1–0.7 m)
during the study.
Sampling methods
We conducted standardized sampling between 18
January and 16 April 2008 (wet season). The average
rainfall, as measured between December 2007 and April
Beta diversity of Amazonian amphibians 511
2008, was 159.4 mm mo
−1
in 2008 (http://atrium-
biodiversity.org). We sampled on flat terrain in all forest
types and avoided slopes that mark the transition between
forest types. These slopes represent an ecotone and may
harbour a mix of species from different habitats. To
account for the interspersion of replicated samples, we
established twenty 50-m transects per habitat at each site.
This number was selected following our preliminary work
at CICRA and published reports from other tropical forests
(Veith et al. 2004). We selected habitat patches dissected
by at least 200 m of trail at each site and all transects
were established away from trails to avoid bias associated
with potential trail effects (von May & Donnelly 2009).
We used a random number table (Heyer et al. 1994)
to determine the distance along the trail from which
each transect began. All transects were perpendicular
with respect to the main trail, began 5 m away from
the trail and were separated by at least 30 m. Because
transects were established in several patches, transects
representing each forest type were separated by up to
3 km. Thus, transects included the variability associated
with each habitat. We used a compass and a 50-m string
with orange flagging marked at every 5 m to establish
each transect. Understorey vegetation was only disturbed
when tangled vegetation blocked access; in those cases,
we used a machete to clear a narrow path and waited
for at least 3 d before sampling. Our sampling effort was
320 transects, and each transect was sampled only once
to maintain independent sampling units (as opposed to
other studies, where transects were re-sampled multiple
times).
We sampled all transects at night (19h00–01h30)
because most amphibians are nocturnal, and night
sampling using visual encounter surveys (VES; Crump
& Scott 1994) is more effective than other sampling
methods (Doan 2003). Our preliminary work showed that
nocturnal surveys were effective for finding both diurnal
and nocturnal species. Moreover, previous research in
other tropical forests has shown that some diurnal species
are found more often at night than during the day
(Lieberman 1986).
We used distance-and-time-constrained VES (50 × 4-m
transect in 30 min) as an alternative to the traditional VES
method. To reduce the variation in species detectabilities,
which can be considered an issue in VES (Pearman
et al. 1995), our search protocol included disturbance
of the substrate (in traditional VES, the substrate is not
effectively disturbed during search). While walking along
a transect, we first visually scanned the area using our
lights and then disturbed the substrate with a snake
hook. Any frog that was not detected by our first visual
assessmentwas eventually spotted as it jumped awayfrom
its original location. All individuals located within 2 m
on either side of the centre line of the transect, and on
substrate up to 2 m in height, were captured. We placed all
encountered individuals in separate plastic bags that were
tied to the string marking the centre line of the transect.
All sampling was conducted by two or three observers
with headlamps. If there were three observers, only the
first two actively searched while the third one recorded
data. If there were only two observers, data recording was
done at the end of each survey. Each transect took 30 min
to complete, i.e. 1 person-hour was effectively invested
per transect. We identified, measured and released all
captured individuals.
We collected voucher specimens only when field
identification was not possible. These specimens were
identified and deposited at the Museo de Historia Natural
of Universidad Nacional Mayor de San Marcos, in Lima,
Peru. Species nomenclature follows the on-line reference
to amphibian taxonomy, Amphibian Species of the World
(http://research.amnh.org/herpetology/amphibia/index.
php).
Data analysis
Because our primary interest was to evaluate amphibian
community structure across forest types, we grouped and
analysed most data with respect to forest type. In this
paper, the term ‘abundance’ refers to ‘relative abundance’
under the a ssumption that the number of individuals
counted in a transect represents the abundance in which
species occur in a particular place and time.
We first used sample-based rarefaction curves to
compare patterns of species richness among forest types.
We pooled data collected at all sites and used the pro-
gram EstimateS, version 8.0 (http://viceroy.eeb.uconn.
edu/estimates) for this comparison (Gotelli & Colwell
2001). We then used analysis of variance ( ANOVA) to
compare the average species richness among forest types,
and graphically compared the minimum and maximum
number of species recorded in each forest type across
all sites. We plotted rank-abundance curves to compare
the species abundance distributions among forest types.
We arbitrarily defined as abundant species those that
were represented by at least 20 individual observations
(which correspond to approximately 1% of all identified
individuals).
