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Determinantsofrainforestfloristicvariationonanaltitudinalgradientin
southernCostaRica
AdinaChainGuadarrama,BryanFinegan,SergioVilchezandFernandoCasanoves
JournalofTropicalEcology/Volume28/Issue05/September2012,pp463481
DOI:10.1017/S0266467412000521,Publishedonline:
Linktothisarticle:http://journals.cambridge.org/abstract_S0266467412000521
Howtocitethisarticle:
AdinaChainGuadarrama,BryanFinegan,SergioVilchezandFernandoCasanoves(2012).Determinantsofrainforest
floristicvariationonanaltitudinalgradientinsouthernCostaRica.JournalofTropicalEcology,28,pp463481doi:10.1017/
S0266467412000521
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Journal of Tropical Ecology (2012) 28:463–481. © Cambridge University Press 2012
doi:10.1017/S0266467412000521
Determinants of rain-forest floristic variation on an altitudinal gradient
in southern Costa Rica
Adina Chain-Guadarrama∗,1, Bryan Finegan†, Sergio Vilchez‡and Fernando Casanoves‡
∗Graduate School, Mailbox 152, Tropical Agricultural Centre for Research and Higher Education (CATIE), Turrialba 30501, Costa Rica
†Production and Conservation in Forests Program, Tropical Agricultural Centre for Research and Higher Education (CATIE), Turrialba 30501, Costa Rica
‡Biostatistics Unit, Tropical Agricultural Centre for Research and Higher Education (CATIE), Turrialba 30501, Costa Rica
(Received 27 July 2012)
Abstract: The degree to which geographical location rather than environment affects the maintenance of high tropical
forest beta diversity on altitudinal gradients is not well understood. Forest composition and its relationship to climate,
soil, altitude and geographical distance were determined across an 1114-km2landscape in south Pacific Costa Rica
spanning an altitudinal gradient (0–1500 m asl). In 37 0.25-ha plots, >200 species of dicot trees (≥30 cm dbh) and
canopy palms (≥10 cm dbh) were found. Ordination analysis showed strong species composition patterns related to
altitude; plot coordinates on the main axis showed negative correlations to the abundance of lowland-forest species
Iriartea deltoidea (r=−0.54) and Brosimum utile (r=−0.65), and positive correlations to higher-altitude species
Alchornea glandulosa (r=0.63), Quercus sp. (r=0.50) and Ocotea sp. 2 (r=0.48). Mantel correlations, correlograms
and variation partitioning analysis of relationships between floristic composition and spatial and environmental factors
indicated that spatial location of the plots – potentially, dispersal limitation – was the single most important (R2adj =
0.149) driver of beta diversity, but that environmental heterogeneity also plays an important role. In particular, palm
species turnover was strongly related to soil chemical properties. The effects of dispersal limitation on floristic assembly
could determine the future distribution of plant communities as a result of climate change.
Key Words: altitude, beta diversity, canopy trees, canopy palms, climate change, dispersal assembly, geographical
distance, niche assembly, variation partitioning
Resumen: El efecto relativo de la ubicaci´
on espacial y
el ambiente sobre el mantenimiento de la alta diversidad
beta de los bosques tropicales en gradientes altitudinales
no es bien entendido. La composici´
on flor´
ısticaysu
relaci´
on con el suelo, el clima, la elevaci´
on y la distancia
geogr´
afica fue determinada en un paisaje de 1114 km2
en el Pac´
ıfico sur de Costa Rica, a lo largo de un
gradiente altitudinal (0–1500 msnm). En 37 parcelas
de 0.25-ha, >200 especies de ´
arboles (≥30 cm dap)
y palmas de dosel (≥10 cm dap) fueron encontradas.
An´
alisis de ordenaci´
on mostraron fuertes patrones de
composici´
on de especies relacionados a la altitud; las
coordenadas de las parcelas sobre los principales ejes
mostraron correlaciones negativas con la abundancia
de Iriartea deltoidea (r=−0.54) y Brosimum utile
(r=−0.65) ambas especies de bosques de bajura, y
correlaciones positivas con especies de mayores altitudes
como Alchornea glandulosa (r=0.63), Quercus sp.
1Corresponding author. Email: achain@catie.ac.cr
(r=0.50) y Ocotea sp. 2 (r=0.48). Correlaciones
de Mantel, correlogramas y an´
alisis de partici´
on de la
varianza de las relaciones entre la composici´
on flor´
ıstica
y factores espaciales y ambientales indicaron que la
ubicaci´
on espacial de las parcelas – potencialmente
limitaci´
on en la dispersi´
on – fue el determinante m´
as
importante (R2adj =0.149) de la diversidad beta, pero
que la heterogeneidad ambiental tambi´
en juega un rol
importante. En particular, el recambio de las especies de
palmas estuvo fuertemente relacionado a las propiedades
qu´
ımicas del suelo. Los efectos de la limitaci´
on en la
dispersi´
on sobre el ensamblaje flor´
ıstico podr´
ıa determinar
la distribuci´
on futura de las comunidades vegetales como
resultado del cambio clim´
atico.
INTRODUCTION
The theories of niche assembly and dispersal
assembly provide a valuable framework for interpreting
the relationships between species composition and
464 ADINA CHAIN-GUADARRAMA ET AL.
environmental and spatial heterogeneity, leading to a
better understanding of the high species turnover and
diversity of Neotropical rain forests (Condit et al. 2002,
Potts et al. 2002). Niche assembly theory states that
vegetation abundance and species composition patterns
are determined by local environmental conditions
and competition (de Blois et al. 2002, Jones et al.
2006). Under dispersal assembly theory, in contrast,
community composition depends on the composition
of the surrounding communities and species dispersal
capacity, and floristic similarity among sites is predicted
to decrease with increasing geographical distance (higher
spatial isolation) because of spatially limited dispersal,
independent of any environmental difference (Hubbell
2001).
There is no consensus regarding the relative
importance of niche and dispersal assembly in tropical
forests. Several authors (Costa et al. 2009, Duque et al.
2002, Jones et al. 2006, 2008; Phillips et al. 2003,
Pyke et al. 2001, Ruokolainen et al. 2007, Sesnie
et al. 2009, Tuomisto et al. 2003a, 2003b) suggest
that environmental variables are the most important
determinants of beta diversity. Others indicate a
predominant role for dispersal limitation (Bohlman et al.
2008, Chust et al. 2006, Duque et al. 2009, Normand
et al. 2006, Vormisto et al. 2004). Contrasting results
seem likely to be due partly to differences in the plant
groups studied (Sesnie et al. 2009), the environmental
variables measured (Jones et al. 2008), the spatial scale of
observation and the associated degree of environmental
heterogeneity in the landscapes (Condit et al. 2002, Costa
et al. 2009, Honorio Coronado et al. 2009, Jones et al.
2006, Sesnie et al. 2009). Bohlman et al. (2008), Condit
et al. (2002), Duque et al. (2002, 2009) and Tuomisto
et al. (2003a) report that environmental control of
compositional similarities is stronger at regional scales
and geographical control at finer scales. Across the
Amazon basin, on the other hand, Honorio Coronado
et al. (2009) report that geographical distance is more
important in explaining species turnover, while soil
fertility plays a more important role at smaller scales.
The lack of attention to altitudinal gradients in these
studies is notable. Variation in tropical forest structure
and floristics with altitude is marked (Homeier et al. 2010,
Lieberman et al. 1996, Mac´
ıa et al. 2007, Martin et al.
