Biodivers Conserv
DOI 10.1007/s10531-013-0553-x
ORIGINAL PAPER
Disturbances, elevation, topography and spatial
proximity drive vegetation patterns along an altitudinal
gradient of a top biodiversity hotspot
Pedro V. Eisenlohr • Luciana F. Alves • Luı́s Carlos Bernacci •
Maı́ra C. G. Padgurschi • Roseli B. Torres • Eduardo M. B. Prata •
Flavio Antonio M. dos Santos • Marco Antônio Assis • Eliana Ramos •
André Luı́s C. Rochelle • Fernando R. Martins • Mariana C. R. Campos •
Fernando Pedroni • Maryland Sanchez • Larissa S. Pereira •
Simone A. Vieira • José Ataliba M. A. Gomes • Jorge Y. Tamashiro •
Marcos A. S. Scaranello • Cora J. Caron • Carlos Alfredo Joly
Received: 8 May 2013 / Accepted: 21 August 2013
Ó Springer Science+Business Media Dordrecht 2013
Abstract The correlation between vegetation patterns (species distribution and richness)
and altitudinal variation has been widely reported for tropical forests, thereby providing
theoretical basis for biodiversity conservation. However, this relationship may have been
oversimplified, as many other factors may influence vegetation patterns, such as distur-
bances, topography and geographic distance. Considering these other factors, our primary
question was: is there a vegetation pattern associated with substantial altitudinal variation
(10–1,093 m a.s.l.) in the Atlantic Rainforest—a top hotspot for biodiversity conserva-
tion—and, if so, what are the main factors driving this pattern? We addressed this question
by sampling 11 1-ha plots, applying multivariate methods, correlations and variance par-
titioning. The Restinga (forest on sandbanks along the coastal plains of Brazil) and a
lowland area that was selectively logged 40 years ago were floristically isolated from the
other plots. The maximum species richness ([200 spp. per hectare) occurred at
P. V. Eisenlohr L. F. Alves M. C. G. Padgurschi F. A. M. dos Santos
A. L. C. Rochelle F. R. Martins L. S. Pereira J. Y. Tamashiro
M. A. S. Scaranello C. J. Caron C. A. Joly
Departamento de Biologia Vegetal, Instituto de Biologia, CP 6109, Universidade Estadual de
Campinas (UNICAMP), Campinas, SP 13083-970, Brazil
Present Address:
P. V. Eisenlohr (&)
Departamento de Botânica, Universidade Federal de Minas Gerais,
Belo Horizonte, MG 31270-901, Brazil
e-mail: pedrov.eisenlohr@gmail.com
L. F. Alves L. C. Bernacci R. B. Torres J. A. M. A. Gomes
Centro de Pesquisa e Desenvolvimento de Recursos Genéticos Vegetais, Instituto Agronômico de
Campinas (IAC) CP 28, Campinas, SP 13012-970, Brazil
E. M. B. Prata
Programa de Pós-Graduação em Botânica, CP 478, Instituto Nacional de Pesquisas da Amazônia
(INPA), Manaus, AM 69060-001, Brazil
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approximately 350 m a.s.l. (submontane forest). Gaps, multiple stemmed trees, average
elevation and the standard deviation of the slope significantly affected the vegetation
pattern. Spatial proximity also influenced the vegetation pattern as a structuring environ-
mental variable or via dispersal constraints. Our results clarify, for the first time, the key
variables that drive species distribution and richness across a large altitudinal range within
the Atlantic Rainforest.
Keywords Atlantic Rainforest Conservation hotspot Multivariate
analysis Species distribution Species richness
Introduction
The investigation of vegetation patterns has been recognized as a crucial step in providing
theoretical basis for biodiversity conservation (e.g., Ivanauskas et al. 2006; Marques et al.
2011). One of the most widely reported type of vegetation patterns for tropical forests is
the variation in species distribution and richness along elevational gradients (Austin and
Greig-Smith 1968; Beals 1969; Gentry 1988; Rodrigues and Shepherd 1992; Auerbach and
Shmida 1993; Rahbek 1995; Lieberman et al. 1996; Vázquez and Givnish 1998; Oliveira-
Filho and Fontes 2000; Zhao et al. 2005; Sanchez et al. 2013). However, it is unlikely that
studies on this subject have found a realistic model because species distributions and
richness can be influenced by other factors, such as the geographic distance between
locations (e.g., Hubbell 2001; Diniz-Filho et al. 2012), disturbances of human or natural
origin (e.g., Connell 1978; Vázquez and Givnish 1998; Laurance 2001; Pereira et al. 2007)
and topography (e.g., Bourgeron 1983; O’Brien et al. 2000; Bohlman et al. 2008). When
investigating ecological patterns, controlling or accounting for a set of factors that are
potentially important for determining these patterns is essential to obtaining reliable results
(Simberloff 1983; Rey Benayas and Scheiner 2002).
