Published online 20 February 2004
Pattern and process in Amazon tree turnover,
1976–2001
O. L. Phillips1*, T. R. Baker1,2, L. Arroyo3,4, N. Higuchi5, T. J. Killeen3,6,
W. F. Laurance7,8, S. L. Lewis1,9, J. Lloyd2, Y. Malhi9, A. Monteagudo10,11,
D. A. Neill4, P. Nu´n˜ez Vargas10, J. N. M. Silva12,13, J. Terborgh14,
R. Va´squez Martı´nez11, M. Alexiades15, S. Almeida16, S. Brown17, J. Chave18,
J. A. Comiskey19, C. I. Czimczik2, A. Di Fiore20, T. Erwin19, C. Kuebler6,
S. G. Laurance7,8, H. E. M. Nascimento7,8, J. Olivier18, W. Palacios21,
S. Patin˜o2,22, N. C. A. Pitman15, C. A. Quesada2,23, M. Saldias3,
A. Torres Lezama24 and B. Vinceti25
1Earth and Biosphere Institute, School of Geography, University of Leeds, Leeds LS2 9JT, UK
2Max-Planck-Institut fu¨r Biogeochemie, Postfach 100164, 07701 Jena, Germany
3Museo Noel Kempff Mercado, Santa Cruz, Bolivia
4Missouri Botanical Garden, St Louis, MO 63166-0299, USA
5Instituto National de Pesquisas Amazoˆnicas, Alameda Cosme Ferreira 1756-Aleixo, CEP 69083-000, Manaus, Brazil
6Center for Applied Biodiversity Science, Conservation International, Washington, DC 20036, USA
7Smithsonian Tropical Research Institute, Balboa, Panama
8Biological Dynamics of Forest Fragments Program, Smithsonian Institution/INPA CP 478, Manaus,
AM 69022-970, Brazil
9School of Geosciences, University of Edinburgh, Drummond Street, Edinburgh EH8 9XP, UK
10Herbario Vargas, Universidad Nacional San Antonio Abad del Cusco, Cusco, Peru
11Proyecto Flora del Peru´, Jardin Botanico de Missouri, Oxapampa, Peru
12CIFOR, Tapajos, Para, Brazil
13EMBRAPA Amazonia Oriental, Belem, Para, Brazil
14Duke University School of the Environment, Center for Tropical Conservation, 3705-C Erwin Road, Durham,
NC 27705, USA
15New York Botanical Garden, Bronx River Parkway at Fordham Road, NY 10458, USA
16Museu Paraense Emilio Goeldi, Avenida Magalhaes Barata 376, Belem, Para 66040, Brazil
17Winrock International, 1621 North Kent Street, Suite 1200, Arlington, VA 22209, USA
18Laboratoire Evolution et Diversite´ Biologique, CNRS/UPS Toulouse, Baˆtiment IVR3, Campus Universite´ Paul Sabatier
Toulouse III, 118 route de Narbonne, 31062 Toulouse cedex 4, France
19Smithsonian Institution, Washington, DC 20013-7012, USA
20Department of Anthropology, New York University, New York, NY 10003, USA
21Fundacion Jatun Sacha, Quito, Ecuador, Ecuador-002
22Alexander von Humboldt Biological Research Institute, Bogota´, Colombia
23Departamento de Ecologı´a, Universidade de Brasilia, CEP 70919-970, Brazil
24INDEFOR, Universidad de Los Andes, Me´rida 5101, Venezuela
25International Plant Genetic Resources Institute, Via dei Tre Denari 472/a, 00057 Maccarese (Fiumicino), Rome, Italy
Previous work has shown that tree turnover, tree biomass and large liana densities have increased in
mature tropical forest plots in the late twentieth century. These results point to a concerted shift in forest
ecological processes that may already be having significant impacts on terrestrial carbon stocks, fluxes and
biodiversity. However, the findings have proved controversial, partly because a rather limited number of
permanent plots have been monitored for rather short periods. The aim of this paper is to characterize
regional-scale patterns of ‘tree turnover’ (the rate with which trees die and recruit into a population) by
using improved datasets now available for Amazonia that span the past 25 years. Specifically, we assess
whether concerted changes in turnover are occurring, and if so whether they are general throughout the
Amazon or restricted to one region or environmental zone. In addition, we ask whether they are driven
* Author for correspondence (o.phillips@geog.leeds.ac.uk).
One contribution of 17 to a Theme Issue ‘Tropical forests and global atmospheric change’.
Phil. Trans. R. Soc. Lond. B (2004) 359, 381–407 381  2004 The Royal Society
DOI 10.1098/rstb.2003.1438

382 O. L. Phillips and others Patterns and process in Amazon tree turnover
by changes in recruitment, mortality or both. We find that: (i) trees 10 cm or more in diameter recruit
and die twice as fast on the richer soils of southern and western Amazonia than on the poorer soils of
eastern and central Amazonia; (ii) turnover rates have increased throughout Amazonia over the past two
decades; (iii) mortality and recruitment rates have both increased significantly in every region and environ-
mental zone, with the exception of mortality in eastern Amazonia; (iv) recruitment rates have consistently
exceeded mortality rates; (v) absolute increases in recruitment and mortality rates are greatest in western
Amazonian sites; and (vi) mortality appears to be lagging recruitment at regional scales. These spatial
patterns and temporal trends are not caused by obvious artefacts in the data or the analyses. The trends
cannot be directly driven by a mortality driver (such as increased drought or fragmentation-related death)
because the biomass in these forests has simultaneously increased. Our findings therefore indicate that
long-acting and widespread environmental changes are stimulating the growth and productivity of
Amazon forests.
Keywords: recruitment; mortality; tree turnover; dynamics; Amazonia; forest
1. INTRODUCTION
Ecosystems worldwide are changing as a result of myriad
anthropogenic processes. Some processes are physically
obvious (e.g. deforestation), others may be less so but also
affect biodiversity (e.g. fragmentation, hunting). Atmos-
pheric changes such as increasing CO2 concentrations,
increasing temperatures and altered rates of nitrogen
deposition are changing the environment of even remote
regions. Anthropogenic atmospheric change will certainly
become more significant through the century, as atmos-
pheric CO2 concentrations will reach values unpre-
cedented for at least 20 or even 60 million years (Retallack
2001; Royer et al. 2001). Nitrogen-deposition rates and
climates are predicted to move far beyond Quaternary
envelopes (Prentice et al. 2001; Galloway & Cowling
2002).
Although we are able to measure most of these physical
and chemical drivers with reasonable accuracy and pre-
cision, quantifying possible ecological responses to atmos-
pheric change is an extremely difficult task. The task is
particularly urgent in the tropical forests, as a high pro-
portion of the Earth’s biodiversity, plant carbon stocks and
forest productivity is centred within this biome (Malhi &
Grace 2000). The principal means of monitoring ecologi-
cal processes within mature forests is with permanent sam-
ple plots, but the network of assessment and monitoring
sites has traditionally been sparse, spatially aggregated and
poorly integrated at regional scales. Over the past decade
we have sought to overcome these limitations by
developing collaborative networks of researchers: recog-
nizing that by pooling local efforts and small-scale datasets
we can start to answer large-scale questions. In particular
the Amazon Forest Inventory Network (RAINFOR, see
http://www.geog.leeds.ac.uk/projects/rainfor/), which was
established in 2000, seeks to document and understand
patterns and changes in mature Amazon forests on both
spatial and temporal scales (Malhi et al. 2002).
Earlier large-scale analyses have suggested that signifi-
cant changes occurred in the structure and function of
mature tropical forests by the close of the twentieth cen-
tury. For example, turnover rates of trees in mature trop-
ical forest plots increased throughout the 1980s and early
1990s (Phillips & Gentry 1994; Phillips 1996). This trend
was demonstrated separately for both the neotropics and
the palaeotropics, with the changes appearing to be gener-
ally immune to concerns such as the effect of individual
Phil. Trans. R. Soc. Lond. B (2004)
ENSO cycles (Phillips 1995; cf. Sheil 1995a), bias
towards high-biomass ‘majestic forest’ when plots are
established (Condit 1997; Phillips et al. 1997), damage
caused by botanical collecting (Phillips et al. 1998a; cf.
Sheil 1995b) and census-interval artefacts (Lewis et al.
2004c; cf. Sheil 1995a). In a set of forest plots in Ama-
zonia that largely overlaps with that used in the neotrop-
ical turnover dataset, we have also shown that the
structure and composition of mature non-fragmented for-
ests are changing, with an increase in the biomass of trees
(Phillips et al. 1998b; Baker et al. 2004b; but see Clark
2002; Phillips et al. 2002a) and in the density and relative
dominance of large lianas (Phillips et al. 2002b). Taken
together, these results imply that changes in structure, com-
position and dynamics are common manifestations
reflecting a profound shift in the overall ecology of tropical
forests. However, to fully test the proposition that ecologi-
cal processes in mature tropical forests are changing sys-
tematically, additional evidence needs to be evaluated
against two sets of criteria.
(i) Are the changes observed so far concerted across
space and time? Are they geographically coincident
(occurring together in the same forest region and
sites), geographically widespread (occurring across
spatial and environmental gradients) and temporally
robust (occurring over protracted periods of time
and relatively insensitive to short-term climatic
fluctuations)?
(ii) Can the phenomena be explained in terms of under-
lying ecological processes, such as growth, mortality
and recruitment? Specifically, is the increase in turn-
over driven by changes in recruitment or mortality,
or both? Is the increase in above-ground biomass
driven by greater basal area growth or reduced basal
area death? Are these ecological processes consistent
with one another and with possible mechanistic driv-
ers?
In this paper, we provide a much fuller description of
the patterns of tree turnover than has been possible so far,
concentrating on Amazonia which comprises more than
half the world’s remaining area of humid tropical forest
and where changes in tree and liana biomass have pre-
viously been demonstrated. We explore aspects of the two
sets of criteria described above, and show results before
and after accounting for potentially important artefactual
sources of error. Companion papers (Lewis et al. 2004a,b)
develop a conceptual framework that links plausible