We used the additive partitioning approach (Lande
1996) to describe patterns of beta diversity across the
landscape. According to Lande (1996), gamma diversity
is composed by the addition of the alpha and beta
components, γ = α + β. We obtained γ by pooling the
number of species recorded in all habitats ( = forest types),
while α represented the mean number of species recorded
in each habitat and β represented the mean number of
species not found in each habitat. We estimated β
by β =
γ − α. Researchers have used this approach to describe
512 RUDOLF VON MAY ET AL.
0
10
20
30
40
50
60
0 100 200 300 400 500 600 700
Number of individuals
Number of species
Floodplain
Terra firme
Bamboo
Palm swamp
Figure 1. Rarefaction curves based on data collected at four sites (CICRA, CM1, CM2 and TRC). Each curve represents the expected number of species
for a given number of observed individuals, though the rarefaction was based on randomization of sample order. The bars indicate ± 1 SD. The
dotted vertical line indicates the point of comparison for the four curves.
amphibian diversity patterns across habitats (Gardner
et al. 2007a, Pineda & Halffter 2004).
We used non-metric multidimensional scaling (nMDS)
plots to visualize patterns of community structure. For
this analysis, each site had four habitat patches (sensu
Leibold et al. 2004) and each patch represented a
different forest type. The nMDS plots were based on
a Bray–Curtis dissimilarity matrix, first using species
presence/absence and then using species abundance data
(Clarke & Warwick 1994). We also ran an analysis
of similarity (ANOSIM) to test for relationship among
forest types and calculated the similarity percentage
contribution (SIMPER) to evaluate which species were
most important in determining the dissimilarity between
pairs of groups (Clarke & Warwick 1994). We applied
the indicator species analysis procedure (Dufrene &
Legendre 1997) to determine which species can be used as
indicators of particular forest types. We used the statistical
software Primer-E, version 5.0 (Clarke & Warwick 1994)
to generate the nMDS plots and to run the ANOSIM and
SIMPER, and we used PC-ORD version 5.0 (MjM Software,
Gleneden Beach) for the indicator species analysis.
We used a Mantel test to evaluate the correlation
between similarity and geographic distance. As in the
previous analyses, each site had four habitat patches. We
used a matrix containing presence/absence data (Jaccard
similarity index) and a matrix with the geographic
distance among habitat patches. We also used a matrix
containing abundance data (Bray–Curtis dissimilarity
index) and a matrix with the geographic distance among
habitat patches. First, we tested whether there was a
correlation between similarity and distance when forest
types are not taken into account (as in previous studies).
For this analysis, our matrix contained all possible
pairs of habitat patches. Second, we tested whether
there was a correlation between similarity and distance
when forest types are taken into account. For this
analysis, we ran a separate Mantel test for each forest
type. We also performed Pearson correlations on the
same dataset to further assess the relationship between
similarity and distance (the values of distance, originally
measured in km, were log-transformed in this case).
We used an Excel spreadsheet integrated with PopTools
(http://www.cse.csiro.au/poptools) to perform Mantel
tests and SPSS version 14.0 (SPSS Inc., Chicago) for the
correlations.
RESULTS
We captured and identified 1967 individuals of 65
amphibian species at four sites (Appendix 1). Fifty-one
individuals (2.59%) escaped prior to identification and
were not included in the analyses. As is typical for most
amphibian communities in the Neotropics, the family
Hylidae was the most species-rich (26 species). Ten
other amphibian families were recorded, three of which
were represented by only one species (Appendix 1). The
only non-anuran family was Plethodontidae (lungless
salamanders).
Oursample size wassufficient to characterizethe species
richness and composition across forest types because the
species accumulation curves approached an asymptote
(Figure 1). Overall, we recorded more individuals and
Beta diversity of Amazonian amphibians 513
0
5
10
15
20
25
30
Floodplain Terra firme Bamboo Palm swamp
No. of species
Figure 2. Mean (markers), minimum and maximum (bars) number of
amphibian species recorded in each forest type, based on data collected
at four sites (CICRA, CM1, CM2, TRC). Bars on top denote significantly
different groups in Student–Newman–Keuls post hoc comparisons.
species in the floodplain than in other forest types
(Figure 1). Accordingly, the mean number of species
in the floodplain was higher than in other forest types
(ANOVA, F
3,12
= 5.37, P = 0.014) and the maximum and
minimum numbers of species in this habitat were also
higher than in other habitats (Figure 2). We recorded
nearly the same number of individuals and species in the
terra firme and bamboo, and both forest types exhibited
similar pattern of species accumulation. We recorded the
lowest number of species in the palm swamp, although
this habitat ranked second in terms of total abundance.