The Brazilian Atlantic Rainforest, a top hotspot for biological conservation (Myers et al.
2000; Laurance 2009; Ribeiro et al. 2011), lacks realistic, comprehensive models for
understanding local floristic patterns. Despite this problem, several studies have addressed
important questions concerning vegetation patterns along elevation gradients. Oliveira-
Filho and Fontes (2000) reported that altitude is one of the principal factors influencing
floristic differentiation in the Atlantic Forest sensu lato of southeastern Brazil. In an
M. A. Assis E. Ramos
Departamento de Botânica, Instituto de Biociências de Rio Claro, CP 199, Universidade Estadual
Paulista Júlio de Mesquita Filho, Rio Claro, SP 13506-900, Brazil
M. C. R. Campos
School of Plant Biology, University of Western Australia, M089 35 Stirling Highway,
Crawley, WA 6009, Australia
F. Pedroni M. Sanchez
Departamento de Ciências Biológicas e da Saúde, Universidade Federal de Mato Grosso, Pontal do
Araguaia, MT 78698-000, Brazil
S. A. Vieira
Núcleo de Estudos e Pesquisas Ambientais (NEPAM), Universidade Estadual de Campinas
(UNICAMP), Campinas, SP 13083-867, Brazil
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analysis of the floristic similarity between rainforest and semi-deciduous forest sites in São
Paulo State, Ivanauskas et al. (2000) detected the establishment of certain groups relative
to the elevation. Scudeller et al. (2001) found that the distinctions between the provincial
and coastal Atlantic plateaus are linked to altitude, precipitation and temperature. Sanchez
et al. (2013) found that the Serra do Mar rainforests along the seashore, mountain slopes
and highlands differed dramatically in their floras, physiognomies and origins. At the local
scale, Rodrigues and Shepherd (1992) and Moreno et al. (2003) also found consistent
floristic links relative to altitude. Thus, well-supported relationships between altitude and
floristic variations have been reported to the Atlantic Forest.
Our objective was to enhance the existing theoretical support for conservation initiatives
for protecting lands in moist tropical forests, considering the existing need for more
comprehensive approaches that include a set of potentially relevant predictors for vege-
tation patterns along altitudinal gradients. Strong particularities and novelties of this study
are the thorough documentation of: (1) a broad and standardised sampling effort (11 ha of
vegetation in equally sampled plots); (2) sampling conducted along a single conservation
unit (Serra do Mar State Park) while minimising environmental factors that might interfere
with the investigation; (3) the measurement of variable sets for the few factors that might
interfere with the investigation, such as the topography and disturbance history, allowing
for a consistent analysis of the vegetation patterns throughout the elevational range.
We addressed the following questions: (1) is there a pattern in the floristic composition
associated with the altitudinal variation that we analysed (10–1,093 m) for trees in the
Atlantic Rainforest; i.e., do areas having a similar altitude exhibit higher species similarity
than those areas that are more distant with respect to altitude? (2) Does the species richness
differ from areas of lower altitude to areas of higher altitude? (3) What are the principal
factors driving the variation in tree species composition and richness?
Methods
Study area
We studied the Atlantic Rainforest in the Picinguaba and Santa Virginia Research Station of
the Serra do Mar State Park, near Ubatuba and São Luis do Paraitinga municipalities in São
Paulo State, southeastern (SE) Brazil (Fig. 1), which covers an elevation range of 1,083 m
(10–1,093 m a.s.l.). The coordinates and other data on the areas studied are presented in
Table 1; additional data can be found in Alves et al. (2010) and Joly et al. (2012). The climate
is tropical (Af according to the Köeppen system), with higher rainfall in summer (Morellato
et al. 2000) and without significant rainfall variations along the slope. The topography of the
region includes the coastal plain, isolated hills and mountains of the elongated ‘‘Morraria’’
Coast and the inner slopes of the ‘‘Serrania’’ Coast (Ponçano et al. 1981).
The soils are generally low in nutrients, with low fertility and high levels of aluminium.
In area ‘‘A’’ (Restinga forest), the soil type is classified as Quartzipsamment, whereas in
the other areas the soils are Inceptisols (Table 1). Shallow and well-drained soils pre-
dominate along the slope, with the exception of the Restinga forest. Soils become more
aged with elevation, according to ‘‘Kr’’ and ‘‘Ki’’ weathering indices—at the top, there is
predominance of oxides (high levels of Al2O3). Soil carbon and nitrogen contents increase
significantly with altitude. On the other hand, pH does not strongly vary along the ele-
vational gradient (in general \4). The litter production has been higher at lower elevations
(8–10 Mg ha-1 at plots lower than 100 m a.s.l. and 7 Mg ha-1 above 400 m a.s.l.). Those
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Fig. 1 Locations of the 11 study areas along an altitudinal gradient in the Atlantic Rainforest (SE Brazil).
