Population structure and nut yield of a Bertholletia excelsa stand in Southwestern Amazonia

Population structure and nut yield of a Bertholletia excelsa

stand in Southwestern Amazonia

Lucia H.O. Wadt a, Karen A. Kainer b,*, Daisy A.P. Gomes-Silva c

a Centro de Pesquisa Agroflorestal do Acre (Embrapa Acre), Caixa Postal 321, Rio Branco, Acre 69901-108, Brazilb University of Florida, School of Forest Resources and Conservation and the Tropical Conservation and

Development Program, P.O. Box 110410, Gainesville, FL 32611-0410, USAc Conselho Nacional de Desenvolvimento Cientıfico e Tecnologico (CNPq), Rua Palmas, 96, Jardim Tropical,

Rio Branco, Acre 69.910-560 Brazil

Received 14 May 2004; received in revised form 26 February 2005; accepted 28 February 2005

Abstract

Although Brazil nut (B. excelsa) is often touted as one of the most economically successful NTFPs, little is known about the

population structure of this species within its natural range in Southwestern Amazonia or ecological factors that affect fruit

production. Since these are considered fundamental for sustainable resource management, we examined a natural Brazil nut

stand in an extractive reserve in Acre, Brazil, posing the following questions: (1) What is the spatial distribution, species density,

and size–class structure of B. excelsa? and (2) What tree-level factors influence Brazil nut production? In a 420 ha census, 568

trees �10 cm diameter at breast height (dbh) were counted, resulting in a density of 1.35 trees ha�1. Based on the nearest-

neighbor method, an index of aggregation (R) of 0.77 indicated a rejection of the null hypothesis of a strictly random distribution

pattern. Yet, this value suggests a much greater tendency toward randomness than either clumping or uniformity. Our data do not

show the commonly reported existence of groves, referring to clearly defined clusters of 50 to several hundred trees separated

from similar clusters by great distances. Almost 1/4 of the population (23%) was composed of non-reproductive juveniles.

Maximum R2 improvement analysis applied to four distinct diameter classes provided insight into the dynamics of production-

related variables over the species life cycle. While dbh explained 1/3 of production variance (R2 = 0.3360) in the smallest

diameter class (10 cm � dbh < 50 cm), which included those in the process of reaching reproductive maturity, crown form best

explained production variance of very large trees (dbh � 100 cm). Results also demonstrated a significant negative correlation

between crown vine load and production of trees � 50 cm dbh (r = �0.13, P = 0.008), suggesting the need for further study on

vine cutting as a possible silvicultural treatment for enhancing nut yields.

# 2005 Elsevier B.V. All rights reserved.

Keywords: Brazil nut; Size–class structure; Spatial distribution; Species density; Tropical forest; Vine load

www.elsevier.com/locate/foreco

Forest Ecology and Management 211 (2005) 371–384

* Corresponding author. Tel.: +1 352 846 0833;

fax: +1 352 846 1277.

E-mail address: [email protected] (K.A. Kainer).

0378-1127/$ – see front matter # 2005 Elsevier B.V. All rights reserved

doi:10.1016/j.foreco.2005.02.061

1. Introduction

The extraction of non-timber forest products

(NTFPs) is widely considered to be one of the most

.

L.H.O. Wadt et al. / Forest Ecology and Management 211 (2005) 371–384372

ecologically benign uses of tropical forests. In the late

1980s, this perception precipitated enthusiasm for

commercial extraction of NTFPs as an alternative to

deforestation (Anderson, 1990), balancing forest

conservation and sustainable development (Nepstad

and Schwartman, 1992). Subsequent, more refined

conceptual analyses of NTFPs centered on the

ecological implications of NTFP harvest (Peters,

1996; Arnold and Perez, 2001; Ticktin, 2004), the

challenge of maintaining long-term economic viabi-

lity of extracted resources from the wild (Homma,

1992; Wilkie and Godoy, 1996), and methods for

assessing development and conservation benefits of

NTFPs for local communities (Wollenberg and Ingles,

1998).

Brazil nut (B. excelsa Humb. and Bonpl.) is often

touted as one of the most economically successful

NTFPs (Peters, 1996; Clay, 1997; Ortiz, 2002). The

seeds (or nuts) have been collected for decades, and in

2002 in Brazil alone, were worth over US$ 10 million

(IBGE, 2002). Perhaps more importantly, Brazil nut

collection and processing generates income for

thousands of families in Bolivia, Brazil, and Peru

(Clay, 1997). To date, all Brazil nuts upon which this

industry is based have been collected in the wild by

extractivists living in or near the relatively pristine

Brazil nut-rich forests. Similar to other NTFPs (Peters,

1996), the exploitation of Brazil nut has not

traditionally been based on a firm ecological under-

standing of population dynamics of the species.