We found the same pattern when comparing species
richness based on a standardized abundance (e.g. 350
individuals; Figure 1).
The gamma diversity, according to the additive
partitioning approach, can be expressed as: 65 [γ ] =
18.2 [α] + 46.8[β].Withineach forest type, beta diversity
contributed about half of the total gamma diversity
(floodplain = 52%, terra firme = 54%, bamboo = 46%,
palm swamp = 53%). Overall, mean diversity and
evenness were higher in the floodplain than in the other
forest types (Appendix 1).
We found differences in species abundance distribu-
tions among forest types, as indicated by different shapes
of the rank-abundance distribution curves (Figure 3).
We found more abundant species (i.e. those with > 20
individuals) in the floodplain than in the other forest
types, and the slope of the floodplain curve resembles
the curve for all data combined. The species abundance
distributions in the terra firme and the bamboo resemble
each other and indicate that these forest types have less
abundant species compared with the floodplain. The
abundance distribution in the palm swamp also indicates
that this forest type has less abundant species compared
with the floodplain (Figure 3).
Our indicator species analysis confirmed the patterns
exhibited by the rank-abundance distribution curves
and detected additional species that could be used to
characterize each forest type. Overall, between one and
six species could be used to characterize each forest
type (these species are labelled with an asterisk in
Appendix 1) and they contribute more than 50% to
0.0
0.5
1.0
1.5
2.0
2.5
0 5 10 15 20 25 30 35 40 45 50 55 60 65
Species abundance rank
Log(abundance + 1)
All data
Floodplain
Terra firme
Bamboo
Palm swamp
A
B
C
D
H
K
R
EF
G
I
J
L
M
(E, D, G, I, F, C, B, M, A, R)
(A, B, C, G, D, E, Q)
(A, B, D, C, E, U, O)
(H, F, C, J, K, L)
(Most abundant species)
0.0
0.5
1.0
1.5
2.0
2.5
0 5 10 15 20 25 30 35 40 45 50 55 60 65
Species abundance rank
Log(abundance + 1)
All data
Floodplain
Terra firme
Bamboo
Palm swamp
A
B
C
D
H
K
R
EF
G
I
J
L
M
(E, D, G, I, F, C, B, M, A, R)
(A, B, C, G, D, E, Q)
(A, B, D, C, E, U, O)
(H, F, C, J, K, L)
(Most abundant species)
(E, D, G, I, F, C, B, M, A, R)
(A, B, C, G, D, E, Q)
(A, B, D, C, E, U, O)
(H, F, C, J, K, L)
(Most abundant species)
Figure 3. Rank-abundance distribution curves of species recorded across sites and forest types. The rank-abundance curve for all data combined is
shown on top of the individual curves for each forest type. Each forest type includes pooled data from four sites. The most abundant species (> 20
individuals observed across all sites) are labelled with particular letters in the curve for all data; only four of the 18 most abundant species were
not labelled, but they are N, O, P, Q (between labels M and R). For each forest type, the most abundant species ( > 70%) are labelled in decreasing
order in parentheses. The relative abundance was transformed to log(abundance + 1), where abundance is the number of individuals recorded in
each forest type. Letter codes: A = Pristimantis reichlei,B= Leptodactylus (Adenomera)sp.,C= Rhinella margaritifera,D= Pristimantis toftae,E=
Engystomops freibergi,F= Leptodactylus petersii,G= Hamptophryne boliviana,H= Dendrophryniscus minutus,I= Chiasmocleis ventrimaculata,J=
Ameerega hahneli,K= Hypsiboas cinerascens,L= Hypsiboas lanciformis,M= Phyllomedusa vaillanti,N= Noblella myrmecoides,O= Ameerega trivittata,
P = Scinax ictericus,Q= Oreobates cruralis,R= Pristimantis carvalhoi,U= Dendropsophus minutus.
514 RUDOLF VON MAY ET AL.
Floodplain
Terra firme
Bamboo
Palm swamp
Stress: 0.14
Floodplain
Terra firme
Bamboo
Palm swamp
Stress: 0.14
Figure 4. Non-metric multidimensional scaling plot, four sites (CICRA,
CM1, CM2 and TRC), four forest types, wet season 2008. Species
abundance data were used for dissimilarity matrix and nMDS plot.
the total average dissimilarity between forest types.