The codes for the localities are presented in Table 1
data were provided by SC Martins (unpublished data), Sousa Neto et al. (2011), Vieira
et al. (2011) and Sanchez et al. (2013).
The codes for the localities where the sampling was completed are listed in Table 1 in
alphabetical order according to their positions in the landscape. These codes follow those
of the ‘‘BIOTA Functional Gradient’’ Project (Joly et al. 2012). It should be noted that the
altitudinal belts (Table 1) do not follow the Brazilian classification system for vegetation
(IBGE 2012), but rather used an alternative classification based on the field observations of
the research team. The relative locations of the 11 1-ha areas are shown in Fig. 1. Area
‘‘A’’ (Restinga or Sandy Forest, at approximately sea level) is located where the major
local rivers flow into the region. In this area, the soil is exposed to the seasonal water table
(César and Monteiro 1995). Areas ‘‘B’’, ‘‘D’’, ‘‘E’’, ‘‘F’’, ‘‘G’’, ‘‘H’’, ‘‘I’’ and ‘‘J’’ are
located at increasing altitudes along the slope of the Serra do Mar, whereas areas ‘‘K’’ and
‘‘N’’ are located at the summit (Table 1). According to long-time residents, area ‘‘F’’ was
selectively logged *40 years ago for the tree Hieronyma alchorneoides (Phyllanthaceae).
A more detailed description of the study area was provided by Joly et al. (2012).
Data collection and preparation of matrices
We measured all individual trees with a diameter equal to or greater than 4.8 cm at 1.30 m
above the ground (DBH) in each 1-ha area (100 m9 100 m, a widely used sampling
design in tropical forests, thus allowing comparisons with other studies) (Table 1). The
sampling protocol was described in detail by Alves et al. (2010) and Joly et al. (2012).
To investigate the patterns of species distribution and variation in species richness, we
first constructed three matrices, each of which included the 11 study areas. The first matrix
contained data on the numbers of individuals of each species. The second matrix was
composed of the following environmental variables (Table 1): topographic variables—
aspect, slope and standard deviation of the slope (SD slope is reported in the tables and
figures), 3D surface area (SurfArea), average elevation (Elevation) and elevational range
(ElevRange); disturbance variables—gaps.area-1 (Gaps), basal area of standing dead stems
(BAdead) and number of trees with two or more stems (MultStem); and two synthetic
climatic variables—annual precipitation (AnnuPrec) and annual mean temperature
(AnnMTemp). The units used for each variable are provided in Table 1.
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Table 1 Codes and other environmental information for the 11 sampled areas along an altitudinal gradient in the Atlantic Rainforest, SE Brazil
Areas Forest type Geographic Average Elevational Aspect Slope Multiple Total Basal area Annual Annual mean
coordinates elevation range (%) ±SD stemmed gaps. of standing rainfall temperature (°C)
(lat, long) (m a.s.l.) (m a.s.l.) trees area-1 dead stems (mm)
(m2) (m2)
‘‘A’’ Restinga 238210 2100 S, 448510 0400 W 10 1 -0.956 1.10 ± 0.7 117 2,662 0.40 2,406 22.60
‘‘B’’ Lowland Forest 238200 1400 S, 448500 0600 W 46 23 -1.000 14.7 ± 5.8 69 2,700 0.44 2,406 22.60
‘‘D’’ Lowland Forest 238200 0800 S, 448500 0000 W 57 26 -0.807 12.6 ± 5.5 65 3,526 0.89 2,324 22.25
‘‘E’’ Lowland Forest 238200 0400 S, 448490 5700 W 73 25 -0.876 11.1 ± 4.8 75 2,774 1.03 2,324 22.25
‘‘F’’ Lowland Forest 238220 5300 S, 458040 4600 W 105 28 -0.956 11.9 ± 6.8 184 4,770 1.12 2,582 21.90
‘‘G’’ Submontane Forest 238220 2600 S, 458040 5100 W 188 22 -0.987 12.2 ± 4.6 46 1,970 1.95 1,975 18.31
‘‘H’’ Submontane Forest 238220 2500 S, 458040 5500 W 208 16 -0.988 9.20 ± 3.5 56 3,245 1.73 1,975 18.31
‘‘I’’ Submontane Forest 238220 0100 S, 458050 0100 W 350 48 -0.852 27.3 ± 6.3 48 1,627 0.83 1,865 17.46
‘‘J’’ Submontane Forest 238210 5800 S, 458050 0300 W 375 46 -1.000 25.5 ± 8.0 31 1,749 2.11 1,865 17.46
‘‘K’’ Montane Forest 238190 3200 S, 458040 0500 W 1,066 47 0.850 25.5 ± 8.3 44 2,314 3.16 1,724 16.28
‘‘N’’ Montane Forest 238190 3600 S, 458040 3200 W 1,025 34 0.787 15.8 ± 5.4 64 3,860 3.14 1,724 16.28
Other data are reported by Alves et al. (2010) and Joly et al. (2012). Altitudinal belts (see :‘‘Forest type’’ column) do not follow the Brazilian classification system for
vegetation (IBGE 2012), but rather used an alternative classification based on the field observations of the BIOTA research team
SD standard deviation
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To acquire data on the topographic variables, we first used the elevation information