Patterns and process in Amazon tree turnover O. L. Phillips and others 383
physical and chemical mechanistic drivers to predicted
changes and present tests for the phenomena that comp-
lement the approach taken here. We define ‘turnover’ as
the rate with which trees move through a population (the
flux) in relation to the number of trees in the population
(the pool), and estimate this flux by the mean rate with
which they recruit and die.
In this paper our specific aims are to determine the
following.
(i) The extent to which turnover rates have changed (or
not) throughout the Amazon Basin. (Note that the
turnover increase has so far only been shown for the
neotropics and palaeotropics as a whole).
(ii) If there are consistent patterns in the changes in
turnover rates across the different climatic, edaphic
and geographical regions within Amazonia.
(Amazon forests vary greatly, so it is important to
know if the patterns of change vary too.)
(iii) Whether these changes are driven by recruitment
rate changes, mortality rate changes or both.
(Turnover changes in neotropics and palaeotropics
have only been shown so far in aggregate, and have
not been deconstructed into component processes.)
Addressing these questions first requires careful con-
sideration of possible sources of error, and correcting for
these where possible. Potential sources of error stem from
the differing census intervals with which plots are moni-
tored, the timing of censuses, the possible tendency of for-
esters and ecologists to select good-looking, high-biomass,
mature-phase patches for plots (‘majestic forest’), and
changes through time in the spatial and environmental
distribution of available datasets (‘site-switching’). In § 2
we describe how we have attempted to address these prob-
lems, but first we briefly review these concerns.
Turnover rates are sensitive to the length of interval
over which they are measured and the actual timing of
censuses in at least four ways. First, individual stem death
and recruitment are discrete events. This means that over
progressively shorter intervals, estimates of rates depart
disproportionately from long-term trends as a function of
when census dates fall in relation to individual tree deaths
and the local forest gap-phase cycles: short intervals there-
fore introduce more random noise (Hall et al. 1998).
Second, detecting new recruits and deaths is not always
straightforward. Over shorter intervals the error associated
with determining recruitment increases because a larger
proportion of ingrowing stems are close to the minimum
size threshold of 10 cm, which increases the relative
impact of stem hydration fluctuations or measurement
error on recruitment estimates (Sheil 1995b). Similarly,
measurement errors of mortality rates may increase
because proportionally greater fractions of apparently
dead trees will be ‘barely alive’ or ‘just dead’. However,
over longer time intervals the precision of recruitment and
mortality estimates declines as more trees will have
recruited and died undetected in the interval. Third, sea-
sonality and regional and global-scale climate fluctuations
such as ENSO events generate intra-annual and supra-
annual fluctuations in stem hydration (e.g. Baker et al.
2002), growth rates and mortality probabilities (e.g.
Nakagawa et al. 2000), so the timing of the census can
affect the rates measured in each interval. Finally, the
cohort of stems dying over short intervals is represented
Phil. Trans. R. Soc. Lond. B (2004)
disproportionately by intrinsically short-lived trees, so
shorter census intervals are biased to record higher turn-
over rates than longer intervals. Sheil & May (1996) pro-
vide a theoretical discussion of this effect. Lewis et al.
(2004c) developed an empirical quantification of its sig-
nificance but found that it probably cannot account for
published findings of increased turnover.
Additional methodological issues that have been sug-
gested to account for increased turnover include possible
biases in the way that plots are selected on local, regional
and global scales. Locally, some sites could be affected by
a ‘majestic forest’ artefact, if ecologists preferentially select
mature-stage forest when establishing plots (Phillips &
Sheil 1997; Phillips et al. 1997, 2002a). Such plots would
subsequently undergo locally accelerated dynamics as
large trees die, killing smaller trees and improving the light
environment for new recruits. On much larger scales,
regionally and globally aggregated turnover results could
be biased by unequal sampling of forest types across time
(‘site-switching’). In large multi-site datasets, site-switch-
ing is inevitable because plots are monitored at different
times for different lengths in different environments and
different parts of the world. For example, in the dataset
used in Phillips (1996) the average monitoring date for
palaeotropical forest plots was 1971, whereas for neotrop-
ical plots it was 1982 (Lewis et al. 2004c). If the nature
of the site-switching is such that inherently more dynamic
forests have been monitored more recently than less
dynamic forests, then simply correlating turnover rates
with time may lead to type I error: the erroneous con-
clusion that forests as a whole are becoming more dynamic
when in fact they are not (Condit 1997).
A final concern that has been raised is that the stochas-
tic nature of forest dynamics makes it very difficult to use
small plots to detect signals of change (e.g. Hall et al.
1998). This is undoubtedly true. However, we have shown
before that our approach of looking for aggregate effects
across many plots can overcome this difficulty (e.g. Phil-
lips & Gentry 1994; Phillips 1996). We wish to emphasize
that the null hypothesis being tested here is not that ‘tree
turnover rates have not increased within a specific, individ-
ual site’. Rather, it is that ‘tree turnover rates have not
systematically increased across all sites in a region’.
2. METHODS
(a) Site selection
The region considered is the Amazon river basin and contigu-
ous forested areas, including all mature forest except for that
which has experienced obvious anthropogenic disturbances
(logging, fragmentation and fires) and excluding small forest
patches in forest–savannah mosaic landscapes. Data were
obtained from published sources where available, but most data
analysed are from unpublished permanent monitoring plots
maintained by the authors, across sites in Bolivia, Brazil,
Ecuador, French Guiana, Peru and Venezuela. Together, these
forests constitute a substantial proportion of the RAINFOR
Amazon forest inventory network (Malhi et al. 2002). The
criteria used for selecting appropriate tree turnover data include
a minimum initial population of 200 or more trees, 10 cm or
greater diameter, a minimum area of 0.25 hectares (ha, where
1 ha = 104 m2) and a minimum monitoring period of 2 years.
Most reported data are much more substantial than these values