In recent years, there has been increased interest in

understanding the functional ecology of these natural

Brazil nut populations, often in relation to sustaining

and/or improving yield. Significant new research has

been forthcoming on the population structure and

dynamics of B. excelsa (Peres and Baider, 1997;

Baider, 2000; Zuidema and Boot, 2002). Most

recently, a basin-wide comparative analysis of 23

Brazil nut populations suggests that intensive nut

(seed) harvest over decades (largely Eastern Amazo-

nia) has resulted in inadequate juvenile recruitment to

maintain populations over the long term (Peres et al.,

2003). Fewer studies have focused explicitly on how

to manage natural populations for increasing Brazil

nut production (Kainer et al., 1998; Arredondo and

Zonta, 2000; Pena-Claros et al., 2002); see Zuidema

(2003) for a more comprehensive list of ecological and

management-oriented findings and related research.

In the Western Brazilian state of Acre, conservation

units such as extractive reserves have been created to

promote the use of renewable natural resources while

maintaining the major ecological functions of the

natural ecosystem (Fearnside, 2003). The requirement

of management plans within these reserves has

triggered a transition from a traditional pattern of

general forest exploitation to more conscious manage-

ment of the ecosystem and its resources. This includes

managing the Brazil nut populations, since Brazil nuts,

with rubber, are considered to be the cornerstones of the

extractive economy in much of this region. Since one of

the first steps in sustainable management of a given

forest resource is to ascertain the population structure

and production potential of the target species (Peters,

1994, 1996), we examined a natural Brazil nut stand in

Extractive Reserve Chico Mendes in Acre, Brazil,

posing the following questions: (1) What is the spatial

distribution, species density, and size–class structure of

B. excelsa? and (2) What tree-level factors influence

Brazil nut production?

2. Species description

B. excelsa is a large (up to 50 m tall), dominant,

upper canopy tree. Its distribution is restricted to non-

flooded (terra firme) forests in the Amazon basin and

the Guianas (Prance, 1990). Mori and Prance (1990)

assert that Brazil nut is not evenly distributed

throughout this range, but instead often occurs in

groves of 50–100 individuals with each grove separated

from another by distances of up to 1 km. Plant densities

(diameter at breast height (dbh) � 10 cm) averaged 1.3

individuals ha�1 in a Peres and Baider (1997) study in

Southeastern Amazonia, although some stands that they

described as groves had densities of 5.1 trees ha�1.

Brazil nut is also long-lived, with carbon dating of a

225 cm dbh individual confirming an age of 500 years

(Camargo et al., 1994). The massive size and long-lived

nature of Brazil nut trees suggests that they play a

critical ecological role in the forested ecosystem. In

their investigation of tropical trees over 70 cm diameter

above buttresses, Clark and Clark (1996) emphasize the

ecological importance of large, long-lived individuals

on ecosystem structure and function, including their

role in carbon storage, disturbance regime maintenance

and creation of microenvironments, and their ecophy-

L.H.O. Wadt et al. / Forest Ecology and Management 211 (2005) 371–384 373

siological niche as canopy emergents more tightly

coupled to weather and climatic changes.

Basic information on Brazil nut pollination is limited

due largely to the difficulties of observing pollinators in

the natural forest. It is clear, however, that Brazil nut

trees rely on out-crossing for seed development

(O’Malley et al., 1988), and are pollinated principally

by large-bodied bees, especially Euglossinae bees,

capable of lifting the hood of the zygomorphic flower

(Prance, 1976, Nelson et al., 1985). Flowers appear at

the beginning of the rainy season, and each flower is

open and viable for only a few hours at sunrise, falling

that same afternoon (Nelson et al., 1985).

Once successfully pollinated, fruit maturation takes

14 months, resulting in a hard 10–16 cm round fruit that

falls annually during the rainy season. The 10–25 large

(�3.5–5 cm � 2 cm) seeds (or nuts) remain inside this

woody fruit until extraction by humans or other

predators, notably agoutis (Dasyprocta spp.), capable

of breaking through the hard pericarp. There is strong

variation in individual tree production between years,

though this individual variation tends to average out

over a population of trees at any given year (Zuidema,

2003). In the Southwestern Amazon, fruit fall and

collection occur mostly in January and February.