from each 10 9 10 m corner point determined in the field using high-precision equipment
(a total station) to create a digital elevation model (DEM) at a 1-ha scale. The DEM was
created using the ‘‘topo-to-raster’’ tool, an interpolation method implemented in ArcGIS to
generate DEMs (Hutchinson 1989). This procedure allowed us to store topographic
information in a raster format with a high spatial resolution. The new elevation data from
the DEM was fitted to probability density functions to extract the expected value (e.g., the
mean for a Gaussian distribution) and deviation for each plot. The 3D surface variable was
also estimated from the DEM. We estimated gaps.area-1 as the surface area directly under
a canopy opening, extending to the base of the forest edge, according to the extended gap
definition of Runkle (1982). Gaps are relatively small-sized in our Atlantic forest site. The
mean gap area varies between 60.4 and 115.2 m2, and the maximum total gap area per plot
was 2,598.9 m2, corresponding to less than 30 % of the plot area. The climatic variables
were extracted from WORLDCLIM (Hijmans et al. 2005). Although some authors do not
recommend using WORLDCLIM for climatic interpolation in mountainous areas (Sklenář
et al. 2008), to our knowledge, there is no disadvantage in the Atlantic Forests.
Finally, we constructed a coordinate matrix, containing the latitude and longitude of
each area, for the purpose of assessing and controlling spatial autocorrelation and per-
forming variance partitioning (below).
Variations in floristic patterns and correlations with predictor variables
To ordinate the 11 areas without interference from any environmental variable, we per-
formed a DCA (Detrended Correspondence Analysis) using PC-ORD 6.0 (McCune and
Mefford 2011). We discarded the NMS (Non-Metric Multidimensional Scaling), a widely
recommended method (McCune and Grace 2002), following the same reasons that Santos
et al. (2012). We removed the 243 singletons because the Chi squared distance used in
DCA is sensitive to rare species (McCune and Grace 2002). The DCA was performed
using the options ‘‘down-weight rare species’’ and ‘‘rescale axes’’.
We used OLS multiple linear regressions (Quinn and Keough 2002) to analyse the
predictability of the environmental variables on the floristic variation summarised by the
two principal axes of the DCA. We initially discarded variables with very weak correla-
tions (r \ 0.3) with the DCA axis and removed colinearities, keeping only the most
important variable ([r with the respective DCA axis) in each group of collinear variables.
After this procedure, no variables remaining with a variance inflation factor [10 (Quinn
and Keough 2002), and we then chose the best model (lowest AICc—Corrected Akaike
Information Criterion; Burnham and Anderson 2002). However, we detected a strong
outlier in the linear trend of the OLS models, the Restinga plot (area ‘‘A’’). We then opted
to present both DCAs (DCA1, with Restinga, and DCA2, without Restinga), illustrating the
overall floristic patterns, while discarding the linear model that included Restinga. Further
information on the OLS procedure can be found in the Common procedures for all OLS
models section below.
Variations in species richness and correlations with predictor variables
To avoid a dependent relationship between the numbers of individuals and species, we
estimated the species richness by running an individual-based rarefaction analysis (Gotelli
and Colwell 2001) using ECOSIM, version 7.71 (Gotelli and Entsminger 2004). We
compared the estimated species richness among sites using the same sampling effort
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(numbers of individuals) with 95 % confidence intervals. This level of sampling corre-
sponds to the number of individuals in area ‘‘B’’ (1,082 trees) because this site had the
lowest value among the studied areas. The obtained species richness was hereafter treated
as the estimated species richness.
Finally, we performed a multiple linear regression analysis (OLS) to investigate whether
changes in species richness could be explained by our environmental predictors. The
colinearities were removed, as described above, with one exception: although altitude was
less correlated with species richness than was basal area of standing dead stems, the effect
of removing the multicolinearity was more powerful when we discarded the last variable;
therefore, we decided on the altitude. We used the same methods described in the floristic
variation section above to select the best model (see also the Common procedures for all
OLS models section below).