2011) making a major contribution to high biodiversity
in global hotspots (Malhi et al. 2011), and its underlying
causes are complex (Grubb 1977). In all these studies,
niche assembly is the implicit underlying model of species
turnover. The potential roles of spatial location and
dispersal limitation remain largely untested.
Working in terra firme forest sites along an altitudinal
gradient in a fragmented landscape of south Pacific
Costa Rica, we therefore sought to (1) characterize
forest sites according to their floristic composition,
(2) quantify floristic and environmental differences
among forest sites, (3) determine the spatial structure
of floristic and environmental characteristics, and (4)
assess relationships between species composition and
environment, altitude and geographical distance. Two
alternative general hypotheses are proposed: (1) species
composition is determined by environmental variability,
with spatial autocorrelation of species composition
related to the existence of environmental gradients,
even when geographical distance is controlled (niche
assembly); and (2) species composition is determined by
dispersal limitation, thus floristic similarity decreases as
geographical distance increases, even when environment
is controlled (dispersal assembly). Because palms
represent an a priori functional group (Chazdon et al.
2010)and show awell-defined response toenvironmental
variation (Clark et al. 1995, Costa et al. 2009, Poulsen
et al. 2006, Sesnie et al. 2009) we determined their
response separately from that of dicot trees.
METHODS
Study site
The study was carried out in a landscape that links
La Amistad International Park, in the Cordillera de
Talamanca, and the protected areas of the Osa Peninsula,
in the south Pacific region of Costa Rica (Figure 1).
The 1114-km2landscape encompasses an altitudinal
gradient from sea level to more than 1500 m asl. From
south-west to north-east, terrain rises abruptly from the
coast to 1500 m asl in the south coastal range (Fila
Coste ˜
na del Sur), which functions as a geographical
barrier 18–20 km from the coast. It is a fragmented
landscape where remnant forest patches are surrounded
by coffee plantations on higher ground in the north and
oil palm in the lowlands to the south (C´
espedes et al.
2008). Three large forest patches (>1000 ha) exist within
areas containing a low probability of land-use change, in
addition to an important number of smaller forest patches
(C´
espedes et al. 2008).
Predominant soil types are poorly developed entisols
and acid, infertile utisols, and moderately fertile
inceptisols (Fassbender & Bornemisza 1987, ITCR 2004,
S´
anchez 1981). Annual rainfall ranges from 3000 to
5500 mm (ITCR 2004). Two seasons are recognized, a
drier one from December to April, with the lowest rainfall
in February, and a wetter season the rest of the year where
October is the wettest month (IMN, http://www.imn.
ac.cr/educacion/climacr/pacifico_sur.html). Mean an-
nual temperature at the lowest point in in our sampling
corresponded to c. 26 ◦Candc. 19 ◦C at the highest point
(Figure 2; Table 1).
Forest beta diversity on an altitudinal gradient 465
Figure 1. Location of the studied landscape at the south Pacific region of Costa Rica. Remnant forest patches, protected areas and locations of sample
plots are observed.
Field sampling and measurements
We adopted the sampling protocol developed by Ramos
& Finegan (2006) and Sesnie et al. (2009) and shown
by the latter authors to be effective for testing hypotheses
like ours. A total of thirty-seven 0.25-ha plots (50 m ×
50 m) were established between the altitudes of 64 m and
1528 m asl in forest patches with a minimum area of 5
ha (Figure 1). Sites with manageable access were selected
and extreme conditions of flooded or disturbed areas were
avoided, identifying only terra firme environments. Plots
were positioned at random points at a minimum distance
of 150 m from the forest edge and at least 300 m from
the nearest neighbouring plot. On large forest patches
we often established more than one plot. All plots were
geo-referenced using a GPS (Garmin GPSMAP R
60CSx),
and altitude was recorded using a calibrated altimeter.
On each plot, the stem diameter at breast height (dbh,
1.3 m) of all dicot trees ≥30 cm dbh and palms ≥10 cm
dbh was measured. Species were identified on site by a
parataxonomist and when necessary, herbarium samples
were taken and identified by Nelson Zamora, curator of
botany at Costa Rica’s National Institute for Biodiversity
(INBio) (Bouroncle & Finegan 2011, Finegan & Delgado
2000, Sesnie et al. 2009).
We collected a homogenized soil sample compiled from
five subsamples up to 30 cm depth in each sample
plot. Soil depth was determined using a 1.10-m-long
metal rod. Soil was analysed at the soils laboratory
of the Centro Agron´
omico Tropical de Investigaci´
on
yEnse
˜
nanza (CATIE), in Turrialba, Costa Rica, where
samples were air-dried and soil chemical and physical
analysis conducted. Soil texture (per cent of sand, silt
and clay) was determined by the Bouyoucos method.
Extractable K and P were measured using Modified Olsen
extractions with a 0.5 M sodium bicarbonate (NaHCO3)
solution at a pH of 8.5. Soil pH in water was measured,
and extractable Ca, Mg, and total acidity extractions
were done in a 1N suspension of potassium chloride
(KCl).
466 ADINA CHAIN-GUADARRAMA ET AL.
Figure 2. (Colour online) Illustration of climate gradients in the studied landscape. Sampled vegetation plots are indicated by black squares. Altitude
(a), temperature seasonality given by temperature standard deviation (b), annual precipitation (c), and seasonal precipitation regimes given by
precipitation of the driest month (d) and precipitation coefficient of variation (e), are shown.
Forest beta diversity on an altitudinal gradient 467
Table 1. Climatic and altitude attributes of the 37 (0.25 ha) vegetation
plots sampled in the studied landscape.
Mean ±SE Range
Altitude (m asl) 759 ±70.9 64–1528
Mean annual temperature (◦C) 23.0 ±0.4 18.6–26.4
Temperature standard deviation 6.8 ±0.1 5.8–7.8
Minimum temperature of the coldest
month (◦C) 16.7 ±0.4 12.4–20.3
Annual precipitation (mm) 3374 ±75.4 2852–5057
Precipitation coefficient of variation 60.6 ±1.1 44–72
Precipitation of the driest month (mm) 52.3 ±4.8 17–146
Temperature and precipitation data were taken
for each sample plot from Worldclim (http://www.
worldclim.org/) digital data layers. Worldclim data
represent average global climate grids from the years
1950–2000, with a resolution of c. 1km
2(30 arc-
second). Worldclim data have been previously used
to map and spatially model species distributions using
geographical information systems (Hijmans et al. 2005).
We used mean annual temperature (Ann Temp) and two
measures of seasonal temperature variation, minimum
temperature of the coldest month (Min Temp) and
temperature standard deviation (Temp SD). Annual
precipitation (Ann prec) and rainfall seasonality from
precipitation coefficient of variation (Prec CV) and pre-
cipitation of the driest month (Prec Driest) were also
taken for further statistical comparison with floristic data.
Data analyses
To visualize relationships between plots and the most
important species contained within them we performed a
Non-metric Multidimensional Scaling (NMS) ordination
analysis in PC-ORD v. 4.25 (PC-ORD. Multivariate
analysis of ecological data, Version 4.25, MjM Software
Design, Gleneden Beach, Oregon, USA), using the
autopilot option and the Sørensen similarity coefficient.
Ordination was carried out using the importance value
index (IVI) of each species. Forty runs with real data and
50 runs with randomized data were used to evaluate the
stability of the final ordination. Only species recorded in
two or more plots were taken into account for this analysis
(Greig-Smith 1983).