3. Study area

Field studies were carried out within Extractive

Reserve (RESEX) Chico Mendes, a conservation unit

of 976,570 ha located from 108 to 118S of the equator

within the eastern portion of the state of Acre, Brazil

(Fig. 1). Within RESEX Chico Mendes, research was

executed at an extractivist landholding or colocacao in

the southeastern portion of the reserve: Colocacao Rio

de Janeiro in Seringal Filipinas—a Brazil nut-rich area

of 420 ha. Though no hard data exist for this particular

site, the few available documents indicate that Brazil

nuts have been extracted commercially in this region

since the early 1940s (Pechnik et al., 1950), with

elderly residents adding that collection for sale has

taken place since the 1920s.

The study region has lightly undulating topography,

with dominant vegetation classified as humid, moist

tropical forests (Holdridge, 1978). The forest type in

this site is considered to be open forest following Daly

and Mitchell (2000), with an average basal area of

24.7 m2 ha�1, well-spaced dominants, a relatively

closed understory, and a strong presence of various

palm and liana species. The region has a pronounced 3-

month dry season from June to August. Average annual

rainfall in the study area is from 1600 to 2000 mm, 95%

of which falls between September and May (IMAC,

1991). Average temperature is approximately 25 8C(ZEE, 2000), and brief intrusions of frigid air from the

south are common during the dry season, with

temperatures dropping to 8 8C. Soils of the region

are classified under the Brazilian system as Argissolos

(ZEE, 2000), roughly equivalent to Ultisols under the

U.S. Soil Taxonomy system.

4. Methods

4.1. Field measurements

Brazil nut trees �10 cm dbh at Colocacao Rio de

Janeiro were evaluated from May to August 2001, and

additional trees encountered through December 2002

(while carrying out subsequent field work) were also

included in the census. This approximation of a census

of such a large area was possible because of three

factors: (1) the resident extractivist, who has a long-

standing economic relationship with his Brazil nut

trees, accompanied the researchers to locate individuals

on his landholding, mostly by following existing trails

used for travel and accessing Brazil nut, rubber, and

game resources; (2) the ease in identifying the species

due to distinct tree characteristics (i.e., bark, emergent

crown, fruits); (3) the continuous combing for trees by

the research team. Trees encountered were marked,

georeferenced with a Garmin XL GPS unit, and

measured for dbh, using a chrome-clad diameter tape at

1.4 m above the ground; B. excelsa does not have

pronounced buttresses.

The extractivist who has user rights and collects nuts

from these stands, estimated average annual nut

production for each individual tree based on his

recollection from the previous 5-year collection period,

placing it into one of four production classes: (1) no

production; (2) �1 lata; (3) 1–3 latas; (4) >3 latas. The

‘lata’ unit is a volume measure where 1 lata = 18 l,

which can hold �11 kg of nuts. To verify extractivist

estimates, fruits from the 2002 fruit fall were

enumerated and nut production measured on a


Fig. 1. Location of 420 ha study area (RESEX Chico Mendes, Seringal Filipinas, Colocacao Rio de Janeiro).


subsample of 140 reproductively mature trees (defined

as dbh � 50 cm). This subsample was selected ran-

domly, controlling for diameter to ensure representation

by all classes. All fruits (or a subsample if number of

fruits >50) were cracked open, and nuts were then

weighed.

Light climate, crown form, and vine load were also

evaluated for all trees censused. To characterize light

climate, crown position was categorized based on a

modified Dawkins illumination index cited in Synnott

(1979). The four categories were: (1) dominant (full

overhead and sidelight); (2) co-dominant (full overhead

light); (3) intermediate (some overhead or sidelight);

(4) suppressed (no direct light). To evaluate horizontal

crown form, trees were placed into one of five

categories, again based on Dawkins as cited in Synnott

(1979): (1) perfect (complete circle); (2) good (irregular

circle); (3) tolerable (half-crown); (4) poor (less than

half-crown); (5) very poor (one or a few branches). Vine

load within the crown and on the trunk were evaluated

separately. Vine load within crowns was placed into one

of four categories: (1) no vines; (2) �25% of crown

covered; (3) 25–75% of crown covered; (4) >75% of

crown covered. Trunk vine load was scored as either

zero (no vines touching the trunk) or one (at least one

vine touching the trunk). For each tree, determination of

crown position, crown form, and vine load categories

was based on observations and discussions by at least

three members of the research team.