384 O. L. Phillips and others Patterns and process in Amazon tree turnover
might suggest: among the plot data analysed, the mean (and
median) values of the initial population are 954 (572) trees, the
area monitored averages 1.7 (1.0) ha and the monitoring period
averages 10.1 (9.6) years. These plots are mostly replicates from
within different landscapes across Amazonia, with each plot sep-
arated from others in the same landscape by between a few hun-
dred metres and a few kilometres.
(b) Turnover rate calculations
Annual mortality and recruitment rates were separately esti-
mated using standard procedures that use logarithmic models
which assume a constant probability of mortality and recruit-
ment through each inventory period (Swaine et al. 1987; Phillips
et al. 1994). To reduce noise, turnover rates for each period were
represented by the mean of recruitment and mortality (91 sites),
or as mortality rates alone when recruitment data were not avail-
able (four sites) (table 1).
(c) Analytical approach
Change in a rate process can be evaluated in many ways,
depending on the exact hypothesis being tested and the quality
of the data (Phillips 1996). Some sites have only one measure-
ment interval, whereas others have turnover rates reported for
multiple intervals. To use the greatest information content poss-
ible, we have used several different approaches here and in a
companion paper (Lewis et al. 2004a).
The core approach used in this paper involves calculating
mortality and recruitment rates for each site for each year in
which it was monitored, and plotting these rates as a function of
calendar year. (In the companion paper we focus on evaluating
changes within plots.) We test for change by comparing meas-
ured rates in the last year in which at least 10 sites were moni-
tored with rates in the first year in which at least 10 sites were
monitored. With our current dataset this typically allows com-
parisons across two decades from the early 1980s to 2001.
The method described here has the advantages of using all
the available turnover data and of being able to show graphically
the statistical range of site values within each calendar year and
across all calendar years. However, a concern is that the results
may be skewed by using short or varying census intervals
through time as it is not possible to coordinate censuses at the
Amazonian scale, nor is it even possible to select censuses retro-
spectively so that they are simultaneous and equally frequent at
all sites. We take a pragmatic approach to minimize the impact
of this concern. Thus, all rates are calculated for each site over
intervals of as close to 5 years as practical, so that short intervals
are collapsed together where possible (see electronic Appendix
A). Adjacent intervals less than 5 years are combined when the
difference between the combined period and 5 years is less than
the summed difference between each of the constituent intervals
and 5 years. To account for any residual census interval effect, we
also present key results with and without an empirical correction
for the census interval effect derived from 10 long-term sites from
Latin America, Africa, Asia and Australia (Lewis et al. 2004c).
We also needed to identify those plots potentially affected by
a ‘majestic forest’ bias, as a gradual or sudden breakdown of
mature phase forest will lead to locally accelerated dynamics.
We can rule out the possibility that a majestic forest effect could
be artificially accelerating dynamics in most plots, based on
either the sample unit shape and size, or the site selection pro-
cedures used, or the fact that the stand has gained basal area
through the monitoring period as their rate processes are
unlikely to be driven by locally accelerated dynamics resulting
Phil. Trans. R. Soc. Lond. B (2004)
from death of large trees (table 2). The remaining seven plots
potentially most susceptible to majestic forest bias were
excluded from these analyses. Out of these, four (BDF-04,
BDF-08, CRP-01 and JAS-02) have more than one interval, and
the impact of leaving these forests out is evaluated in the results.
A further concern with our analytical approach is that a calen-
dar year signal confounds within-site change with among-site
change, so aggregated results could be influenced by biases that
could arise through unequal sampling of forest types across time
(‘site-switching’). Therefore we also present results in a way that
eliminates site-switching, to show only the aggregate of within-
site changes. This is achieved by ‘stretching’ all multi-interval
data backwards and forwards. We do this by applying the rate
actually recorded in the first interval rate for each year before
the first census back to 1976 (for each site initiated after 1976),
and applying the rate actually recorded in the last interval for-
wards to 2001 (for each site last censused before 2001). This
should be a conservative procedure with respect to the null
hypothesis because we are assuming no change in rates for all
years in which a site was not monitored. Most plots have been
monitored for less than 25 years and so stretching always flattens
the average gradient of any trend in rates. The main analyses—
correcting for site-switching, census-interval and majestic-forest
effects—are shown graphically and in table 3. Results using the
raw uncorrected data are shown principally in tabular form. To
explore the sensitivity of the main results to the exclusion of the
four potential majestic forest sites, a supplementary set of
census-interval and site-switching corrected analyses was run
using these data, and results compared with the main analyses
that corrected for all possible effects.
To be able to test whether patterns are widespread or simply
driven by change in one region or another, we arbitrarily divided
Amazonia into two roughly equal areas with as equal sample
sizes as possible: western and southern Amazonia, which we call
‘west and south’, and eastern and central Amazonia, which we
call ‘east and central’ (figure 1). Most east and central Amazon
forests are on the actively weathering Guyanan or Brazilian
shield or associated Cretaceous and Tertiary planation surfaces,
whereas most west and south Amazon forests are located on
Quaternary or Holocene Andean sediment (Irion 1978;
Sombroek 1984; Richter & Babbar 1991; but see also Lips &
Duivenvoorden 1996). Our geographical division is also consist-
ent with what we know about the floristic make-up of Amazon
forests, lying roughly perpendicular to the main southwest–
northeast gradient in composition (Terborgh & Andresen 1998).
In separate disaggregations we divided Amazonia in a climatic
sense (‘aseasonal’ versus ‘seasonal’, using the criterion of one
month or more receiving less than 100 mm rain to define
seasonality), and in an edaphic sense (poor soil versus richer
soils, with oxisols, oligotrophic histosols, and spodosols and
other white sands defined as ‘poor’, and alfisols, eutrophic histo-
sols, ultisols, clay-rich entisols, and alluvial and basaltic
inceptisols defined as ‘richer’). Climate data come from local
meteorological stations where possible, and otherwise from a
twentieth century climatology developed to characterize baseline
climates for the International Panel on Climate Change (see
http://ipcc-ddc.cru.uea.ac.uk). Soil classifications come from
published profiles where possible, and otherwise are based on our
own preliminary analyses (C. A. Quesada, C. I. Czimczik and J.
Lloyd, unpublished data). These categories represent an advance
on previous approaches that lumped the neotropics into a single
category (e.g. Phillips 1996) and allow us to maintain reasonable
sample sizes in each through the late twentieth century.

Patterns and process in Amazon tree turnover O. L. Phillips and others 385
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pf
f
H
u
an
ch
ac
a
D
os
,
H
C
C
-2
2
B
ol
iv
ia
14
°
35


S
60
°
44


W
70
0–
80
0
W

S
te
rr
a
15
00
ox
is
ol
0
1
1
50
0
20
L
.A
.
M
u
se
o
4.
89
N
/A
N
/A
pl
ot
2
fi
rm
e
N
oe
l
K
em
pf
f
L
as
L
on
d
ra
s,
pl
ot
1
L
S
L
-0
1
B
ol
iv
ia
14
°
24


61
°
09


W
17
0
W

S
se
as
on
al
ly
15
00
u
lt
is
ol
1
1
1
50
0
20
L
.A
.
M
u
se
o
4.
95
N
/A
N
/A
fl
oo
d
ed
N
oe
l
K
em
pf
f
L
as
L
on
d
ra
s,
pl
ot
2
L
S
L
-0
2
B
ol
iv
ia
14
°
24


61
°
09


W
17
0
W

S
se
as
on
al
ly
15
00
u
lt
is
ol
1
1
1
50
0
20
L
.A
.
M
u
se
o
4.
95
N
/A
N
/A
fl
oo
d
ed
N
oe
l
K
em
pf
f
L
os
F
ie
rr
os
B
os
qu
e
L
F
B
-0
1
B
ol
iv
ia
14
°
37


S
60
o
52


W
22
5
W

S
te
rr
a
15
00
ox
is
ol
0
1
1
50
0
20
T
.K
.
M
u
se
o
7.
78
N
/A
N
/A
I
fi
rm
e
N
oe
l
K
em
pf
f
L
os
F
ie
rr
os
B
os
qu
e
L
F
B
-0
2
B
ol
iv
ia
14
°
33


S
60
°
56


W
22
5
W

S
te
rr
a
15
00
ox
is
ol
0
1
1
50
0
20
T
.K
.
M
u
se
o
7.
76
N
/A
N
/A
II
fi
rm
e
N
oe
l
K
em
pf
f
B
D
F
F
P
,
11
01
B
D
F
-0
3
B
ra
zi
l
2°
24