Pearson correlations to determine whether significant
relationships (P <0.05) existed among soil, climate, and
altitude variables were performed using the InfoStat
statistical package (InfoStat 2010, Grupo InfoStat,
Universidad Nacional de C´
ordoba, C´
ordoba, Argentina).
Relationships between floristic, environmental, alti-
tude and geographical distances were assessed through
Mantel correlation tests. Bray–Curtis distance matrices
were computed for abundances of all species and for
palm abundance separately. Euclidean distance matrices
were obtained for soil, climate and altitude variables, as
well for geographical distance (from plots coordinates)
which were transformed to natural logarithms (Hubbell
2001). Combined Euclidean environmental matrices
were obtained for all climate variables (Climate), all
soil variables (Soil), all soil texture variables (Soil T),
and all soil chemical variables (Soil Q); when necessary
the variables were standardized before computing the
combined matrices. The statistical significance of simple
rMwas estimated by 999 permutations (Legendre
& Legendre 1998). Since multiple tests are done
simultaneously we compared probabilities P to a
Bonferroni-corrected probability level of α’=α/k, where
α=0.05 and k is the total number of tests performed
(Legendre & Legendre 1998).
When multiple factors are correlated with floristic
composition, the assessment of spatial autocorrelation
allows spatial patterns to be identified among environ-
mental variables, providing a better understanding of
the relationship between environmental variables and
floristic composition and distribution. Using the PASSaGE
v.2 software (Rosenberg & Anderson 2011), the degree
of spatial autocorrelation between floristic composition
and environmental variables was evaluated using Mantel
correlograms, where Mantel correlation coefficients (rM)
are compared between distance classes to determine the
level of spatial autocorrelation among variables through
increasing interplot distances. Distance classes ranged
from 0.3–48 km, according to minimum and maximum
distance between plot pairs, spaced initially in 0.3-km
intervals and then at 3-km intervals. The statistical
significance of rMin the correlogram was estimated by
999 permutations, and P values were adjusted using a
progressive Bonferroni correction following Legendre &
Legendre (1998).
In addition to the frequently used Mantel correlation-
based approach to test hypotheses like ours, we performed
a variation partitioning analysis (Borcard et al. 1992,
Legendre 2008) to partition the variation of species
compositioninto the contributionsof soil, climate, altitude
and space mechanisms that are correlated to dicot tree
and palm species in the landscape. Following Legendre
& Gallagher (2001) and Jones et al. (2008), prior to
the analysis we transformed the species abundances
using a Hellinger transformation to downweight the most
abundant species in the analysis. The decostand function
in the vegan library (vegan: Community Ecology Package,
R package version 2.1–14/r2120, http://R-Forge.R-
project.org/projects/vegan/) of R statistical software (R: A
Language and Environment for Statistical Computing, R
Development Core Team, http://www.R-project.org) was
used.
The spatial structure was represented by positive
eigenvalues generated by a principal coordinates of
neighbour matrices (PCNM) analysis. PCNMs are
constructed from a matrix of geographical distance
among samples and used as spatial predictors that
468 ADINA CHAIN-GUADARRAMA ET AL.
can be easily incorporated as explanatory variables
into regression models or canonical analysis (Borcard
& Legendre 2002, Dray et al. 2006). PCNMs were
generated using the PCNM library in R (PCNM:
PCNM spatial eigenfunction and principal coordinate
analyses, R package version 2.1/r82, http://R-Forge.R-
project.org/projects/sedar/).
Following Jones et al. (2008) and Legendre et al.
(2009), a two-step forward selection (Blanchet et
al. 2008) was run on each set of environmental
(climate or soil variables significantly related to species
composition according to previous Mantel tests) and
spatial (PCNMs) explanatory variables separately in order
to select those with a significant (P <0.05, 999 random
permutations) contribution to explaining variation in
floristic composition. The function forward.sel in packfor
R library (packfor: Forward Selection with permutation
(Canoco p.46), R package version 0.0–8/r100, http://R-
Forge.R-project.org/projects/sedar/) was used, and only
the selected variables were used in subsequent analyses.
The forward selection procedure is based on permutation
procedure using residuals from the reduced model in a
redundancy analysis.
The variation of the dicot tree and palm community
composition data and palm community separately
was partitioned between soil, climate, altitude and
PCNMs explanatory variables using redundancy analysis
(Borcard et al. 1992, Legendre et al. 2009). This analysis
partitions the variation of response table Y with respect
to two, three, or four explanatory tables, without the
requirement of removing collinear variables prior to
partitioning (Borcard et al. 1992). Adjusted R2values
(Radj2) indicating the proportion of variation explained by
each set of explanatory variables were reported (Peres-
Neto et al. 2006). The significance of these fractions was
tested by permutation tests (P <0.05, 999 permutations).
These analyses were computed using the functions
varpart, rda and anova in the vegan library of R.
The use of canonical analysis on raw data offers
more powerful tests of significance than regression on
distance matrices (Gilbert & Bennett 2010, Legendre
et al. 2005). However, the use of Mantel tests in our
analysis permits comparison with previous studies testing
hypothesis about the origin of beta diversity (Bohlman et
al. 2008, Duque et al. 2009, Honorio Coronado et al. 2009,
Mac´
ıa et al. 2007, Sesnie et al. 2009).
RESULTS
Floristics
A total of 1365 individuals (391 palms and 974
dicot trees) from 237 species (eight species of palm
and 229 of tree), 149 genera and 54 families were
recorded (Appendix 1). More than 80% of individuals
were identified to the species level; grouping individuals
with similar morphological characteristics the rest
were assigned either to genus (14%) or unknown
morphospecies (3%) level.
A three-dimensional solution was obtained from the
NMS analysis (Figure 3), performed with information
of 131 species recorded in two or more plots (106
species recorded in only one plot were not taken into
account), with a final stress index of 18.0 that was
significantly lower than 95% of the runs from Monte Carlo
randomizations (P <0.05). Axes 1, 2 and 3 respectively
explained 10.7%, 30.7% and 24.4% of floristic variation
among plots, for a total of 65.9% of explained variation.
Axis 2 represented an altitudinal gradient. It showed
strong negative correlations with two palm species,
Iriartea deltoidea (r=−0.54) and Socratea exorrhiza
(r=−0.39), and dicot tree species such as Brosimum
utile (r=−0.65), Brosimum lactescens (r=−0.44), Inga
sp. (r=−0.44), and Carapa guianensis (r=−0.41), which
dominate lowland forests; and strong positive correlations
with Alchornea glandulosa (r=0.63), Beilschmiedia
tovarensis (r=0.56), Pseudolmedia mollis (r=0.54),
Dacryodes sp. (r=0.50), Quercus sp. (r=0.50) and Ocotea
sp. 2 (r=0.48), dicot tree species characteristic of higher-
altitude sites. Compositional differences between lowland
sites were brought out by Axis 3 and were determined
by variation in the abundance of the palms Welfia regia
(r=0.54) and Euterpe precatoria (r=0.47), and dicot
tree species of the genus Vochysia (V. ferruginea,V. allenii,
r=0.68 and r=0.39, respectively), as well as other
species such as Hirtella triandra (r=0.49), Aspidosperma
megalocarpon (r=0.43) and Calophyllum longifolium (r=
0.39), all positively correlated with Axis 3. Anacardium
excelsum (r=−0.54) and Tetragastris panamensis (r=
−0.46), negatively correlated with Axis 1, also played an
important role in the differentiation of the lowland sites.