The SIMPER results show that the average dissimilarity
between the floodplain and the terra firme was 73.2%.
The average dissimilarity between the floodplain and
bamboo was 75.1%, and for the floodplain and palm
swamp, 81.0%. The terra firme and bamboo were the
most similar habitats, as their average dissimilarity was
57.2%. In contrast, both sites were very different from the
palm swamp, as the average dissimilarity was 86.7% and
86.2%, respectively.
We found that community structure differs across
forest types, as both the presence/absence and abundance
matrices were effective at discriminating among forest
types (Figure 4; only the nMDS plot based on
the abundance matrix is shown). When considering
abundance data, we found a significant pattern of
turnover across forest types (ANOSIM, Global R =
0.524, P = 0.001; Figure 4). In contrast, there was no
significant effect of site on assemblage turnover (Global
R = 0.055, P = 0.262). Pairwise comparisons indicated
that the floodplain differs from the other three forest
types and that the palm swamp differs from both the
terra firme and bamboo (all comparisons P < 0.029),
but that the terra firme and bamboo do not differ from
each other (P = 0.629). To verify whether this pattern
is maintained in the absence of uncommon species,
we repeated the same analyses excluding 16 species
represented by singletons and doubletons (Appendix 1).
Again, we found a significant pattern of turnover across
forest types (ANOSIM, Global R = 0.513, P = 0.001), but
no effect of site (Global R = 0.030, P = 0.576).
In the Mantel tests, we only found a correlation between
assemblage similarities and geographic distance when
forest types and abundance data were included in the
analyses. First, when forest types were not considered in
the analysis, we found no correlation between similarity
and g eographic distance. The lack of correlation was
observed both with presence/absence data (Jaccard
similarity index; Mantel test, r =−0.078, P = 0.255)
and abundance data (Bray–Curtis dissimilarity; Mantel
test, r = 0.217, P = 0.068). When we conducted the
analysis separately for each forest type, but only included
presence/absence data, we found no correlation between
similarity and geographic distance (Jaccard similarity
index; Mantel tests and Pearson correlations, P > 0.05 for
each forest type). Only when we conducted the analysis
separately for each forest type and included species
abundance data, we found a significant correlation
between similarity and geographic distance in both
floodplain and terra firme (Bray–Curtis dissimilarity;
Figure 5a, b). We found no correlation in the bamboo
and the palm swamp (Figure 5c, d). However, the
trend observed in the bamboo suggests that it would be
premature to exclude the possibility that similarity and
geographic distance are correlated in this forest type. A
linear function best fitted the data in the floodplain (y =
9.85x + 48.0, R
2
= 0.78) and the terra firme (y = 23.7x +
18.8, R
2
= 0.97), where y = Bray–Curtis dissimilarity and
x = distance.
DISCUSSION
Our results support the prediction that amphibian species
richness, composition and abundance differ across forest
types in the heterogeneous landscape of south-eastern
Peru. Previous researchers have also shown that forest
heterogeneity is important for maintaining amphibian
diversity (Ernst & R
¨
odel 2006, 2008; Gardner et al.
2007a), but they often focused on different habitat
‘states’ such as primary and secondary forest. Here, we
focused on differences among naturally occurring forest
types in a region where patterns of amphibian diversity
have not been studied in detail. Our results corroborate
some general patterns (e.g. high species diversity in the
floodplain; Crump 1971, Rodr
´
ıguez 1992) and improve
the knowledge of amphibian community structure across
other poorly studied habitats, especially bamboo and palm
swamp.