S
59
°
54


W
75
E

C
te
rr
a
22
00
ox
is
ol
0
1
1
10
0
10
0
W
.L
.
S
m
it
hs
on
ia
n
18
.1
7
10
.2
4
7.
92
G
av
ia
o
fi
rm
e
In
st
it
u
ti
on
B
D
F
F
P
,
11
02
B
D
F
-0
4
B
ra
zi
l
2°
24


S
59
°
54


W
75
E

C
te
rr
a
22
00
ox
is
ol
0
1
1
10
0
10
0
W
.L
.
S
m
it
hs
on
ia
n
18
.1
7
10
.2
5
7.
92
G
av
ia
o
fi
rm
e
In
st
it
u
ti
on
B
D
F
F
P
,
11
03
B
D
F
-0
5
B
ra
zi
l
2°
24


S
59
°
54


W
75
E

C
te
rr
a
22
00
ox
is
ol
0
1
1
10
0
10
0
W
.L
.
S
m
it
hs
on
ia
n
18
.0
8
10
.4
2
7.
67
G
av
ia
o
fi
rm
e
In
st
it
u
ti
on
B
D
F
F
P
,
11
13
B
D
F
-0
9
B
ra
zi
l
2°
24


S
59
°
54


W
75
E

C
te
rr
a
22
00
ox
is
ol
0
1
1
10
0
10
0
W
.L
.
S
m
it
hs
on
ia
n
10
.2
5
5.
08
5.
17
F
lo
re
st
al
fi
rm
e
In
st
it
u
ti
on
B
D
F
F
P
,
12
01
B
D
F
-0
6
B
ra
zi
l
2°
24


S
59
°
54


W
75
E

C
te
rr
a
22
00
ox
is
ol
0
1
3
10
0
10
0
W
.L
.
S
m
it
hs
on
ia
n
18
.0
0
10
.0
8
7.
92
G
av
ia
o
fi
rm
e
In
st
it
u
ti
on
B
D
F
F
P
,
13
01
B
D
F
-1
0
B
ra
zi
l
2°
24


S
59
°
54


W
75
E

C
te
rr
a
22
00
ox
is
ol
0
1
1
10
0
10
0
W
.L
.
S
m
it
hs
on
ia
n
13
.6
7
3.
75
9.
92
F
lo
re
st
al
1
fi
rm
e
In
st
it
u
ti
on
B
D
F
F
P
,
13
01
B
D
F
-1
2
B
ra
zi
l
2°
24


S
59
°
54


W
75
E

C
te
rr
a
22
00
ox
is
ol
0
1
2
10
0
10
0
W
.L
.
S
m
it
hs
on
ia
n
13
.6
7
3.
75
9.
92
F
lo
re
st
al
3
fi
rm
e
In
st
it
u
ti
on
B
D
F
F
P
,
23
03
F
az
.
B
D
F
-0
1
B
ra
zi
l
2°
24


S
59
°
54


W
75
E

C
te
rr
a
22
00
ox
is
ol
0
1
2
10
0
10
0
W
.L
.
S
m
it
hs
on
ia
n
12
.4
2
5.
33
7.
09
D
im
on
a
4–
6
fi
rm
e
In
st
it
u
ti
on
(C
on
ti
nu
ed
.)
Phil. Trans. R. Soc. Lond. B (2004)

Patterns and process in Amazon tree turnover O. L. Phillips and others 387
T
ab
le
1.
(C
on
ti
nu
ed
.)
so
il
se
as
on
al
it
y
(0
=
(0
=
ce
n
su
s
ce
n
su
s
po
or
,
as
ea
so
n
al
,
m
ax
.
pl
ot
m
in
.
pl
ot
m
on
it
or
in
g
in
te
rv
al
in
te
rv
al
si
te
al
ti
tu
d
e
fo
re
st
ra
in
fa
ll
so
il
1
=
1
=
ar
ea
d
im
en
si
on
d
im
en
si
on
pr
in
ci
pa
l
in
st
it
u
ti
on
/
pe
ri
od
1
2
si
te
n
am
e
co
d
e
co
u
n
tr
y
la
ti
tu
d
e
lo
n
gi
tu
d
e
(m
)
re
gi
on
ty
pe
a
(m
m
)
or
d
er
ri
ch
er
)
se
as
on
al
)
(h
a)
(m
)
(m
)
in
ve
st
ig
at
or
pu
bl
ic
at
io
n
(y
ea
rs
)
(y
ea
rs
)
(y
ea
rs
)
B
og
i
1
B
O
G
-0
1
E
cu
ad
or
0°
42


S
76
°
26


W
27
1
W

S
te
rr
a
32
52
in
ce
pt
is
ol
1
0
1
10
00
10
N
.P
.
D
u
ke
5.
83
N
/A
N
/A
fi
rm
e
U
n
iv
er
si
ty
B
og
i
2
B
O
G
-0
2
E
cu
ad
or
0°
42


S
76
°
25


W
27
0
W

S
te
rr
a
32
52
in
ce
pt
is
ol
1
0
11
10
00
10
N
.P
.
D
u
ke
5.
83
N
/A
N
/A
fi
rm
e
U
n
iv
er
si
ty
C
u
ya
be
n
o
C
Y
B
-0
1
E
cu
ad
or
0°
00


S
76
°
12


W
26
5
W

S
32
52
in
ce
pt
is
ol
1
0
1
10
0
10
0
pu
bl
is
he
d
K
or
n
in
g
2.
54
N
/A
N
/A
te
rr
a
&
B
al
sl
ev
fi
rm
e
(1
99
4)
Ja
tu
n
S
ac
ha
2
JA
S
-0
2
E
cu
ad
or
1°
04


S
77
°
36


W
45
0
W

S
te
rr
a
40
13
u
lt
is
ol
/
1
0
1
10
0
10
0
D
.N
.
H
er
ba
ri
o
14
.4
2
6.
92
7.
50
fi
rm
e
in
ce
pt
is
ol
N
ac
io
n
al
Ja
tu
n
S
ac
ha
3
JA
S
-0
3
E
cu
ad
or
1°
04


S
77
°
40


W
45
0
W

S
te
rr
a
40
13
u
lt
is
ol
/
1
0
1
10
0
10
0
D
.N
.
H
er
ba
ri
o
13
.1
7
9.
63
3.
54
fi
rm
e
in
ce
pt
is
ol
N
ac
io
n
al
Ja
tu
n
S
ac
ha
4
JA
S
-0
4
E
cu
ad
or
1°
04


S
77
°
40


W
45
0
W

S
te
rr
a
40
13
u
lt
is
ol
/
1
0
0.
92
10
0
10
0
D
.N
.
H
er
ba
ri
o
11
.5
5
3.
95
7.
60
fi
rm
e
in
ce
pt
is
ol
N
ac
io
n
al
Ja
tu
n
S
ac
ha
5
JA
S
-0
5
E
cu
ad
or
1°
04


S
77
°
40


W
45
0
W

S
te
rr
a
40
13
en
ti
so
l
1
0
1
10
0
10
0
D
.N
.
H
er
ba
ri
o
12
.6
7
5.
08
7.
58
fi
rm
e
N
ac
io
n
al
T
ip
u
ti
n
i
2
T
IP
-0
2
E
cu
ad
or
0°
38


S
76
°
08


W
24
6
W

S
te
rr
a
32
52
in
ce
pt
is
ol
1
0
0.
8
10
0
10
0
N
.P
.
D
u
ke
4.
42
N
/A
N
/A
fi
rm
e
U
n
iv
er
si
ty
T
ip
u
ti
n
i
3
T
IP
-0
3
E
cu
ad
or
0°
38


S
76
°
08


W
24
8
W

S
se
as
on
al
ly
32
52
en
ti
so
l
1
0
1
10
0
10
0
N
.P
.
D
u
ke
4.
00
N
/A
N
/A
fl
oo
d
ed
U
n
iv
er
si
ty
N
ou
ra
gu
es
G
P
N
O
R
-0
2
F
re
n
ch
4°
05


N
52
°
40


W
11
0
E

C
te
rr
a
29
97
ox
is
ol
0
1
10
10
00
10
0
J.
C
h.
,
J.
O
.
C
N
R
S
7.
52
N
/A
N
/A
G
u
ia
n
a
fi
rm
e
N
ou
ra
gu
es
P
P
N
O
R
-0
1
F
re
n
ch
4°
05