Altitude, climate and soil gradients
The more than 20-fold variation in altitude was the most
evident gradient in the landscape (Table 1; Figure 2).
Altitude was correlated negatively with all temperature
variables, annual and precipitation of the driest month
values, and positively correlated with precipitation
coefficient of variation (so that higher-altitude sites, as
well as being cooler, tend to have lower and more
seasonal rainfall; Table 2). Mean annual and minimum
temperature of the coldest month showed considerable
variation across the landscape, with temperature being
moderately cooler in plots of higher altitudes. Annual
precipitation and precipitation of driest month also varied
widely (Table 1, Figure 2).
Chemical and physical soil properties also showed a
high degree of variation (Table 3). Cations (Ca, Mg, K)
Forest beta diversity on an altitudinal gradient 469
Figure 3. Ordination of vegetation plots along NMS axes two and one (a), and two and three (b). Plot’s symbol size represents plot altitude, so bigger
squares are equal to higher altitudes. Species better correlated to these two main axes of variation are shown (cross symbol): Vochysia ferruginea
(VOCHFE), Welfia regia (WELFRE) and Socratea exorrhiza (SOCREX), Tetragastris panamensis (TETRPA), Iriartea deltoidea (IRIADE) and Anacardium
excelsum (ANACEX) characteristic of lowland forests; and Beilschmiedia tovarensis (BEILTO), Alchornea glandulosa (ALCHGL) and Quercus sp. (QUERSP)
characteristic of higher altitude forests.
470 ADINA CHAIN-GUADARRAMA ET AL.
Table 2. Correlation matrix and coefficients from simple Pearson correlation tests between soil, climate and altitude values from forest plots. Significant correlation are shown as ∗∗∗P<0.001,
∗∗P<0.01, ∗P<0.05. See text for those variables with abbreviations.
Altitude Soil depth Sand Silt Clay pH Acidity Ca Mg K P Ann Temp Temp SD Min Temp Ann Prec Prec Driest
Soil depth 0.47∗∗
Sand 0.65∗∗∗ 0.58∗∗∗
Silt −0.38∗−0.03 −0.51∗∗
Clay −0.59∗∗∗ −0.65∗∗∗ −0.93 0.15
pH −0.35∗−0.38∗−0.29 0.56∗∗∗ 0.10
Acidity −0.17 0.08 0.20 −0.55∗∗∗ 0.01 −0.54∗∗∗
Ca −0.30 −0.39∗−0.17 0.25 0.09 0.82∗∗∗ −0.20
Mg −0.44∗−0.08 −0.34∗0.49∗∗ 0.17 0.44∗∗ −0.03 0.55∗∗∗
K−0.27 −0.29 −0.13 0.39∗−0.02 0.43∗∗ −0.09 0.41∗∗ 0.41∗∗
P 0.14 −0.06 0.06 −0.0017 −0.06 −0.09 −0.04 0.03 0.05 0.08
Ann Temp −0.97∗∗∗ −0.46∗∗ −0.65∗∗∗ 0.36∗0.59∗∗∗ 0.29 0.23 0.30 0.44∗∗ 0.33∗−0.15
SD Temp −0.82∗∗∗ −0.48∗∗ −0.46∗∗∗ 0.12 0.47∗∗ 0.24 0.30 0.42∗∗ 0.42∗∗ 0.24 −0.05 0.85∗∗∗
MinTemp −0.98∗∗∗ −0.46∗∗ −0.64∗∗∗ 0.34∗0.59∗∗∗ 0.28 0.23 0.29 0.42∗∗ 0.31 −0.16 0.99∗∗∗ 0.86∗∗∗
Ann Prec −0.51∗∗ −0.01 −0.24 0.21 0.19 0.04 0.07 0.12 0.13 0.04 0.09 0.53∗∗∗ 0.52∗∗∗ 0.56∗∗∗
Prec Driest −0.68∗∗∗ −0.09 −0.36∗0.26 0.30 0.06 0.17 0.11 0.20 0.15 −0.05 0.70∗∗∗ 0.59∗∗∗ 0.73∗∗∗ 0.93∗∗∗
Prec CV 0.77∗∗∗ 0.24 0.49∗∗ −0.37∗−0.40∗∗ −0.19 −0.08 −0.18 −0.27 −0.27 0.13 −0.78∗∗∗ −0.60∗∗∗ −0.81∗∗∗ −0.82∗∗∗ −0.90∗∗∗
Forest beta diversity on an altitudinal gradient 471
Table 3. Summarizedsoilchemicaland physical properties of all sampled
forest plots across the fragmented landscape in south Pacific Costa Rica.
Mean ±SE Range
Ca (cmol(+)l
−1) 5.2 ±0.8 0.21–22.7
K (cmol(+)l
−1) 0.2 ±0.02 0.06–0.4
Mg (cmol(+)l
−1) 1.9 ±0.3 0.18–11.2
P(mgl
−1) 1.8 ±0.7 0.4–6.3
Total acidity (cmol(+)l
−1) 3.3 ±0.5 0.09–13.9
pH 5.1 ±0.1 4.22–6.4
Sand (%) 48.2 ±2.7 21–75.2
Silt (%) 24.5 ±1 13.8–35.9
Clay (%) 27.3 ±2.3 5–51.3
Soil depth (cm) 75.3 ±3 42.8–108
werepositively correlated amongthemselves and with pH,
in turn negatively correlated with acidity (Table 2). Soils
with a high percentage of silt were positively associated
with Mg and K presence (Table 2). Extractable phosphorus
(P) did not correlate with any soil or climate variables.
Soil trends with altitude were increasing depth and sand
content, and decreasing pH, silt and clay content and Mg.
No significant relationship between other soil cations and
altitude was found (Table 2).
Spatial autocorrelation among variables
Multivariate Mantel correlograms (Figure 4) indicated
that patterns of both environmental and compositional
similarity occurred with respect to distance across the
landscape. Floristic and environmental (soil and climate)
similarity increased rapidly up to the 1.2–3-km class, from
which both presented a steep decay. Floristic, climatic and
altitudinal correlations among plots continued to decline
until loss of significance between 18 and 21 km; after
this distance, positive significant autocorrelation was
observed, but always with lower correlation coefficients.
Soil chemical properties were significantly spatially
autocorrelated until the 3–6-km class, and physical
properties up to the 12–15-km class, as well as in some
larger distance classes, albeit with very low correlation
coefficients. Last class contained information based on
very few plot comparisons and was taken out from the
correlograms.
Correlation between floristic composition and climate, soil,
altitude and space
Significant correlations (P <0.05) were found between
species composition and soil texture and chemical
properties (Table 4). Soil depth was correlated only with
overall species composition (dicot trees and palms). Sand,
clay and all texture factors combined were correlated with
the overall species composition, but not with palm species
composition alone. All chemical soil properties combined
and K and Ca separately were correlated with both overall
species composition and palm species composition. Mg
was only correlated with palm community and pH with
overall floristic composition. All temperature variables
were correlated with dicot tree and palm communities,
but only precipitation coefficient of variation with overall
species composition. Combined climatic variables were
correlated with both the overall species composition
and palm species separately. Finally, both altitude and
geographical distance were highly correlated with overall
composition and palm composition separately, showing
the highest Mantel rvalues in comparison with soil and
climate variables (Table 4).