4.2. Data analysis

Diameter distribution by classes, spatial distribution,

population density, and basal area were determined

from the stand-level census. For analyzing patterns of

Brazil nut spatial distributions, we applied Clark and

Evans’ (1954) nearest-neighbor method. Nonetheless,

since a boundary strip was not defined in our census and

we considered the landholding boundary to be reason-

ably smooth, we applied Donnelly’s (1978) modifica-

tion to the Clark and Evans test as recommended to

eliminate bias (Krebs, 1999). Based on GPS locations

of the trees encountered, we determined average

distance between each individual tree and its nearest

neighbor, and the index of aggregation of the popula-

tion (R) that provides an indication of whether the

population has a clumped, random, or uniform

distribution. Finally, we calculated the z-value to

determine the statistical level (P-value) at which the

observed pattern deviates from an expected random

pattern. These calculations were applied to the entire

Brazil nut population as well as two demographic

subsets of the whole: reproductively mature adults

(dbh � 50 cm) and non-reproductive juveniles

(dbh < 50 cm).

All trees were initially grouped into 10 cm diameter

classes to determine size–class structure. Following,

trees were further grouped into one of four diameter

classes, and descriptive statistics were employed to

construct frequencies for variables measured of all

individuals enumerated in the census. These four

diameter classes were considered to have some

biological meaning, though not a true proxy for tree

age or development. The smallest class consisted of

trees 10 cm � dbh < 50 cm, of which the vast majority

were not reproductively mature individuals. Those

50 cm � dbh < 100 cm were considered to be young

individuals, increasing in productivity. Those 100 cm

� dbh < 150 cm were considered to be mature adults

in full production, and those �150 cm were considered

old growth and exhibiting declining productivity.

Spearman’s correlation coefficients were calculated

as a preliminary data analysis step to determine the

strength of relationships between measured variables

for all Brazil nut trees (dbh � 10 cm) in the Rio de

Janeiro landholding. For key variables showing

statistically significant correlations (P � 0.05), multi-

ple regression models based on maximum R2 improve-

ment were then constructed to further explore these

relationships. This latter analysis is possible with

ordered categorical data when there is a very large

number of observations (as in the number of trees in our

study) because of the central limit theorem. This

theorem states that the distribution of a linear function

of errors tends toward normality as the number of

observations becomes large, almost irrespective of the

individual distributions of the components (Box et al.,

1978).

Production data from the subsample of 140 Brazil

nut trees were then used to fit a multiple linear

regression model, enabling comparison of extractivist

nut production categorical estimates with a continuous

scale, ratio measurement. To ensure that linear

regression assumptions were met, log 10 transforma-

tions were taken on total nut weight, which was used

as the response variable. Different models were tested


using predictors and combinations thereof, and model

validity was checked through residual analysis. All

statistical analyses were accomplished with SAS,

Version 8.2 (SAS Institute Inc., Cary, NC).

5. Results

5.1. Stand-level parameters

Five-hundred and sixty eight trees �10 cm dbh

were counted in the 420 ha census, resulting in a

density of 1.35 trees ha�1. Basal area of the Brazil nut

population was 0.7615 m2 ha�1. The average distance

between these trees was 34.3 � 22.5 m (ranging from

1.0 to 233.0 m).

Based on application of Donnelly’s (1978) nearest-

neighbor method, the index of aggregation (R) for the

entire population was 0.77 (Table 1). This value

indicates a rejection of the null hypothesis of a strictly

random pattern of distribution across the 420 ha

landscape, yet suggests a much greater tendency

Table 1

Analysis of the spatial distribution patterns of Brazil nut trees in Colocac

Class Na Rb

Adults (dbh � 50 cm) 422 0.8199

Juveniles (dbh < 50 cm) 138 0.6528

Total 560c 0.7737

a Number of individual.b Index of aggregation [R = 1, if the spatial pattern is random; R = 0 wh

exists (Krebs, 1999)].c Eight of the total 568 individuals were not georeferenced, and thus, w

Table 2

Descriptive results of the characterization of 568 Brazil nut trees accordi

Diameter class N Crown positiona (%) Crown formb (%)

D CD I S Per G T Po

10 � dbh < 50 145 22 28 33 16 30 36 23 6

50 � dbh < 100 192 66 28 6 0 21 46 26 5

100 � dbh <150 182 87 13 0 0 14 49 31 4

dbh � 150 49 94 6 0 0 18 41 35 4

Total 568 64 21 11 4 21 44 28 5

a Crown position: D, dominant; CD, co-dominant; I, intermediate; S, sb Crown form: Per, perfect; G, good; T, tolerable; Po, poor; VP, very pc Percentage of the crown that was covered with vines.d Trunk vine load: 0 = no vines on trunk; 1 = at least one vine touchinge Reproductive individuals.

toward randomness than either clumping or unifor-

mity (Fig. 2). When analyzed separately, the spatial

distribution of reproductively mature adults versus

non-reproductive juveniles diverged slightly, such that

this disaggregated analysis demonstrated that juve-

niles tended to be slightly more clumped than adults

(Table 1).