N
52
°
40


W
11
0
E

C
te
rr
a
29
97
ox
is
ol
0
1
12
40
0
30
0
J.
C
h.
,
J.
O
.
C
N
R
S
9.
55
N
/A
N
/A
G
u
ia
n
a
fi
rm
e
P
ar
ac
ou
P
A
R
F
re
n
ch
5°
15


N
52
°
50


W
19
E

C
te
rr
a
32
00
ox
is
ol
?
0
1
18
.7
5
25
0
25
0
pu
bl
is
he
d
F
av
ri
ch
on
11
.0
0
N
/A
N
/A
G
u
ia
n
a
fi
rm
e
et
al
.
(1
99
7)
S
ai
nt
E
lie
T
ra
ns
ec
t
1
E
L
I-
01
F
re
n
ch
5°
30


N
53
°
00


W
40
E

C
te
rr
a
31
20
ox
is
ol
?
0
1
0.
78
39
0
20
pu
bl
is
he
d
P
el
is
si
er
&
10
.0
0
N
/A
N
/A
G
u
ia
n
a
fi
rm
e
R
ie
ra
(1
99
3)
S
ai
nt
E
lie
T
ra
ns
ec
t
2
E
L
I-
02
F
re
n
ch
5°
30


N
53
°
00


W
40
E

C
te
rr
a
31
20
ox
is
ol
?
0
1
1
50
0
20
pu
bl
is
he
d
P
el
is
si
er
&
10
.0
0
N
/A
N
/A
G
u
ia
n
a
fi
rm
e
R
ie
ra
(1
99
3)
A
llp
ah
u
ay
o
A
,
A
L
P
-1
1
P
er
u
3°
57


S
73
°
26


W
11
4
W

S
te
rr
a
27
63
u
lt
is
ol
1
0
0.
44
50
0
20
O
.P
.,
R
.V
.
L
ee
d
s,
10
.1
5
5.
25
4.
9
po
or
ly
d
ra
in
ed
fi
rm
e
JB
M
A
llp
ah
u
ay
o
A
,
w
el
l
A
L
P
-1
2
P
er
u
3°
57


S
73
°
26


W
12
5
W

S
te
rr
a
27
63
en
ti
so
l
0
0
0.
4
50
0
20
O
.P
.,
R
.V
.
L
ee
d
s,
10
.1
5
5.
25
4.
9
d
ra
in
ed
fi
rm
e
(p
sa
m
m
en
t)
JB
M
A
llp
ah
u
ay
o
B
,
cl
ay
ey
A
L
P
-2
2
P
er
u
3°
57


S
73
°
26


W
11
4
W

S
te
rr
a
27
63
u
lt
is
ol
1
0
0.
44
50
0
20
O
.P
.,
R
.V
.
L
ee
d
s,
10
.1
6
5.
25
4.
91
fi
rm
e
JB
M
A
llp
ah
u
ay
o
B
,
sa
n
d
y
A
L
P
-2
1
P
er
u
3°
57


S
73
°
26


W
12
5
W

S
te
rr
a
27
63
en
ti
so
l
0
0
0.
48
50
0
20
O
.P
.,
R
.V
.
L
ee
d
s,
10
.1
6
5.
25
4.
91
fi
rm
e
(p
sa
m
m
en
t)
JB
M
A
lt
os
d
e
M
ai
za
l
A
L
M
-0
1
P
er
u
11
°
48


S
71
°
28


W
40
0
W

S
te
rr
a
23
00
u
lt
is
ol
1
1
2
20
0
10
0
J.
T
.,
P
.N
.
D
u
ke
5.
00
N
/A
N
/A
fi
rm
e
U
n
iv
er
si
ty
C
oc
ha
S
al
va
d
or
M
N
U
-0
8
P
er
u
11
°
59


S
71
°
11


W
40
0
W

S
ra
re
ly
23
00
en
ti
so
l
1
1
2
20
0
10
0
J.
T
.,
P
.N
.
D
u
ke
10
.0
7
5.
04
5.
03
M
an
u
fl
oo
d
ed
U
n
iv
er
si
ty
C
u
zc
o
A
m
az
on
ic
o,
C
U
Z
-0
1
P
er
u
12
°
35


S
69
°
09


W
20
0
W

S
te
rr
a
24
17
in
ce
pt
is
ol
1
1
1
50
0
20
O
.P
.,
R
.V
.
L
ee
d
s,
9.
38
5.
23
4.
14
C
U
Z
A
M
1E
fi
rm
e
JB
M
C
u
zc
o
A
m
az
on
ic
o,
C
U
Z
-0
2
P
er
u
12
°
35


S
69
°
09


W
20
0
W

S
te
rr
a
24
17
in
ce
pt
is
ol
1
1
1
50
0
20
O
.P
.,
R
.V
.
L
ee
d
s,
9.
35
5.
21
4.
14
C
U
Z
A
M
1U
fi
rm
e
JB
M
(C
on
ti
nu
ed
.)
Phil. Trans. R. Soc. Lond. B (2004)

388 O. L. Phillips and others Patterns and process in Amazon tree turnover
T
ab
le
1.
(C
on
ti
nu
ed
.)
so
il
se
as
on
al
it
y
(0
=
(0
=
ce
n
su
s
ce
n
su
s
po
or
,
as
ea
so
n
al
,
m
ax
.
pl
ot
m
in
.
pl
ot
m
on
it
or
in
g
in
te
rv
al
in
te
rv
al
si
te
al
ti
tu
d
e
fo
re
st
ra
in
fa
ll
so
il
1
=
1
=
ar
ea
d
im
en
si
on
d
im
en
si
on
pr
in
ci
pa
l
in
st
it
u
ti
on
/
pe
ri
od
1
2
si
te
n
am
e
co
d
e
co
u
n
tr
y
la
ti
tu
d
e
lo
n
gi
tu
d
e
(m
)
re
gi
on
ty
pe
a
(m
m
)
or
d
er
ri
ch
er
)
se
as
on
al
)
(h
a)
(m
)
(m
)
in
ve
st
ig
at
or
pu
bl
ic
at
io
n
(y
ea
rs
)
(y
ea
rs
)
(y
ea
rs
)
C
u
zc
o
A
m
az
on
ic
o,
C
U
Z
-0
3
P
er
u
12
°
34


S
69
°
08


W
20
0
W

S
te
rr
a
24
17
in
ce
pt
is
ol
1
1
1
50
0
20
O
.P
.,
R
.V
.
L
ee
d
s,
9.
37
5.
22
4.
15
C
U
Z
A
M
2E
fi
rm
e
JB
M
C
u
zc
o
A
m
az
on
ic
o,
C
U
Z
-0
4
P
er
u
12
°
34


S
69
°
08


W
20
0
W

S
te
rr
a
24
17
in
ce
pt
is
ol
1
1
1
50
0
20
O
.P
.,
R
.V
.
L
ee
d
s,
9.
34
5.
18
4.
16
C
U
Z
A
M
2U
fi
rm
e
JB
M
In
fi
er
n
o
IN
F
-0
1
P
er
u
12
o
44


S
69
°
42


W
22
6
W

S
te
rr
a
23
00
in
ce
pt
is
ol
1
1
1
10
00
10
M
.A
.
N
ew
Y
or
k
7.
08
N
/A
N
/A
fi
rm
e
B
ot
an
ic
al
G
ar
d
en
Je
n
ar
o
H
er
re
ra
:
JE
N
-1
0
P
er
u
4°
55


S
73
°
44


W
12
0
W

S
te
rr
a
25
21
ox
is
ol
0
0
1
10
0
10
0
pu
bl
is
he
d
S
pi
ch
ig
er
5.
00
N
/A
N
/A
S
pi
ch
ig
er
fi
rm
e
et
al
.
(1
99
6)
Je
n
ar
o
H
er
re
ra
,
JE
N
-0
3
P
er
u
4°
55


S
73
°
44


W
11
0
W

S
se
as
on
al
ly
24
52
en
ti
so
l
1
0
1
10
0
10
0
pu
bl
is
he
d
N
eb
el
et
al
.
4.
04
N
/A
N
/A
R
es
ti
n
ga
pl
ot
3
fl
oo
d
ed
(2
00
1)
Je
n
ar
o
H
er
re
ra
,
JE
N
-0
6
P
er
u
4°
55