We also performed Mantel tests to assess the
relationship between the dicot tree community alone
Figure 4. Mantel correlograms. Spatial autocorrelation coefficients of forest plot pairs for 18 geographical distance classes. Mantel correlations from
distance matrices of floristic composition, altitude, climate (all temperature and precipitation variables combined), soil texture (sand, clay and silt
per cent combined) and soil chemical properties (pH, Ca, K, Mg, and P combined). Filled or hatched symbols denote progressive Bonferroni corrected
significant correlations (P <0.05), and unfilled symbols non-significant correlations. The x-axis shows the maximum value in each distance class.
472 ADINA CHAIN-GUADARRAMA ET AL.
Table 4. Simple Mantel correlation (rM) tests between floristic
composition and climate, soil, altitude and geographical distance.
Tests were performed for the overall canopy community (dicot trees
and palms) and palms separately. Significant correlations denoted by
an asterisk (∗) were determined from 999 permutations and further
Bonferroni-corrected (α=0.05/22). See text for those variables with
abbreviations.
Dicot trees and palms Palms
Variable rMrM
Soil Ca 0.17∗0.15∗
K 0.21∗0.25∗
Mg 0.09 0.14∗
P−0.01 0.10
Acidity −0.01 0.04
pH 0.18∗0.12
Soil Q 0.18∗0.26∗
Depth 0.11∗0.07
Sand 0.30∗0.09
Silt 0.06 0.10
Clay 0.22∗0.05
Soil T 0.23∗0.10
Soil 0.26∗0.26∗
Climate Ann Temp 0.38∗0.27∗
Temp SD 0.32∗0.24∗
Min Temp 0.38∗0.29∗
Ann Prec 0.04 0.09
Prec CV 0.12∗0.09
Prec Driest 0.05 0.09
Climate 0.25∗0.21∗
Geographical distance 0.47∗0.30∗
Altitude 0.44∗0.31∗
and all environmental and space variables. Association
patterns were similar to those observed for tree and palm
combined, so in subsequent sections we only present
analysis for the overall community of canopy species.
Variation partitioning of dicot tree and palm species
composition
The forward selection procedure retained five principal
coordinates of neighbour matrices (PCNMs) for modelling
the overall species composition variation and two PCNMs
for modelling palm species separately. The final soil
component related to the overall species composition
included clay, Ca and K, and Ca and Mg for palms
separately. The climate component included minimum
temperature of the coldest minth for dicot trees and palms
combined, and temperature standard deviation for palms
separately.
Soil, climate, altitude and space were related to
20.4% of variation of the overall species composition,
while 79.6% remained unexplained (Figure 5). PNCMs
were more strongly correlated with variation of species
composition (R2
adj =0.15%), in comparison with soil,
climate and altitude components (R2
adj of 0.09, 0.07
and 0.07 respectively, Figure 5). These results indicated
that a relatively high proportion of the variation in
species composition is spatially structured, which is
corroborated by the percentage of species composition
variation related to soil, climate and altitude components
that could not be separated from space (48.0%, 83.3% and
81.3% respectively, calculated from Table 5), i.e. a high
percentage of these components is spatialized (Figure 5).
In turn, 28.2% of the total variation related to PCNMs was
associated to the soil component and 38.6% to the altitude
and 38.2% to climate. Furthermore, when observing
the proportion related to the pure effect of each one
of the four measured components, i.e. while controlling
the three other components, climate and altitude were
not significant or null fractions and the space and soil
components were best related to variation in species
composition (Table 5).
Thirty-one per cent of variation of palm species
composition was related to soil, climate, altitude and space
factors. As in the case of overall species composition,
the results indicated that variation of palm floristic
composition was spatially structured. PCNMs were also
better related to palm variation among sites (R2adj =
0.22), followed by the climate component (R2adj =0.20),
Figure 5. Total explained variation by all and each one of the four measured components (soil, climate, altitude and space) for both the overall plant
community and palm community separately. The amount of variance that could not be separated from space is indicated by the diagonally hatched
section of each bar.
Forest beta diversity on an altitudinal gradient 473
Table 5. Variation partitioning results for dicot tree and palm
communities sampled in 37 0.25-ha plots in the studied landscape.
Adjusted R2statistics (R2
adj) and F statistics are presented for all
measured factors, S: soil, C: climate, A: altitude, SP: space. The effect of
factors controlling for any combinations of other factors is denoted by
symbol |. Negative or null fractions are denoted with a dash.
Dicot trees and palms Palms
Factor R2
adj FPR
2
adj FP
S 0.087 2.15 0.005 0.118 3.41 0.005
C 0.068 3.64 0.005 0.198 9.90 0.005
A 0.071 3.74 0.005 0.173 8.55 0.005
SP 0.149 2.26 0.005 0.222 6.13 0.005
All 0.204 1.93 0.005 0.313 4.48 0.005
S|C,A,SP 0.037 1.45 0.005 0.053 2.29 0.017
C|S,A,SP 0.002 1.08 0.360 0.003 0.00 N/A
A|S,C,SP – – – – – –
SP|S,C,SP 0.059 1.46 0.005 0.033 1.95 0.044
S|SP 0.045 1.58 0.005 0.085 3.08 0.005
C|SP 0.011 1.42 0.034 0.052 3.41 0.018
A|SP 0.013 1.49 0.020 0.018 1.80 0.150
SP |S 0.107 1.88 0.005 0.188 5.62 0.005
SP |C 0.092 1.77 0.005 0.075 2.81 0.010
SP |A 0.091 1.76 0.005 0.066 2.53 0.013
altitude (R2adj =0.17) and soil (0.12.1%; Figure 5).
The amount of palm community variance related to the
intersection of altitude, climate and PCNMs (R2adj =
0.13), that represents a considerable proportion of the
variance related to each one of this factors separately
(77.1%, 67.4% and 60.2% in the above given order),
suggest that is difficult to separate the effect of these
three factors on palm species distribution. In fact 74%
and 89.7% of the variation of palm community related
to climate and altitude was spatialized, and 66.2% and
70.1% of variation related to space could not be separated
from climate and altitude processes respectively. Again
the pure effects of climate and altitude were non-
significant, and altitude was not significantly related to
palm species composition when space was controlled for.
Moreover, the pure contribution of soil and space was
higher than any of the other components (Table 5) and,
in contrast to results obtained for the overall species
composition, the effect of soil on palm composition was
less spatialized, suggesting the importance of edaphic
properties in the determination of variation in palm
composition across the landscape.
DISCUSSION
Recognized plant communities
Multivariate analysis of vegetation, carried out with data
from dicot canopy trees ≥30 cm dbh and palms ≥10
cm dbh, allowed us to identify a clear pattern of floristic
differences among vegetation plots in the study landscape.
Previous studies conducted with both understorey and
canopy plant species have not found important differences
between these groups in their relationship with the
environment(Duque et al. 2002,2009; Mac´
ıaet al. 2007),
allowing us to conclude that recognized floristic patterns
for trees ≥30 cm dbh and palms ≥10 cm dbh are probably
representative of the forest as a whole. This assessment
of forest compositional variation was a baseline for
subsequent assessment of relationships between forest
floristic composition and environmental factors.
Altitude, environment and floristic gradients
Species composition turnover displayed an evident spatial
pattern associated with environmental variables and
geographical distance, as observed in the correlograms.
As in other studies in Central America (Condit et al. 2002,
Sesnie et al. 2009), environmental and floristic similarity
in this study showed first a steep decline and then a
persistent decline to a point where significance was lost
(between 40 km and 50 km very low floristic correlation
was found). Sesnie et al. (2009) evaluated these trends on
analtitudinal gradient to1200 m aslalthough Condit et al.