Common procedures for all OLS models
We followed the general format of the procedures proposed by Eisenlohr (2013) for the
aforementioned OLS multiple linear regression models and heeded the caveats identified
by this same author. We used correlograms to apply Moran’s I coefficient as an indicator of
spatial autocorrelation (SAC) using SAM 4.0 (Rangel et al. 2010) to check for possible
violations of the assumption of spatial independence in the residuals of both the full and
selected OLS models (Diniz-Filho et al. 2003; Diniz-Filho et al. 2008). The number and
size of distance classes used the defaults for SAM. The significance of SAC was detected
by sequential Bonferroni criteria (Fortin and Dale 2005). When significant SAC in the
residuals of the OLS models was detected, we used spatial filters (Moran’s Eigenvector
Maps—MEMs; Dray et al. 2006), obtained using the ‘‘spacemakeR’’ package in the R
environment (The R Foundation for Statistical Computing 2012), as additional predictors.
We selected MEMs until we found randomness (non-significant SAC) in the residuals.
When we found SAC in the full model (i.e. prior to AICc selection), we used MEMs as
fixed variables in the selection (Diniz-Filho et al. 2008).
We confirmed the assumptions of homoscedasticity and linearity by creating graphical
displays of the residuals analysis (X: predicted values; Y: residuals), demonstrating an
even distribution of the points above and below the zero-residual horizontal line (Quinn
and Keough 2002). The normality of the residuals was confirmed by using the Shapiro–
Wilk test.
The significance of the linear model was assessed using the F test. The relative influence
of each environmental variable on the floristic data was assessed based on partial
regression coefficients and t tests. In all of the tests, we used 5 % as the significance level.
Variance partitioning
We also performed variance partitioning using a partial regression analysis to obtain the
components that explain the variation in each response variable (ordination axes and
species richness) (Legendre and Legendre 2012). The environmental variables were the
same as in the respective OLS models, and the spatial variables (MEMs) were forward-
selected using the ‘‘packfor’’ package for the R environment. The forward procedure is
justified by our requirement to obtain only MEMs that provide significant contributions to
the response variables (Bellier et al. 2007). Note that the MEMs selected for variance
partitioning were not necessarily the same MEMs chosen for the removal of spatial
autocorrelation from the OLS residuals because the objectives were distinctly different.
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Axis 1 of the DCA2 did not have any significant spatial variables, and thus, we did not
perform a variance partitioning in this case.
Results
Considering the overall sampling from 11 ha of the Atlantic Rainforest in southeastern
Brazil, we registered a total of 16,796 trees, 563 species, 195 genera and 68 families
(including ‘singletons’). A summary of the vegetation data from each area is presented in
Table 2, and other details are provided by Joly et al. (2012).
Variations in floristic patterns and correlations with predictor variables
The first axis of DCA1 showed contrast between areas ‘‘A’’ (Restinga), ‘‘K’’?‘‘N’’
(montane forests) and the other areas (Fig. 2a). Topography (slope and SD slope) and
elevational range were the most highly correlated with this axis (Table 3). Considering the
strong differences between soils of Restinga and other areas, we might infer that the
edaphic component was also important in such floristic differentiation. For the second axis,
areas ‘‘F’’ (lowland forest, selectively logged) and ‘‘I’’?‘‘J’’ (submontane forest) were
dissociated from each other and from other areas (Fig. 2a). The disturbance variables
(mainly multiple stems) appeared to be the most relevant in this case (Table 3).
The first axis of DCA2 showed dissociation between areas ‘‘F’’, ‘‘K’’?‘‘N’’ and the
other areas (Fig. 2b). Disturbance (total gap area and basal area of standing dead stems)
and elevation variables once again assumed great importance (Fig. 2b; Table 3). The linear
model indicated gaps and average elevation as being significant variables (Table 4). The
second axis suggested a significant role for the basal area of standing dead stems and the
annual mean temperature (Fig. 2b); however, only the spatial filter (MEM) added to
account for the spatial autocorrelation was significant (Table 4). Thereafter, climatic
variables did not present a primary role in the floristic patterns. Since there is predomi-
nance of oxides at the top and soil carbon and nitrogen contents increase along the slope,
soil could also be important to explain the floristic dissociation between ‘‘K’’?‘‘N’’ and the
Table 2 Number of trees and
Site Number Species Genus Family
the species, genus and family
(area) of trees richness richness richness
richness for 11 sites sampled
along an altitudinal gradient in
the Atlantic Rainforest (SE ‘‘A’’ 1,671 85 61 32
Brazil) ‘‘B’’ 1,082 136 87 39
‘‘D’’ 1,286 156 94 42
‘‘E’’ 1,240 141 93 41
‘‘F’’ 1,381 106 72 38
‘‘G’’ 1,496 151 101 41
‘‘H’’ 1,528 152 98 41
‘‘I’’ 1,993 203 111 50
‘‘J’’ 1,832 211 117 49
‘‘K’’ 1,851 178 83 44
‘‘N’’ 1,436 148 73 39
All 16,796 563 195 68
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Fig. 2 DCA ordinations from species abundance data along an altitudinal gradient in the Atlantic
Rainforest (SE Brazil). Arrows indicate a posteriori Pearson correlations with environmental variables.