We found that a large proportion of amphibian
gamma diversity in south-eastern Peru is attributed to
habitat-related beta diversity. Although the numerically
dominant species may vary across habitats or sites, our
results indicate that forest type is more important than
site location in predicting both species composition and
abundance. The observation that bamboo and terra firme
assemblages do not clearly differ from each other (except
for a few species; Appendix 1) is not surprising as bamboo
habitats are physically nested within a larger land area
covered by terra firme. This pattern is consistent with
findings by Silman et al. (2003), who demonstrated that
Beta diversity of Amazonian amphibians 515
0
10
20
30
40
50
60
70
80
90
100
0.0 0.5 1.0 1.5 2.0 2.5
Log(distance+1)
Bray-Curtis dissimilarity
0
10
20
30
40
50
60
70
80
90
100
0.0 0.5 1.0 1.5 2.0 2.5
Log(distance+1)
Bray-Curtis dissimilarity
0
10
20
30
40
50
60
70
80
90
100
0.0 0.5 1.0 1.5 2.0 2.5
Log(distance+1)
Bray-Curtis dissimilarity
0
10
20
30
40
50
60
70
80
90
100
0.0 0.5 1.0 1.5 2.0 2.5
Log(distance+1)
Bray-Curtis dissimilarity
(a) Floodplain
Mantel test
r = 0.95, P = 0.037
Pearson correlation
r = 0.88, P = 0.020
(c) Bamboo
Mantel test
r = 0.92, P = 0.089
Pearson correlation
r = 0.80, P = 0.060
(b) Terra firme
(d) Palm swamp
Mantel test
r = 0.96, P = 0.038
Pearson correlation
r = 0.98, P = 0.001
Mantel test
r = 0.77, P = 0.172
Pearson correlation
r = 0.68, P = 0.135
0
10
20
30
40
50
60
70
80
90
100
0.0 0.5 1.0 1.5 2.0 2.5
Log(distance+1)
Bray-Curtis dissimilarity
0
10
20
30
40
50
60
70
80
90
100
0.0 0.5 1.0 1.5 2.0 2.5
Log(distance+1)
Bray-Curtis dissimilarity
0
10
20
30
40
50
60
70
80
90
100
0.0 0.5 1.0 1.5 2.0 2.5
Log(distance+1)
Bray-Curtis dissimilarity
0
10
20
30
40
50
60
70
80
90
100
0.0 0.5 1.0 1.5 2.0 2.5
Log(distance+1)
Bray-Curtis dissimilarity
0
10
20
30
40
50
60
70
80
90
100
0.0 0.5 1.0 1.5 2.0 2.5
Log(distance+1)
Bray-Curtis dissimilarity
0
10
20
30
40
50
60
70
80
90
100
0.0 0.5 1.0 1.5 2.0 2.5
Log(distance+1)
Bray-Curtis dissimilarity
0
10
20
30
40
50
60
70
80
90
100
0.0 0.5 1.0 1.5 2.0 2.5
Log(distance+1)
Bray-Curtis dissimilarity
0
10
20
30
40
50
60
70
80
90
100
0.0 0.5 1.0 1.5 2.0 2.5
Log(distance+1)
Bray-Curtis dissimilarity
(a) Floodplain
Mantel test
r = 0.95, P = 0.037
Pearson correlation
r = 0.88, P = 0.020
(c) Bamboo
Mantel test
r = 0.92, P = 0.089
Pearson correlation
r = 0.80, P = 0.060
(b) Terra firme
(d) Palm swamp
Mantel test
r = 0.96, P = 0.038
Pearson correlation
r = 0.98, P = 0.001
Mantel test
r = 0.77, P = 0.172
Pearson correlation
r = 0.68, P = 0.135
Figure 5. Relationship between Bray–Curtis dissimilarity and geographic distance, for all possible pairwise comparisons, analysed separately for each
forest type.
plant species composition in bamboo and terra firme in
south-eastern Peru are similar to each other. However,
more research is needed to make better generalizations
about how the bamboo forest differs from terra firme in
terms of animal communities.
The patterns of community structure across forest
types that we have observed in south-western Amazonia
resemble those observed in other tropical regions (Ernst
&R
¨
odel 2005, 2008; Gardner et al. 2007b, Watling
2005). For example, East A frican amphibian assemblages
exhibit a significant association with forest types on a
similar geographic scale (Gardner et al. 2007b). Although
the number of species recorded in East Africa is much
lower than in Amazonia, the patterns observed in
both regions suggest that species-sorting across forest
types (sensu Leibold et al. 2004) is important. Our
results, and results from the studies cited above, suggest
that habitat heterogeneity is important for maintaining
phylogenetically distant amphibian faunas.
Perhaps even more remarkable is the similarity
of patterns exhibited by the amphibian assemblages
in south-eastern Peru with those reported for tree
assemblages in the same region (Pitman et al. 1999).