S
73
°
44


W
10
8
W

S
se
as
on
al
ly
24
52
en
ti
so
l
1
0
1
10
0
10
0
pu
bl
is
he
d
N
eb
el
et
3.
87
N
/A
N
/A
R
es
ti
n
ga
,
pl
ot
6
fl
oo
d
ed
al
.
(2
00
1)
Je
n
ar
o
H
er
re
ra
,
JE
N
-0
9
P
er
u
4°
55


S
73
°
44


W
10
6
W

S
se
as
on
al
ly
24
52
en
ti
so
l
1
0
1
10
0
10
0
pu
bl
is
he
d
N
eb
el
et
4.
04
N
/A
N
/A
T
ah
u
am
pa
pl
ot
9
fl
oo
d
ed
al
.
(2
00
1)
M
an
u
,
cl
ay
M
N
U
-0
2
P
er
u
11
°
52


S
71
°
21


W
31
2
W

S
te
rr
a
23
00
u
lt
is
ol
1
1
0.
34

10
0

50
J.
T
.,
P
.N
.
D
u
ke
15
.0
0
10
.0
0
5.
00
fi
rm
e
U
n
iv
er
si
ty
M
an
u
,
C
oc
ha
C
as
hu
M
N
U
-0
5
P
er
u
11
°
52


S
71
°
21


W
31
2
W

S
ra
re
ly
23
00
en
ti
so
l
1
1
2
15
0
15
0
J.
T
.,
P
.N
.
D
u
ke
10
.0
0
5.
00
5.
00
T
ra
il
12
fl
oo
d
ed
U
n
iv
er
si
ty
M
an
u
,
C
oc
ha
C
as
hu
M
N
U
-0
6
P
er
u
11
°
52


S
71
°
21


W
31
2
W

S
ra
re
ly
23
00
en
ti
so
l
1
1
2.
25
15
0
15
0
J.
T
.,
P
.N
.
D
u
ke
10
.0
0
5.
00
5.
00
T
ra
il
2
&
31
fl
oo
d
ed
U
n
iv
er
si
ty
M
an
u
,
C
oc
ha
C
as
hu
M
N
U
-0
1
P
er
u
11
°
52


S
71
°
21


W
31
2
W

S
ra
re
ly
23
00
en
ti
so
l
1
1
0.
97
16
0
60
J.
T
.,
P
.N
.
D
u
ke
25
.7
5
15
.7
5
10
.0
0
T
ra
il
3
fl
oo
d
ed
U
n
iv
er
si
ty
M
an
u
,
te
rr
a
fi
rm
e
M
N
U
-0
4
P
er
u
11
°
53


S
71
°
21


W
31
2
W

S
te
rr
a
23
00
u
lt
is
ol
1
1
2
20
0
10
0
J.
T
.,
P
.N
.
D
u
ke
10
.0
0
5.
00
5.
00
ra
vi
n
e
fi
rm
e
U
n
iv
er
si
ty
M
an
u
,
te
rr
a
fi
rm
e
M
N
U
-0
3
P
er
u
11
°
53


S
71
°
21


W
31
2
W

S
te
rr
a
23
00
u
lt
is
ol
1
1
2
20
0
10
0
J.
T
.,
P
.N
.
D
u
ke
10
.0
0
5.
00
5.
00
te
rr
ac
e
fi
rm
e
U
n
iv
er
si
ty
M
an
u
,
tr
an
s-
M
an
u
M
N
U
-0
7
P
er
u
11
°
53


S
71
°
21


W
31
2
W

S
te
rr
a
23
00
u
lt
is
ol
1
1
1
10
0
10
0
P
.N
.,
J.
T
.
U
N
S
A
A
C
,
3.
00
N
/A
N
/A
u
pl
an
d
fi
rm
e
D
u
ke
U
n
iv
er
si
ty
M
is
ha
n
a
M
S
H
-0
1
P
er
u
3°
47


S
73
°
30


W
11
4
W

S
te
rr
a
27
63
sp
od
os
ol
/
1
0
1
10
0
10
0
O
.P
.,
R
.V
.
L
ee
d
s,
7.
67
N
/A
N
/A
fi
rm
e
u
lt
is
ol
JB
M
P
ak
it
za
,
M
an
u
P
A
K
-0
1
P
er
u
11
o
55


S
71
o
15


W
31
3
W

S
te
rr
a
23
00
u
lt
is
ol
?
1
1
1
10
0
10
0
J.
C
o.
S
m
it
hs
on
ia
n
4.
00
N
/A
N
/A
R
iv
er
,
pl
ot
1
fi
rm
e
In
st
it
u
ti
on
P
ak
it
za
,
M
an
u
P
A
K
-0
2
P
er
u
11
°
55


S
71
°
15


W
31
3
W

S
ra
re
ly
23
00
en
ti
so
l?
1
1
1
10
0
10
0
J.
C
o.
S
m
it
hs
on
ia
n
4.
00
N
/A
N
/A
R
iv
er
,
pl
ot
2
fl
oo
d
ed
In
st
it
u
ti
on
P
ak
it
za
,
M
an
u
ri
ve
r,
P
A
K
-0
3
P
er
u
11
°
55


S
71
°
15


W
31
3
W

S
sw
am
p
23
00
eu
tr
op
hi
c
1
1
1
10
0
10
0
J.
C
o.
S
m
it
hs
on
ia
n
4.
00
N
/A
N
/A
sw
am
p
hi
st
os
ol
?
In
st
it
u
ti
on
S
u
cu
sa
ri
A
S
U
C
-0
1
P
er
u
3°
26


S
72
°
54


W
10
7
W

S
te
rr
a
26
71
u
lt
is
ol
1
0
1
50
0
20
O
.P
.,
R
.V
.
L
ee
d
s,
8.
93
3.
95
4.
98
fi
rm
e
JB
M
S
u
cu
sa
ri
B
S
U
C
-0
2
P
er
u
3°
26


S
72
°
54


W
10
7
W

S
te
rr
a
26
71
u
lt
is
ol
1
0
1
50
0
20
O
.P
.,
R
.V
.
L
ee
d
s,
8.
93
3.
95
4.
99
fi
rm
e
JB
M
T
am
bo
pa
ta
pl
ot
fo
u
r
T
A
M
-0
6
P
er
u
12
°
50


S
69
°
18


W
22
0
W

S
te
rr
a
23
00
in
ce
pt
is
ol
1
1
0.
96
10
0
10
0
O
.P
.,
R
.V
.
L
ee
d
s,
16
.8
4
7.
83
9.
01
fi
rm
e
JB
M
T
am
bo
pa
ta
pl
ot
on
e
T
A
M
-0
2
P
er
u
12
°
50


S
69
°
17


W
22
0
W

S
te
rr
a
23
00
in
ce
pt
is
ol
1
1
1
10
0
10
0
O
.P
.,
R
.V
.
L
ee
d
s,
20
.7
1
11
.7
9.
01
fi
rm
e
JB
M
(C
on
ti
nu
ed
.)
Phil. Trans. R. Soc. Lond. B (2004)

Patterns and process in Amazon tree turnover O. L. Phillips and others 389
T
ab
le
1.
(C
on
ti
nu
ed
.)
so
il
se
as
on
al
it
y
(0
=
(0
=
ce
n
su
s
ce
n
su
s
po
or
,
as
ea
so
n
al
,
m
ax
.
pl
ot
m
in
.
pl
ot
m
on
it
or
in
g
in
te
rv
al
in
te
rv
al
si
te
al
ti
tu
d
e
fo
re
st
ra
in
fa
ll
so
il
1
=
1
=
ar
ea
d
im
en
si
on
d
im
en
si
on
pr
in
ci
pa
l
in
st
it
u
ti
on
/
pe
ri
od
1
2
si
te
n
am
e
co
d
e
co
u
n
tr
y
la
ti
tu
d
e
lo
n
gi
tu
d
e
(m
)
re
gi
on
ty
pe
a
(m
m
)
or
d
er
ri
ch
er
)
se
as
on
al
)
(h
a)
(m
)
(m
)
in
ve
st
ig
at
or
pu
bl
ic
at
io
n
(y
ea
rs
)
(y
ea
rs
)
(y
ea
rs
)
T
am
bo
pa
ta
pl
ot
si
x
T
A
M
-0
7
P
er
u
12
°
50