a Ordination with Restinga and b without Restinga. The abbreviations are defined in the Methods section.
Weak (r \ 0.3) and redundant variables were not represented
Table 3 DCA ordination results
DCA1 (with Restinga) DCA2 (without Restinga)
for species distribution patterns
along an altitudinal gradient in Axis 1 Axis 2 Axis 1 Axis 2
the Atlantic Rainforest (SE Bra-
zil) and a posteriori Pearson cor- Eigenvalue 0.610 0.4193 0.4200 0.1864
relations for environmental
variables Gradient length 3.419 3.111 2.539 1.513
Gaps 0.003 0.823 0.701 -0.113
MultStem 0.271 0.881 0.639 -0.052
BAdead -0.116 -0.007 0.485 0.492
Slope -0.450 -0.420 -0.102 0.026
SD slope -0.596 -0.024 0.308 -0.156
SurfArea -0.225 -0.458 -0.159 -0.039
Elevation 0.054 -0.064 0.502 0.269
ElevRang -0.476 -0.293 0.076 0.015
AnnMTemp 0.182 0.318 -0.131 -0.665
The abbreviations are defined in AnnuPrec 0.124 0.503 0.037 -0.524
the ‘‘Methods’’ section
other areas. Both models exhibited high coefficients of determination (Axis 1:
R2Adj = 0.7380; Axis 2: R2Adj = 0.7175).
The floristic gradients were at least moderate for all the axes (except for Axis 2 from
DCA2), which was evidenced by the eigenvalues and the gradient length (Table 3). These
results indicated an intermediate to high level of species turnover for the main trends of
floristic variation along this altitudinal variation.
Variations in species richness and correlations with predictor variables
Areas ‘‘I’’ and ‘‘J’’ (submontane forest) were the richest in species among the 11 areas
investigated (Fig. 3). The poorest locality in species richness was area ‘‘A’’ (Restinga
forest), followed by area ‘‘F’’ (lowland forest, selectively logged) (Fig. 3).
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Fig. 3 Comparison of species richness among the 11 areas along an altitudinal gradient in the Atlantic
Rainforest (SE Brazil) at the point of equal numbers of individuals (individual-based rarefaction algorithm).
Narrow bars indicate 95 % confidence intervals
Multiple stems negatively predicted the variation in estimated species richness and the
standard deviation of the slope positively predicted that variation (Table 4). This model
also demonstrates a high predictive power (R2Adj = 0.8362).
Variance partitioning
We detected the primary role of the spatially structured environmental factors (fraction [b])
on the floristic patterns emerged from Axis 2 of the DCA2 (Fig. 4a); 22 % of the species
distribution remained undetermined (fraction [d]; Fig. 4a). The variations in species
richness also indicated a high level of explanation for [b] and demonstrated a high level of
explanation for the sum of components [a], [b] and [c], whereas component [d] represented
only 3 % of the explanation (Fig. 4b). The isolated spatial fraction ([c]) was more relevant
than the isolated environmental fraction ([a]) in both variance partitioning calculations.
Discussion
The ordination analysis indicated several floristic patterns: (i) the strong dissociation of
Restinga (‘‘A’’) relative to the other areas, most likely due to topographic and edaphic dif-
ferences (the Restinga soil is classified as a Quartzipsamment and is subject to seasonal
exposure to the water table); (ii) the strong dissociation of the lowland area (‘‘F’’), which
appears to be influenced by past disturbance, reflected by an increase in the number of the
multiple stemmed trees and total gaps.area-1 variables; (iii) the equivalence of blocks
‘‘I’’?‘‘J’’, most likely due to topographic factors; and (iv) ‘‘K’’?‘‘N’’, which might be influ-
enced by gaps.area-1 and the elevation average. Explanations for these patterns are possible
when considering the geomorphological processes, disturbances and/or elevation zonation.