Pitman et al. (1999) showed that, for most tree species,
habitat preferences are driven by abundance distributions
instead of restricted affinity to a given habitat: ‘habitat
preferences of Amazonian plants are a matter of degree,
and not as strict as suggested by earlier researchers’
(Pitman et al. 1999: p. 2657). Likewise, most amphibian
species we encountered occur in more than one forest
type but usually exhibit high abundance in only one
forest type. Hence, the results of indicator species analysis
should be taken with caution because observing a
particular species does not tell observers which forest
type they are in. The indicator species analysis can be
useful to characterize particular forest types, but it does
not imply that those taxa are specialists to those forest
types. The only exception might be Ranitomeya biolat,a
poison frog that is strongly associated with the bamboo
because it is the only anuran in the region that uses
bamboo internodes as a reproduction and retreat site (von
May et al. 2009b) and does not successfully breed in other
forest types (R. von May, pers. obs.).
Wefound that, whenanalysesare conducted separately
for each forest type and include species abundance data,
similarity between assemblages decreases with increasing
geographic distance. Our results stand in contrast to
findings by Dahl et al. (2009), who did not find a
correlation between similarity and distance in south-
western Amazonia despite the fact that their sites were
separated by up to 400 km. However, Dahl et al. (2009) did
not standardize their sampling with respect to forest type
and did not use abundance data (J. Moravec, pers. comm.).
Likewise, we found no correlation between similarity
516 RUDOLF VON MAY ET AL.
and distance when forest type and abundance data were
not included in the analysis. Thus, our results illustrate
that abundance data and forest type should always
be included in the analysis of amphibian community
structure at relatively small regional scales (as small as
100 km in our study). Researchers working in other
tropical and subtropical regions (and who included both
abundance data and habitat characteristics in their
analyses)also foundthat geographicdistance isimportant
for structuring amphibian assemblages (Keller et al. 2009,
Parris 2004).
The four forest types we studied are relatively
discrete in western Amazonia and are, in part, defined
by vegetation and different environmental conditions
including soil type and flooding regime. Treating each
forest type as a separate unit has many advantages
and allows researchers to understand which major
landscape features influence the structuring of taxonomic
assemblages. An alternative method is to analyse the
variation of amphibian communities across fine-scaled
environmental gradients (e.g. soil type, humidity), with
the aim of identifying which habitat characteristics are
most relevant for community structure. Preliminary
work on this topic has shown that amphibian species
may respond individualistically to some substrate
characteristics (e.g. soil pH, leaf-litter mass; Menin et al.
2007, Van Sluys et al. 2007, R. von May unpubl. data).
More research is needed to link environmental gradients
with animal diversity patterns in western Amazonia.
In conclusion, evaluating community structure across
forest types can improve our understanding of diversity
patterns in Amazonian landscapes. At the same time,
this type of information can aid in conservation. If
different areas are set aside as corridors or small
preserves, the inclusion of each forest type will maximize
the amount of protected biodiversity in the region.
Finally, given that many threatened amphibian species
in Peru might be found outside protected areas, more
information on species’ habitat requirements is needed
to develop strategies for habitat conservation and reserve
design.
ACKNOWLEDGEMENTS
We thank Kelsey Reider, Lisseth Flores, Valeriano Quispe,
Jerry Mart
´
ınez, Ra
´
ul Thupa, Hern
´
an Collado, Jorge P
´
erez,
Mario Napravnik, Kurt Holle, Jes
´
us Ramos and the staff
at CICRA, CM1, CM2 and TRC for help in field work and
logistics. We thank Jes
´
us C
´
ordova and C
´
esar Aguilar for
providing access to the herpetological collection in the
Museo de Historia Natural Universidad de San Marcos.
We thank Nigel Pitman, James Watling, Paul Fine,
Alessandro Catenazzi, Evelyn Gaiser, Steve Oberbauer,
Kyle Summers, Zhenmin Chen, Vivian Maccachero,
Monica Isola, Justin Nowakowski, Robert Hegna, Kelsey
Reider, Seiichi Murasaki, Steven Whitfield, Tiffany Doan
and two anonymous reviewers for providing constructive
comments on the manuscript. We also thank Evelyn
Gaiser, Tom Philippi and Zhenmin Chen for providing
statistical advice. Collection of data and voucher
specimens was authorized by an IACUC permit (Number
05-013) issued by Florida International University
(FIU) and collection and export permits issued by the
Instituto Nacional de Recursos Naturales (INRENA), Peru
(permit numbers 11-2008-INRENA-IFFS-DCB and 09
C/C-2008-INRENA-IANP). We thank Karina Ram
´
ırez
and Carmen Jaimes for advice with permit applications.