S
69
°
16


W
22
0
W

S
te
rr
a
23
00
ox
is
ol
1
1
1
10
0
10
0
O
.P
.,
R
.V
.
L
ee
d
s,
14
.9
7
7.
78
7.
18
fi
rm
e
JB
M
T
am
bo
pa
ta
pl
ot
T
A
M
-0
5
P
er
u
12
°
50


S
69
°
17


W
22
0
W

S
te
rr
a
23
00
u
lt
is
ol
1
1
1
10
0
10
0
O
.P
.,
R
.V
.
L
ee
d
s,
16
.8
6
7.
84
9.
02
th
re
e
fi
rm
e
JB
M
T
am
bo
pa
ta
pl
ot
tw
o
T
A
M
-0
3
P
er
u
12
°
50


S
69
°
17


W
22
0
W

S
sw
am
p
23
00
hi
st
os
ol
0
1
0.
58
10
0
10
0
O
.P
.,
R
.V
.
L
ee
d
s,
15
.0
0
6.
98
7.
98
sw
am
p
(d
ys
tr
op
hi
c)
JB
M
T
am
bo
pa
ta
pl
ot
tw
o
T
A
M
-0
4
P
er
u
12
°
50


S
69
°
17


W
22
0
W

S
te
rr
a
23
00
in
ce
pt
is
ol
1
1
0.
42
10
0
10
0
O
.P
.,
R
.V
.
L
ee
d
s,
14
.9
6
6.
98
7.
98
sw
am
p
ed
ge
fi
rm
e
JB
M
T
am
bo
pa
ta
pl
ot
T
A
M
-0
1
P
er
u
12
°
50


S
69
°
17


W
22
0
W

S
te
rr
a
23
00
in
ce
pt
is
ol
1
1
1
10
0
10
0
O
.P
.,
R
.V
.
L
ee
d
s,
16
.8
1
7.
75
9.
06
ze
ro
fi
rm
e
JB
M
Y
an
am
on
o
A
Y
A
N
-0
1
P
er
u
3°
26


S
72
°
51


W
10
4
W

S
te
rr
a
26
71
ca
m
bi
so
l/
1
0
1
12
0
10
0
O
.P
.,
R
.V
.
L
ee
d
s,
17
.5
9
7.
83
9.
76
fi
rm
e
m
ol
lis
ol
JB
M
C
E
L
O
S
67
/9
A
C
E
L
-0
8
S
u
ri
n
am
e
5°
13


N
55
°
40


W
66
E

C
te
rr
a
22
01
u
lt
is
ol
1
1
0.
64
80
80
pu
bl
is
he
d
d
e
G
ra
af
et
al
.
28
.0
0
?
?
pl
ot
8
fi
rm
e
(1
99
9)
C
an
o
R
os
al
ba
,
Z
1,
C
R
S
-0
1
V
en
ez
u
el
a
9°
15


N
72
°
W
60
E

C
te
rr
a
33
80
ox
is
ol
0
1
1
10
0
10
0
pu
bl
is
he
d
V
ei
llo
n
2.
02
N
/A
N
/A
C
R
1
fi
rm
e
(1
98
5)
C
an
o
R
os
al
ba
,
Z
2,
C
R
S
-0
2
V
en
ez
u
el
a
9°
15


N
72
°
W
35
E

C
te
rr
a
30
00
in
ce
pt
is
ol
1
1
1
10
0
10
0
pu
bl
is
he
d
V
ei
llo
n
1.
99
N
/A
N
/A
C
R
2
fi
rm
e
(1
98
5)
E
l
D
or
ad
o
km
91
E
L
D
-1
2
V
en
ez
u
el
a
6°
30


N
61
°
30


W
21
0
E

C
te
rr
a
32
00
ox
is
ol
?
0
1
0.
5
50
50
A
.
T
or
re
s,
U
n
iv
.
d
e
22
.8
9
9.
63
13
.2
6
fi
rm
e
S
.
B
ro
w
n
lo
s
A
n
d
es
,
W
in
ro
ck
E
l
D
or
ad
o
km
98
E
L
D
-3
4
V
en
ez
u
el
a
6°
30


N
61
°
30


W
38
0
E

C
te
rr
a
32
00
ox
is
ol
?
0
1
0.
5
50
50
A
.
T
or
re
s,
U
n
iv
.
d
e
9.
63
4.
66
4.
98
fi
rm
e
S
.
B
ro
w
n
lo
s
A
n
d
es
,
W
in
ro
ck
R
io
G
ra
n
d
e
R
IO
-1
2
V
en
ez
u
el
a
8°
N
61
°
45
’W
27
0
E

C
te
rr
a
25
00
ox
is
ol
?
0
1
0.
5
50
50
A
.
T
or
re
s,
U
n
iv
.
d
e
22
.8
8
9.
62
13
.2
6
fi
rm
e
S
.
B
ro
w
n
lo
s
A
n
d
es
,
W
in
ro
ck
S
an
C
ar
lo
s
d
e
R
io
S
C
R
-0
1
V
en
ez
u
el
a
1°
56


N
67
°
03


W
12
2
E

C
te
rr
a
35
20
ox
is
ol
0
0
1
10
0
10
0
pu
bl
is
he
d
U
hl
et
al
.
10
.7
1
5.
25
5.
08
N
eg
ro
,
S
C
1
fi
rm
e
(1
98
5)
S
an
C
ar
lo
s
d
e
R
io
S
C
R
-0
2
V
en
ez
u
el
a
1°
45


N
67
°
W
12
2
E

C
te
rr
a
35
20
sp
od
os
ol
0
0
0.
25
25
25
pu
bl
is
he
d
V
ei
llo
n
4.
01
N
/A
N
/A
N
eg
ro
,
S
C
2
fi
rm
e
(1
98
5)
S
an
C
ar
lo
s
d
e
R
io
S
C
R
-0
3
V
en
ez
u
el
a
1°
45


N
67
°
W
11
7
E

C
te
rr
a
35
20
sp
od
os
ol
0
0
2
20
0
50
pu
bl
is
he
d
H
eu
ve
ld
op
&
4.
00
N
/A
N
/A
N
eg
ro
,
S
C
3
fi
rm
e
N
eu
m
an
n
(1
98
3)
a
T
er
ra
fi
rm
e,
d
ef
in
ed
as
pr
es
u
m
ed
n
ot
to
ha
ve
ex
pe
ri
en
ce
d
fl
u
vi
al
fl
oo
d
in
g
in
at
le
as
t
25
0
ye
ar
s.
b
T
ap
aj
os
:
th
es
e
ar
e
12
ha
×
0.
25
ha
pl
ot
s
la
id
ou
t
in
a
ra
n
d
om
iz
ed
fa
sh
io
n
ov
er
an
ar
ea
of
30
0
m
×
12
00
m
;
at
th
e
ti
m
e
of
an
al
ys
is
tr
ea
te
d
as
3
×
1
ha
u
n
it
s.
Phil. Trans. R. Soc. Lond. B (2004)

390 O. L. Phillips and others Patterns and process in Amazon tree turnover
T
ab
le
2.
S
it
e-
by
-s
it
e
su
m
m
ar
y
st
ru
ct
u
ra
l
an
d
d
yn
am
ic
pr
op
er
ti
es
,
al
l
si
te
s.
(D
at
a
ar
e
th
e
be
st
av
ai
la
bl
e
to
th
e
le
ad
au
th
or
at
th
e
ti
m
e
of
fi
n
al
an
al
ys
es
,
bu
t
ar
e
su
bj
ec
t
to
fu
tu
re
re
vi
si
on
as
a
re
su
lt
of
ad
d
it
io
n
al
ce
n
su
se
s
an
d
co
n
ti
n
u
ed
er
ro
r-
ch
ec
ki
n
g.
D
at
e
of
fi
n
al
an
al
ys
es
fo
r
th
is
pa
pe
r,
ca
.
1
M
ar
ch
20
03
.)
re
cr
u
it
m
en
t,
m
or
ta
lit
y,
tu
rn
ov
er
,
‘m
aj
es
ti
c
m
on
it
or
in
g
ba
sa
l
ar
ea
st
em
s
in
te
rv
al
in
te
rv
al
in
te
rv
al
fi
rs
t
ce
n
su
s
fo
re
st
’
bi
as
pe
ri
od
st
ar
t
st
ar
t
re
cr
u
it
m
en
tb
m
or
ta
lit
yb
tu
rn
ov
er
b
co
rr
ec
te
d
b
co
rr
ec
te
d
b
co
rr
ec
te
db
si
te
n
am
e
co
u
n
tr
y
si
te
co
d
e
(n
ce
n
su
se
s)
po
ss
ib
le
?a
(y
ea
rs
)
(m
2
ha