The geomorphological processes occurring in the Restinga forest area (Araújo and
Lacerda 1987; Assis et al. 2011) are markedly different from those in the other studied
areas, as described by Sanchez et al. (2013). The processes operating in Restinga lead to a
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Table 4 Summary of the linear regression models prepared for DCA2 and estimated species richness along
an altitudinal gradient in the Atlantic Rainforest (SE Brazil)
Beta Std. Err. of Beta B Std. Err. of B p level
Axis 1
Constant – – -141.85 48.16 0.02
Gaps 0.74 0.17 0.07 0.02 <0.01
Elevation 0.55 0.17 0.13 0.04 0.01
Axis 2
Constant – – 3.44 113.70 0.98
AnnMTemp 0.18 0.31 3.47 5.86 0.57
MEM 1 1.03 0.31 155.34 47.00 0.01
Species richness
Constant – – 132.712 13.705 <0.01
SD slope 0.499 0.14 6.96 1.84 <0.01
MultStem -0.68 0.14 -0.46 0.09 <0.01
The abbreviations are defined in the ‘‘Methods’’ section. Beta: standardised coefficients. B: unstandardised
coefficients. Significance p level (B0.05) in bold
Axis 1: R2Adj = 0.7380; p \ 0.01. Axis 2: R2Adj = 0.7175; p \ 0.01; Species richness: R2Adj = 0.8362;
p \ 0.01
Fig. 4 Variance partitioning among the components that explain the variations in a DCA2 Axis 2 scores
and b estimated species richness along an altitudinal gradient in the Atlantic Rainforest (SE Brazil). Axis 1
of the DCA2 did not have any significant spatial variables, and thus, we did not perform a variance
partitioning in this case
restrictive environment (Assis et al. 2011) and, consequently, to species selectivity due to
environmental filters (Ackerly 2003; Assis et al. 2011). Disturbances, in turn, can affect
water conditions and temperature (Zhao et al. 2005), which are principal factors affecting
the distribution of vegetation (Holdridge 1947; Gentry 1988). The logging of Hyeronima
alchorneoides in area ‘‘F’’ appears to have been largely responsible for the local loss of
certain species due to changes in the dynamics and succession (Villela et al. 2006), as the
logged species was a large tree reaching a large volume of wood. Human disturbances have
also appeared to influence species distributions in other studies (e.g., Vázquez and Givnish
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1998), and it has been highlighted as a factor influencing biodiversity in the Atlantic Forest
(Pereira et al. 2007; Tabarelli et al. 2010).
We also found evidence of the influence of gaps on floristic variation. The opening of
gaps influences the germination and growth of forest species (Paz and Martı́nez-Ramos
2003). Gap area seems to be a key variable influencing species composition, since it
determines the amount of light that reaches the area of the gap (Barton et al. 1989).
Therefore, different species colonise gaps of different sizes (Whitmore 1996).
The dissociation between ‘‘I’’?‘‘J’’ and the other areas appears to be related to topo-
graphic variables, as described below. Regarding the floristic differences between higher
(‘‘K’’?‘‘N’’) and lower elevation areas, regional studies performed in the Brazilian
Atlantic forest have indicated that altitude is important for detecting floristic affinities (e.g.,
Oliveira-Filho and Fontes 2000; Meireles et al. 2008; Sanchez et al. 2013). The rela-
tionship between vegetation patterns and altitude is well established (Rahbek 1995; Giv-
nish 1999; Körner 2007), and several different patterns have been observed (He and Chen
1997). In the same conservation unit used for this study, Sanchez et al. (2013) addressed
altitudinal limits for certain tree species, where most cases of restricted distribution (51 %)
occurred at higher elevations. Thus, our results confirm that segregation patterns are driven
by a set of factors related to altitude.
At each level of a slope, the vegetation is a result of a confluence of the constituent species,
climate, soils and disturbances (e.g., Zhao et al. 2005). The confluence of these factors, which
have been occurring for thousands of years, might result in elevation zonation, reflecting
differences in species diversity levels for different regions of the slope (Zhao et al. 2005).
However, two observations did not support the pattern of decreases in diversity with
increases in altitude: (i) there was no significant correlation between species richness and
altitude, and (ii) the species richness in higher altitude areas (‘‘K’’ and ‘‘N’’, [500 m, in
montane rainforest) decreased relative to ‘‘I’’ and ‘‘J’’. This result would suggest a ‘‘mid-
altitude bulge’’ pattern for this gradient (Lomolino 2001), thereby confirming the results
provided by Zhao et al. (2005), among others. However, because we did not sample the
vegetation along the complete altitudinal range due to logistic limitations (Joly et al. 2012),
this potential pattern could not be confirmed. Further research on this question is needed.