Funding for this study was provided by the Amazon
Conservation Association, Wildlife Conservation Society,
Tinker Foundation, Graduate Student Association and
Latin American and Caribbean Center at FIU. RvM thanks
FIU’s University Graduate School for a Doctoral Year
Fellowship. This paper is contribution number 181 to
FIU’s programme in tropical biology.
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14–29.
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Appendix 1. Species of amphibian and number of individuals recorded in each forest type. Data from the four study sites were pooled (N = 20
transects per habitat per site). The asterisk(s) next to the number of individuals denotes that the species could be used as an indicator for that
habitat (indicator species analysis: two asterisks, P < 0.05; one asterisk, 0.05 < P < 0.10). Diversity measures (mean ± SE) are included at the
bottom of table.
Species Floodplain Terra firme Bamboo Palm swamp All sites
Aromobatidae
Allobates conspicuus 082010
Allobates femoralis 30003
Allobates trilineatus 11 0 0 0 11
Bufonidae
Dendrophryniscus minutus 5 1 0 169
∗
175
Rhinella margaritifera 38 39 16 77 170
Rhinella marina 01203
Ceratophrynidae
Ceratophrys cornuta 31015
Dendrobatidae
Ameerega hahneli 18 7 1 33 59
Ameerega trivittata 5812227
Ranitomeya biolat 006
∗∗
06
Hemiphractidae
Hemiphractus scutatus 01001
Hylidae
Dendropsophus koechlini 20002
Dendropsophus leali 40004
Dendropsophus minutus 0113317
Dendropsophus parviceps 20002
Dendropsophus rhodopeplus 61007
Dendropsophus schubarti 30014
Hypsiboas boans 10001
Hypsiboas cinerascens 10 3 0 33 46
Hypsiboas fasciatus 13
∗
40 118
Hypsiboas geographicus 00134
Hypsiboas lanciformis 21727
∗
37
Osteocephalus buckleyi 00033
Osteocephalus cf. pearsoni 10102
Osteocephalus leprieurii 12205
Osteocephalus sp. 1 4 0 0 5
Osteocephalus taurinus 01359
Phyllomedusa camba 01001
Phyllomedusa palliata 21014
Beta diversity of Amazonian amphibians 519
Appendix 1. Continued.
Species Floodplain Terra firme Bamboo Palm swamp All sites
Phyllomedusa vaillanti 2904134
Scarthyla goinorum 00033
Scinax garbei 1300114
Scinax ictericus 1166427
Scinax pedromedinae 51006
Scinax ruber 4310
∗
118
Trachycephalus coryaceus 10001
Trachycephalus venulosus 00101
Leiuperidae
Engystomops freibergi 111
∗
10 15 0 136
Edalorhina perezi 20002
Leptodactylidae
Leptodactylus (Adenomera) sp. 33 55 78 25 191
Leptodactylus didymus 20305
Leptodactylus knudseni 10001
Leptodactylus lineatus 02002
Leptodactylus pentadactylus 20002
Leptodactylus petersii 397579
∗∗
130
Leptodactylus rhodomystax 11002
Leptodactylus rhodonotus 10001
Microhylidae
Chiasmocleis bassleri 20002
Chiasmocleis ventrimaculata 69
∗∗
610 287
Ctenophryne geayi 11
∗
30014
Elachistocleis bicolor 20002
Hamptophryne boliviana 87
∗∗
33 3 1 124
Syncope antenori 439016
Plethodontidae
Bolitoglossa altamazonica 109
∗∗
313
Strabomantidae
Noblella myrmecoides 978529
Oreobates cruralis 1292124
Pristimantis altamazonicus 10157
Pristimantis buccinator 460111
Pristimantis carvalhoi 2102023
Pristimantis divnae 03104
Pristimantis fenestratus 30003
Pristimantis ockendeni 03407
Pristimantis reichlei 23 118
∗∗
89 4 234
Pristimantis skydmainos 1200012
Pristimantis toftae 89
∗∗
13 33 3 138
Number of species 51 37 32 30 65
Number of individuals 736 374 359 498 1967
Diversity, Shannon (H
) 2.60 ± 0.14 2.05 ± 0.13 2.09 ± 0.11 1.70 ± 0.20
Diversity, Simpson (1 – λ
) 0.89 ± 0.02 0.80 ± 0.04 0.80 ± 0.01 0.70 ± 0.07
Evenness, Pielou (J
) 0.81 ± 0.04 0.73 ± 0.03 0.74 ± 0.20 0.65 ± 0.08