(2002) make no reference to the altitude of their lowland
plots; in both studies, maximum interplot distances were
very similar.
The complex topography of Costa Rica results in great
climate variability over short distances. Similar trends are
seen in soils, whose variables of structure, composition
and fertility are determined in part by climatic and
topographic factors (Grayum et al. 2004, Grubb 1977).
In our study, floristic and environmental similarity both
respond to altitude changes, both interestingly decreasing
to the lowest point at the same distance where the coastal
range rises up (18–20 km from the coast). Increasing
geographical distance is associated with changes in
altitude and correlated changes in soils and climate. In
this way, geographical distance and especially altitude
represent indirect environmental gradients along which
floristic composition varies (Gentry 1988, Givnish 1999,
Homeier et al. 2010, Mac´
ıa et al. 2007, Sesnie et al. 2009).
Altitude not only closely covaried with temperature and
precipitation variables, but was also correlated with
edaphic factors indicating a decrease in soil nutrient
and fertility as altitude rises (Homeier et al. 2010).
In the absence of dispersal limitation, this terrain-
related environmental heterogeneity is bound to generate
differential species responses, in accordance to the niche
assembly rule. The key question is, what evidence is there
for dispersal limitation?
Determinants of dicot tree and palm floristic patterns
When examining the relationships between plant species
composition and soil, climate, altitude and geographical
474 ADINA CHAIN-GUADARRAMA ET AL.
distance, it is clear that all these factors are intercorrelated
and that the understanding of their relative importance
in determining floristic composition is not an easy
task. However, from our distance matrix and variation
partitioning analyses we found general trends showing
that environmental and floristic patterns evidence a
spatial structure across the landscape and that differences
exist among factors related to overall and palm species
composition individually.
In spite of the very marked environmental hetero-
geneity in our study landscape, Mantel correlations and
variation partitioning analyses show that geographical
distance was the single most important factor in the
determination of variation in the overall composition
of dicot tree and palm species. Complete support for
the dispersal limitation hypothesis is not expected in
such a variable environment, but in our landscape
environmental control was correlated to less variation
in species composition than space. Some authors (Chust
et al. 2006, Condit et al. 2002, Ruokolainen et al. 2007,
Vormisto et al. 2004) have pointed out that such a
predominance of dispersal limitation over environmental
filtering (Chust et al. 2006, Condit et al. 2002, Duque
et al. 2009, Normand et al. 2006) might be attributed to
inadequaterepresentation of keyenvironmental variables
such as soil physical and chemical properties; however,
in the present study, a range of such variables were
measured in each plot.
Altitude is an important correlate of variation in plant
species composition, due to both environmental and
spatial changes associated with it. Mac´
ıa et al. (2007) also
found that altitude and geographical distance explained
species composition patterns in Bolivian Amazonia, but
their results and those of Sesnie et al. (2009) in northern
Costa Rica more strongly supported edaphic control on
plant species composition. These studies had a shorter
altitudinal gradient than ours and Mac´
ıa et al. (2007)
did not include climatic variables in their analysis.
Even though the construction of the climate model
we used is strongly dependent on altitude (Hijmans
et al. 2005), our results suggest that altitude is not
merely a climate surrogate, but also a component of
geographical distance in the terrain. In fact, the variation
partitioning approach pointed out that there is no
variation in species composition that can be correlated
to the pure effect of climate and altitude, demonstrating
that even when altitude and temperature show strong
correlation coefficients with species composition they are
not separable from influences of the environmental and
spatial position components.
Soil factors showed significant correlation coefficients
with plant patterns, contributing to explain dicot tree and
palmfloristic patterns inthe landscape. Ca, K and clay con-
tent were correlated with overall species composition, in
agreement with other studies that indicate a contribution
of environmental control to the determination of floristic
patterns (Costa et al. 2009, Duque et al. 2002, Jones
et al. 2006, Mac´
ıa et al. 2007, Phillips et al. 2003,
Pyke et al. 2001, Rukolainen et al. 2007, Sesnie et al.
2009, Tuomisto et al. 2003a, 2003b). Values of these
chemical properties have been shown to be correlated
with tropical plant diversity (Gentry 1988, Honorio
Coronado et al. 2009, Mac´
ıa et al. 2007) and variation
in tree species composition (Phillips et al. 2003, Potts
et al. 2002, Ruokolainen et al. 2007, Tuomisto et al.
2003c).
In the variation partitioning analysis, the soil
components related to the overall species composition
included clay, Ca and K; however, a high percentage of
the soil explanation was spatialized. This was not the case
for the palm species variation, in which a smaller faction
of the soil component related to palm composition was
spatialized. This suggests that palm species have stronger
responsethan dicot canopytrees to chemical soilgradients
which may not be spatially structured at the studied scale
(see also Clark et al. 1995, Costa et al. 2009, John et al.
2007, Sesnie et al. 2009). It was also observed that the
abundance of W. regia declined substantially in sandy
soils at altitudes up to c. 500 m asl, while I. deltoidea and
E. precatoria were distributed in a much wider area in
a longer altitudinal (up to c. 1000 m asl) and edaphic
gradient, as found by Lieberman et al. (1996) and Sesnie
et al. (2009).
Our results support space as the major individual factor
influencing beta diversity of dicot trees and palms even
in this environmentally variable altitudinal gradient, but
also show that neutral forces are complementary with,
not exclusive from, environmental control. Dicot tree and
palm species turnover in these Neotropical terra firme
forests, 64–1528 m asl appear to be better explained by
a coupled effect of both dispersal limitation and environ-
mental filtering as suggested by several studies in lowland
environments (Chust et al. 2006, Condit et al. 2002,
Duivenvoorden et al. 2002, Duque et al. 2009, Normand
et al. 2006, Valencia et al. 2004, Vormisto et al. 2004).
A relatively high proportion of community variation in
the landscape could not be related to neither climate, soil,
altitude and spatial data, which can be attributed to other
random dispersal and mortality mechanisms, or variation
caused by unmeasured environmental or biological (e.g.
species traits) variables (Legendre et al. 2009). Land-use
configuration, another variable not taken into account
in the analysis, could also have an effect on the plant
species composition (Marini et al. 2011). Our sampling
protocol is designed to minimize edge effects on forest
characteristics (Sesnie et al. 2009), but the possible
influence of the fragmentation of the landscape remains
unknown. Other processes underlying the influence of
geographical location on the species composition of these
forests may be those acting at larger scales. Boundaries of
Forest beta diversity on an altitudinal gradient 475
climatically defined forest types may have shifted by up to
700m during thelast glaciation (Islebeet al. 1995), sothat
the current distributions of species are partly the result of
their capacity to disperse in relation to natural climate
change. They may also be related to species radiations
from different centres of origin especially in this region
of the Central American isthmus where species from
the Amazonian region and the North American region
coincide (Gentry 1992). Finally, we need to consider
factors related to study design and statistical power.
A higher number of plots and replicates at particular
points on the altitudinal gradient may have allowed the
explanation of a higher amount of variance in species
composition turnover.