1
)
(h
a
1
)
(%
yr

1
)
(%
yr

1
)
(%
yr

1
)
(%
yr

1
)
(%
yr

1
)
(%
yr

1
)
B
D
F
F
P
,
11
02
G
av
ia
o
B
ra
zi
l
B
D
F
-0
4
19
81
.1
3
(4
)
Y
?,
g?
18
.1
7
28
.3
9
59
0
2.
49
2.
81
2.
65
3.
14
3.
54
3.
34
C
er
ro
P
el
ao
1
B
ol
iv
ia
C
R
P
-0
1
19
94
.2
1
(3
)
Y
?,
d
?
7.
25
20
.3
0
55
2
1.
87
3.
32
2.
59
2.
19
3.
89
3.
03
C
er
ro
P
el
ao
2
B
ol
iv
ia
C
R
P
-0
2
19
94
.2
7
(3
)
N
,
d
f
7.
19
24
.0
9
47
2
3.
05
2.
30
2.
67
3.
57
2.
69
3.
13
C
ho
re
1
B
ol
iv
ia
C
H
O
-0
1
19
96
.5
3
(2
)
N
,
cf
4.
91
14
.0
8
56
5
2.
39
2.
61
2.
50
2.
71
2.
96
2.
84
H
u
an
ch
ac
a
D
os
,
pl
ot
1
B
ol
iv
ia
H
C
C
-2
1
19
96
.5
2
(2
)
N
,
ce
f
4.
91
24
.6
9
52
9
2.
38
2.
85
2.
61
2.
70
3.
24
2.
96
H
u
an
ch
ac
a
D
os
,
pl
ot
2
B
ol
iv
ia
H
C
C
-2
2
19
96
.5
4
(2
)
N
,
ce
f
4.
89
26
.6
6
64
4
1.
18
1.
79
1.
48
1.
34
2.
03
1.
68
L
as
L
on
d
ra
s,
pl
ot
1
B
ol
iv
ia
L
S
L
-0
1
19
96
.5
3
(2
)
N
,
cf
4.
95
17
.5
2
56
0
1.
56
2.
86
2.
21
1.
77
3.
25
2.
51
L
as
L
on
d
ra
s,
pl
ot
2
B
ol
iv
ia
L
S
L
-0
2
19
96
.5
3
(2
)
N
,
cf
4.
95
20
.4
5
63
0
1.
25
1.
19
1.
22
1.
42
1.
35
1.
39
L
os
F
ie
rr
os
B
os
qu
e
I
B
ol
iv
ia
L
F
B
-0
1
19
93
.6
2
(3
)
N
,
cf
7.
78
23
.5
7
55
7
2.
86
3.
36
3.
11
3.
37
3.
96
3.
66
L
os
F
ie
rr
os
B
os
qu
e
II
B
ol
iv
ia
L
F
B
-0
2
19
93
.6
5
(3
)
N
,
cf
7.
76
28
.0
3
54
0
2.
82
2.
73
2.
78
3.
32
3.
22
3.
28
B
D
F
F
P
,
11
01
G
av
ia
o
B
ra
zi
l
B
D
F
-0
3
19
81
.1
3
(4
)
N
,
fg
18
.1
7
28
.3
9
59
3
1.
10
1.
21
1.
15
1.
39
1.
53
1.
45
B
D
F
F
P
,
11
03
G
av
ia
o
B
ra
zi
l
B
D
F
-0
5
19
81
.2
1
(4
)
N
,
fg
18
.0
8
25
.2
8
65
0
0.
93
1.
84
1.
12
1.
18
2.
32
1.
41
B
D
F
F
P
,
11
13
F
lo
re
st
al
B
ra
zi
l
B
D
F
-0
9
19
87
.0
4
(3
)
N
,
fg
10
.2
5
29
.4
9
57
1
0.
67
1.
25
0.
96
0.
81
1.
51
1.
16
B
D
F
F
P
,
12
01
G
av
ia
o
B
ra
zi
l
B
D
F
-0
6
19
81
.2
9
(4
)
N
,
fg
18
.0
0
25
.4
8
63
2
1.
17
1.
48
1.
33
1.
47
1.
87
1.
68
B
D
F
F
P
,
13
01
F
lo
re
st
al
1
B
ra
zi
l
B
D
F
-1
0
19
83
.4
6
(3
)
N
,
fg
13
.6
7
27
.4
7
63
2
1.
49
1.
40
1.
44
1.
84
1.
73
1.
78
B
D
F
F
P
,
13
01
F
lo
re
st
al
2
B
ra
zi
l
B
D
F
-1
1
19
83
.4
6
(3
)
N
,
fg
13
.6
7
28
.8
5
62
9
0.
61
0.
68
0.
65
0.
75
0.
84
0.
80
B
D
F
F
P
,
13
01
F
lo
re
st
al
3
B
ra
zi
l
B
D
F
-1
2
19
83
.4
6
(3
)
N
,
fg
13
.6
7
28
.4
5
61
7
0.
60
0.
61
0.
60
0.
74
0.
75
0.
74
B
D
F
F
P
,
23
03
F
az
.
B
ra
zi
l
B
D
F
-0
1
19
85
.2
9
(4
)
N
,
d
fg
12
.4
2
30
.1
5
68
8
1.
23
1.
18
1.
20
1.
50
1.
44
1.
47
D
im
on
a
4–
6
B
D
F
F
P
,
33
04
P
or
to
B
ra
zi
l
B
D
F
-1
4
19
84
.2
1
(5
)
N
,
be
g
14
.1
7
32
.0
3
65
1
1.
25
1.
29
1.
27
1.
55
1.
59
1.
57
A
le
gr
e
B
D
F
F
P
,
34
02
C
ab
o
F
ri
o
B
ra
zi
l
B
D
F
-1
3
19
85
.8
6
(4
)
N
,
d
ef
g
13
.0
2
26
.5
2
56
5
1.
31
0.
98
1.
14
1.
61
1.
20
1.
40
B
io
n
te
1
B
ra
zi
l
B
N
T
-0
1
19
86
.5
0
N
,
af
12
.7
0
28
.0
4
56
1
1.
07
0.
91
0.
99
1.
31
1.
12
1.
21
(1
1)
B
io
n
te
2
B
ra
zi
l
B
N
T
-0
2
19
86
.5
0
N
,
af
12
.7
0
30
.1
4
69
2
0.
64
0.
64
0.
64
0.
78
0.
78
0.
78
(1
1)
B
io
n
te
4
B
ra
zi
l
B
N
T
-0
4
19
86
.5
0
N
,
af
12
.7
0
27
.7
6
60
8
1.
07
1.
22
1.
14
1.
31
1.
50
1.
40
(1
0)
B
io
n
te
T
4
B
1
S
B
3
B
ra
zi
l
B
N
T
-0
6
19
86
.5
0
(5
)
N
,
a
7.
00
32
.3
3
57
6
1.
21
1.
43
1.
32
1.
41
1.
67
1.
54
B
io
n
te
T
4
B
2
S
B
1
B
ra
zi
l
B
N
T
-0
5
19
86
.5
0
(5
)
N
,
af
7.
00
26
.0
5
56
5
1.
72
1.
52
1.
62
2.
01
1.
78
1.
89
B
io
n
te
T
4
B
4
S
B
4
B
ra
zi
l
B
N
T
-0
7
19
86
.5
0
(5
)
N
,
af
7.
00
30
.5
9
64
3
1.
23
1.
01
1.
12
1.
44
1.
18
1.
31
(C
on
ti
nu
ed
.)
Phil. Trans. R. Soc. Lond. B (2004)