It has been proposed that depending on the substrate, species diversity might display an
increase or decrease along an elevation gradient (Wilson et al. 1990). Throughout the area
investigated in this study, the soil carbon, nitrogen and total alkalinity levels were highest
in ‘‘I’’ and ‘‘J’’ (the richest species areas), which can be explained by topographic factors
influencing the microclimate (SC Martins, unpublished data). The slope and surface area
could help us to understand the floristic segregation of areas ‘‘I’’ and ‘‘J’’, as we found a
significant interference from variations in slope (SD Slope) on species richness. Accord-
ingly, topography was important for some of the trends of variation in species distribution,
in agreement with Guerra et al. (2013), for example, and topography mainly affected
species richness, as reported by Wolf et al. (2012). Wolf et al. (2012) also noted that higher
species richness is expected where local-scale variations in topography result in higher soil
water availability. This result is expected because higher species richness is usually found
in forests with low seasonal variation in climate (Gentry 1988; Pyke et al. 2001).
The number of multi-stemmed trees per hectare exhibited a significant negative cor-
relation with species richness. Probably many adult trees were dying as a consequence of
slope, thus opening new gaps in the forest. These gaps allowed the increased branching of
the remaining trees, resulting in a greater number of multiple stems; thus, multiple stems
would be associated with gap opening. This phenomenon is supported by the fact that the
death of many adults could result in a decline in species richness (personal observations).
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For example, a peak in multiple stems and gaps.area-1 was observed at one of the areas
(‘‘F’’) having the lowest species richness among all studied sites. Thus, we propose that
despite the expected high species richness in areas with wider gaps (e.g., Terborgh 1992),
in the investigated forests, a more complex pattern has occurred, perhaps due to the
significant effects of logging in area ‘‘F’’.
The summit might experience more fog, with associated decline in species richness, and
increase in floristic differentiation (e.g., Bertoncello et al. 2011). However, our field
observations indicate that the presence of low-level cloud cover and fog formation is not
frequent in the Montane Forest plots. Despite a clear decline in air and soil temperatures
(Vieira et al. 2011), probably such climate condition is not a main driver on vegetation
patterns (Alves et al. 2010). We believe that our Montane forest site is located in a
transitional area exposed to varying degrees of cloud impaction, thus it might be consid-
ered as a lower montane forest under some influence of clouds. Additional studies on
micrometeorology and forest hydrology are necessary to understand this topic.
The studied areas ‘‘I’’ and ‘‘J’’ had a unique level of richness, since it is unusual to
observe an alpha diversity of more than 200 species for Atlantic Rainforest sites (Siqueira
1994; Tabarelli and Mantovani 1999; Scudeller et al. 2001). Our results surpass the species
richness found in other studies developed in Neotropical forests (see Tabarelli and
Mantovani 1999) and contrast with the argument given by these authors that Atlantic
Rainforests has lower species richness when compared to other Neotropical forests.
The variance partitioning indicated a clear influence from the spatially structured envi-
ronment and a secondary influence of the ‘‘pure’’ spatial component on variations in species
composition and richness. The former comprises a well-known pattern in the Atlantic Forest
(e.g., Oliveira-Filho and Fontes 2000; Scudeller et al. 2001) despite the lack of specific
approaches to assessing this component of variation. The influence of the ‘‘pure’’ spatial
component suggests limitations to biotic dispersal and indicates the importance of stochastic
mechanisms (Hubbell 2001). Both components (the spatially structured environment and
‘‘pure’’ space) could be related to the well-known (Martins 1991; Scudeller et al. 2001;
Caiafa and Martins 2010) geographically restricted distribution of tree species in the Atlantic
Forest (see Diniz-Filho et al. 2012). Thus, spatial proximity is undoubtedly a crucial factor
driving the vegetation patterns along the altitudinal range investigated in this study.
We found evidence for a floristic variation influenced by certain environmental vari-
ables, such as disturbances, topography and elevation related variables as well as spatial
proximity, and a high regional richness (a total of 563 species in the investigated area) and
extraordinarily high species richness ([200 spp.) in two montane areas. These results
indicate that the hillside forest studied here is an important location for additional detailed
studies to investigate the processes that generate and maintain its floristic composition and
diversity and is worthy of increasing efforts to preserve its biological heritage.
Acknowledgments We thank the BIOTA/FAPESP Program for supporting the BIOTA Functional Gradient
Project (Atlantic Ombrophyllous Dense Forest: Floristic Composition, Structure and Functioning within the
‘‘Serra do Mar’’ State Park, Brazil FAPESP Grants); the Graduate Programs in Plant Biology and Ecology at
UNICAMP; CNPq for the PhD scholarship granted to the first author; and CNPq, CAPES and FAPESP for
Grants awarded to the other authors and researchers directly or indirectly involved with this paper.
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