Implications for conservation on tropical altitudinal
gradients
This is one of the first studies to determine the relative roles
of niche and dispersal in determining forest compositional
turnover on tropical mountains, which has traditionally
been interpreted as a result of niche assembly. We thus
contribute to a more comprehensive understanding of
spatial patterns of tree species diversity (Lomolino 2001,
Malhi et al. 2011). This is particularly important to
conservationand sustainable useof biodiversity inregions
like Central America where the wide range of altitudes and
high topographic complexity gives rise to high landscape
diversity (Enquist 2002), and where vulnerability to
rising temperatures and changing rainfall patterns is high
(Imbach et al. 2012).
The finding that geographical position may greatly
influence forest composition on a tropical altitudinal
gradient is important for several reasons. First, if species
distributions determined in a landscape relatively free of
humandisturbance – asdetected by oursampling protocol
(Sesnie et al. 2009) – partly reflect dispersal limitation,
then species adaptation to climate change taking place
over a few decades (Imbach et al. 2012) could be more
strongly limited by their dispersal capacities (Pearson &
Dawson 2005) than by their capacity to adapt to new
environmental conditions. This point emphasizes once
more the need to take into account the connectivity and
size of forest patches when protecting plant species from
extinction (Duque et al. 2009, Pearson & Dawson 2005).
On the other hand, if environmental tolerances of species
are wider than their current distributions indicate, then
their capacity to tolerate change in situ might be greater
than thought. Second, the contingent nature of com-
munities emphasized in neutral theory (Hubbell 2001)
suggests that ecosystem-based conservation planning
continues to be valid as an approach to ‘keeping common
species common’ (Caicco et al. 1995) but that treating
ecosystems as equivalent to species as conservation
objects as proposed by Rodr´
ıguez et al. (2011) is illusory.
Finally, if species distributions partially reflect dispersal
limitation, then the limitations of bioclimatic envelope
models in the prediction of climate change impacts
(Pearson & Dawson 2003) are further emphasized.
ACKNOWLEDGEMENTS
This research was supported by The Nature Conservancy
to perform field work, and Consejo Nacional de Ciencia
y Tecnolog´
ıa (CONACyT), M´
exico, which supported the
first author with a complete scholarship during her
master studies. We especially thank Vicente Herra, Leonel
Coto, Octavio Palacio, Bernardo Hern ´
andez, ´
Alvaro ´
Avila,
Agust´
ın Z ´
u˜
niga and Astrid Pulido for their assistance
during fieldwork; Hugo Brenes for assistance with
database management; Diego Delgado for his assistance
with field work logistics; Nelson Zamora who identified
leaf specimens that allowed us to carry out the analysis;
and Lee Vierling for his insightful comments on the
manuscript.
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478 ADINA CHAIN-GUADARRAMA ET AL.
Appendix 1. Species list. The family and scientific name of all tree and palm species recorded in 37 vegetation plots (0.25 ha)
are given, followed by their altitudinal range and number of plots in which they were present. A single altitude value is given
for those species recorded in one plot only. Species nomenclature conforms to that of the flora of Costa Rica as listed by the
National Institute of Biodiversity (INBio) (http://atta.inbio.ac.cr/).
Family Scientific name Altitude range and median (m asl) Number of plots
Actinidaceae Saurauia yasicae 64–1285, 724 3
Anacardiaceae Anacardium excelsum 528–631, 559 3
Astronium graveolens 554 1
Spondias mombin 156 1
Tapirira guianensis 156–1435, 930 9
Annonaceae Annona montana 765–796, 781 2
Guatteria aeruginosa 64–109, 70.4 2
Guatteria aff. recurvisepala 194–456, 388 3
Guatteria amplifolia 109–621, 365 2
Guatteria chiriquiensis 659–1435, 1103 3
Guatteria costaricensis 510 1
Xylopia sericophylla 338 1
Xylopia sp. 1316 1
Apocynaceae Aspidosperma megalocarpon 379–463, 410 3
Lacmellea panamensis 429–771, 579 4
Araliaceae Dendropanax arboreus 194–765, 432 5
Dendropanax globosus 1341 1
Dendropanax sp. 1 429–1341, 934 6
Dendropanax sp. 2 64–631, 309 3
Arecaceae Attalea butyracea 554 1
Cryosophila guagara 194–338, 266 2
Euterpe precatoria 379–1435, 787 15
Geonoma sp. 1435 1
Iriartea deltoidea 64–805, 320 10
Socratea exorrhiza 156–822, 559 14
Welfia regia 109–463, 356 9
Bignoniaceae Jacaranda copaia 109 1
Tabebuia chrysantha 771 1
Bombacaceae Ceiba pentandra 64–429, 247 2
Matisia tinamastiana 771–822, 788 2
Pachira aquatica 379 1
Pseudobombax septenatum 510 1
Quararibea sp. 1098 1
Boraginaceae Cordia cymosa 160–429, 339 2
Cordia megalantha 822 1
Burseraceae Bursera simaruba 64 1
Dacryodes sp. 1129–1435, 1245 4
Protium ravenii 621–1056, 911 2
Tetragastris panamensis 160–822, 556 8
Trattinnickia aspera 109 1
Caryocaraceae Caryocar costaricense 338–771, 554.5 2
Cecropiaceae Cecropia insignis 621–1528, 1174.6 9
Cecropia peltata 1435 1
Pourouma bicolor 156–1316, 922.6 6
Chrysobalanaceae Hirtella triandra 379–463, 434 4
Maranthes panamensis 429–621, 525 2
Clusiaceae Calophyllum brasiliense 181–1341, 981 11
Calophyllum longifolium 388–463, 419 3
Chrysochlamys allenii 1341–1528, 1435 2
Dystovomita paniculata 1341–1528, 1445 2
Garcinia intermedia 1010 1
Marila laxiflora 338–463, 432 2
Symphonia globulifera 160–1435, 781 11
Tovomita longifolia 64–429, 229 3
Combretaceae Terminalia amazonia 765–1010, 860 3
Terminalia bucidoides 160–659, 410 2
Dichapetalaceae Stephanopodium costaricense 156–621, 389 2
Elaeocarpaceae Sloanea longipes 1134 1
Forest beta diversity on an altitudinal gradient 479
Appendix 1. Continued
Family Scientific name Altitude range and median (m asl) Number of plots
Euphorbiaceae Alchornea glandulosa 1098–1435, 1206 8
Alchornea latifolia 554–1273, 896 6
Drypetes standleyi 771–1285, 984 4
Hyeronima alchorneoides 109–1528, 723 5
Hyeronima oblonga 429–1528, 1026 5
Mabea occidentalis 338–388, 363 2
Richeria obovata 463 1
Sapium glandulosum 109–1056, 740 2
Fabaceae Acosmium panamense 510 1
Dialium guianense 181 1
Dussia aff. macroprophyllata 1528 1
Dussia macroprophyllata 194–338, 266 2
Dussia sp. 64–1214, 599 5
Inga sp. 109–429, 222 4
Inga cotobrusensis 1262–1273, 1268 2
Inga densiflora 765 1
Inga golfodulcencis 338 1
Inga jinicuil 160 1
Inga nobilis 631–822, 766 3
Inga oerstediana 1528 1
Inga pezizifera 109–1316, 662 4
Inga punctata 621–1010, 812 3
Inga sertulifera 156 1
Inga thibaudiana 109–631, 299 3
Inga vera 771 1
Lecointea amazonica 160 1
Lonchocarpus heptaphyllus 1341 1
Machaerium biovulatum 765 1
Macrolobium colombianum 156–379, 198 4
Peltogyne purpurea 160–463, 295 3
Platymiscium aff. curuense 621 1
Platymiscium sp. 510 1
Pseudopiptadenia suaveolens 379 1
Pterocarpus rohrii 528 1
Pterocarpus sp. 631–822, 712 3
Schizolobium parahibum 631–822, 720 3
Tachigali versicolor 463–805, 721 3
Vatairea sp. 1 109 1
Fagaceae Quercus sp. 1098–1316, 1215 6
Flacourtiaceae Casearia arborea 1316 1
Hasseltia guatemalensis 338–1341, 1121 5
Lindackeria laurina 554 1
Macrohasseltia macroterantha 1316 1
Pleuranthodendron lindenii 64–1056, 395 2
Hernandiaceae Hernandia didymantha 64–1285, 675 2
Hernandia stenura 1341 1
Hippocastanaceae Billia colombiana 64 1
Billia rosea 1285–1316, 1295 2
Humiriaceae Humiriastrum diguense 160–805, 680 5
Juglandaceae Oreomunnea pterocarpa 1098 1
Lauraceae Beilschmiedia sp. 1 1098 1
Beilschmiedia sp. 2 510–1435, 846 8
Beilschmiedia tovarensis 1129–1435, 1327 7
Caryodaphnopsis burgeri 156 1
Cinnamomum tonduzii 1226 1
Cinnamomum triplinerve 765–1273, 1151 5
Cinnamomun aff. tonduzii 1285 1
Lauraceae sp. 1 1435 1
Lauraceae sp. 2 1226 1
Licaria sp. 1 771 1
Licaria sp. 2 528–1285, 1041 4
480 ADINA CHAIN-GUADARRAMA ET AL.
Appendix 1. Continued
Family Scientific name Altitude range and median (m asl) Number of plots
Licaria sp. 3 1098–1226, 1151 3
Nectandra umbrosa 554–1285, 835 3
Ocotea aff. praetermissa 1214–1285, 1250 2
Ocotea insularis 1129 1
Ocotea oblonga 554–1010, 782 2
Ocotea pullifolia 1010 1
Ocotea sp. 1 1226–1528, 1427 2
Ocotea sp. 2 1129–1435, 1307 4
Ocotea sp. 3 1010 1
Ocotea stenoneura 1134–1214, 1187 2
Persea americana 109 1
Persea rigens 1134 1
Pleurothyrium sp. 1 1098 1
Rhodostemonodaphne kunthiana 1056–1226, 1113 2
Lecythidaceae Couratari guianensis 456–463, 460 2
Grias cauliflora 64–160, 134 3
Lecythis mesophylla 156–160, 158 2
Magnoliaceae Talauma gloriensis 160 1
Malpigiaceae Bunchosia sp. 1 631 1
Bunchosia sp. 2 1010 1
Malvaceae Hampea appendiculata 1226 1
Melastomataceae Graffenrieda galeottii 822–1098, 960 2
Miconia multispicata 194–765, 575 2
Miconia sp. 1 1341 1
Miconia tonduzii 1316 1
Mouriri gleasoniana 338 1
Meliaceae Carapa guianensis 181–463, 345 6
Guarea bullata 194 1
Guarea glabra 194–771, 483 2
Guarea grandifolia 156–1341, 1069 3
Guarea kunthiana 1134 1
Guarea microcarpa 771 1
Guarea rhopalocarpa 64–822, 443 2
Trichilia martiana 1056–1098, 1077 2
Trichilia pallida 510–765, 680 2
Trichilia pittieri 1056 1
Moraceae Batocarpus costaricensis 631–1341, 834 2
Brosimum costaricanum 194–554, 499 4
Brosimum guianense 156–1056, 590 4
Brosimum lactescens 156–621, 399 7
Brosimum utile 109–1341, 468 13
Castilla elastica 64–338, 155 3
Castilla tunu 528–822, 656 3
Ficus sp. 2 429–1528, 832 4
Ficus sp. 1 1226 1
Ficus tonduzii 64–1226, 832 5
Ficus velutina 1435 1
Maquira guianensis subsp. costaricana 160 1
Naucleopsis naga 160–659, 410 2
Pseudolmedia mollis 822–1435, 1195 9
Pseudolmedia spuria 510 1
Sorocea pubivena 822 1
Myristicaceae Otoba novogranatensis 160–1285, 983 11
Virola guatemalensis 429–1134, 605 2
Virola koschnyi 64–1273, 648 14
Virola sebifera 181–1010, 545 6
Myrtaceae Byrsonima arthropoda 338 1
Eugenia aff. aeruginea 1134 1
Forest beta diversity on an altitudinal gradient 481
Appendix 1. Continued
Family Scientific name Altitude range and median (m asl) Number of plots
Olacaceae Chaunochiton kappleri 338–554, 467 6
Minquartia guianensis 194–621, 408 2
Polygonaceae Coccoloba mollis 621 1
Coccoloba padiformis 1134 1
Proteaceae Roupala montana 156 1
Rhizophoraceae Cassipourea elliptica 379 1
Rosaceae Licania hypoleuca 181–1316, 971 3
Licania sparsipilis 379 1
Prunus brachybotrya 621–1285, 1087 4
Rubiaceae Chimarrhis parviflora 109–659, 396 6
Elaeagia auriculata 1056–1528, 1256 4
Elaeagia myriantha 1056 1
Genipa americana 621 1
Ladenbergia heterophylla 1129 1
Rubiaceae sp. 528–631, 562 2
Rutaceae Zanthoxylum ekmanii 554 1
Zanthoxylum riedelianum 1010 1
Sabiaceae Meliosma grandiflora 822–1129, 1032 4
Sapindaceae Allophylus gentryi 822 1
Cupania sp. 771 1
Matayba oppositifolia 456–463, 460 2
Sapotaceae Elaeoluma glabrescens 388–456, 422 2
Micropholis melinoniana 1316 1
Pouteria congestifolia 1528 1
Pouteria fossicola 64 1
Pouteria laevigata 160 1
Pouteria reticulata 388–1056, 758 6
Pouteria sp. 1 463–771, 617 2
Pouteria torta 156–659, 408 2
Pradosia atroviolacea 109 1
Sarcaulus brasiliensis 510–1435, 1062 4
Simaroubaceae Simarouba amara 109–1262, 611 6
Sterculiaceae Sterculia recordiana 379–659, 553 3
Theobroma angustifolium 765 1
Styracaceae Styrax argenteus 822 1
Symplocaceae Symplocos austin-smithii 379–1056, 718 2
Symplocos sp. 338 1
Theaceae Gordonia brandegeei 1273 1
Gordonia fruticosa 1214–1528, 1353 5
Ticodendraceae Ticodendron incognitum 1134–1341, 1203 2
Tiliaceae Apeiba membranacea 194–429, 320 3
Goethalsia meiantha 64–765, 240 4
Heliocarpus appendiculatus 64–1435, 1080 6
Mortoniodendron abelianum 1285 1
Trichospermum grewiifolium 194–1010, 466 2
Ulmaceae Ampelocera macrocarpa 338 1
Trema micrantha 1226 1
Unknown Unknown 1 181 1
Unknown 2 181 1
Unknown 3 181–659, 420 2
Unknown 4 194 1
Unknown 5 1285 1
Unknown 6 1010 1
Unknown 7 379 1
Vochysiaceae Qualea polychroma 109–388, 316 3
Vochysia allenii 160–456, 352 5
Vochysia ferruginea 156–1273, 671 14
Vochysia guatemalensis 765 1
Vochysia megalophylla 379–463, 442 2