5.2. Tree-level parameters

Average diameter of the 568 trees was

86.1 � 45.0 cm, ranging from 10.0 to 207.0 cm dbh

(Fig. 3). The smallest reproductively mature indivi-

dual was 32.4 cm dbh. The average 2002 nut pro-

duction of the 140 trees subsampled was 10.28 �1.25 kg tree�1. Descriptive statistics of grouped

diameter classes revealed that 8.6% of the trees had

diameters �150 cm dbh (Table 2). On the other end of

the diameter spectrum, 25.5% had a dbh < 50 cm.

This value, coupled with extractivist production

estimates, indicates that almost 1/4 of the population

(23%) was composed of non-reproductive juveniles.

¸ao Rio de Janeiro, RESEX Chico Mendes, Acre, Brazil

P Average distance between trees (m)

<0.0001 42.0 � 27.6

<0.0001 59.7 � 52.2

<0.0001 34.3 � 22.5

en clumping occurs; R > 2.15 when a uniform distribution pattern

ere not included in this analysis.

ng to diameter classes

Crown vine loadc (%) Trunk vine loadd (%) Repr.e

(%)VP 0 �25 25–75 >75 0 1

2 74 12 6 7 71 27 20

2 48 27 15 10 66 34 96

2 32 37 18 13 56 44 96

2 37 31 26 6 75 25 96

2 48 27 15 10 65 35 77

uppressed.

oor.

the trunk.


Fig. 2. Spatial distribution of 568 Brazil nut trees �10 cm dbh encountered on the 420 ha study area.

Descriptive statistics also revealed that almost all

trees >50 cm dbh had crown positions that were

considered either dominant or co-dominant; these

same trees also had higher levels of crown and trunk

vine loads when compared with the smallest grouped

diameter class (dbh < 50 cm). And finally, regardless

of tree diameter, crown form of most trees (83%)

ranged from tolerable to perfect, with few considered

to be poor (less than half-crown) or very poor (one or a

few branches) (Table 2).

Correlation coefficients of measured variables from

all Brazil nut trees (dbh � 10 cm) demonstrated

positive correlations between extractivist production

categories and dbh as well as crown form and position

(Table 3), such that the larger the diameter, the higher

the crown position, and the better the crown form, the

better the likelihood of greater nut production. In

contrast, a negative correlation between crown form

and vine loads was observed (Table 3).

Further analysis of the relationships between nut

production and other tree variables using maximum R2

improvement demonstrated that, of the total variance

explained (0.41), dbh was the most important variable

measured (Fig. 4, Column 1). When dividing the 568

trees into four discrete diameter classes to provide

insight into the dynamics of variables related to

production over the species life cycle, variable

importance changed with increased diameter classes

(Fig. 4, Columns 2–5). While dbh was important in

explaining 1/3 of production variance (R2 = 0.3360) in


Fig. 3. Diameter distribution of 568 Brazil nut trees �10 cm dbh.

the smallest diameter class (10 cm � dbh < 50 cm)

(those in the process of reaching reproductive maturity),

crown form was more important in explaining

productionvariance of very large trees (dbh � 100 cm).

The importance of the crown form variable was

also further explored through maximum R2 improve-

ment analysis. This analysis of crown form in the four

diameter classes revealed that, of the minimal crown

form variance explained, crown vine load was the

most important variable in all diameter classes

(Fig. 5). Nonetheless, its importance in explaining

crown form variance diminished greatly as tree

diameter increased.

Extractivist estimates of production were closely

aligned with measured production of the subsample of

140 reproductively mature adults (Fig. 6). The best fit

Table 3

Spearman’s correlation coefficients between measured variables for Brazil

Mendes, Acre, Brazil

Trunk vine load Crown vine load

Production 0.00 0.13**

Trunk vine load 0.58****

Crown vine load

Crown form

Crown position

* Significant differences at �0.05 level.** Significant differences at �0.01 level.

****Significant differences at �0.0001 level.

multiple linear regression model developed to test

extractivist nut production categorical estimates con-

tained the following variables: dbh, extractivist

production class, crown vine load, crown form, and

the crown vine load and form interaction term (Table 4).

Ultimately, only 114 of the subsample of 140 trees

measured for fruit production were utilized in this

particular analysis. The 26 trees that did not produce

any fruits in 2002 were eliminated from the model since

a clear pattern emerged when residuals of the non-

producing trees were plotted against their predicted

values. The extractivist production class variable,

representing the extractivist production estimates,

was the most useful variable in predicting nut

production levels (P < 0.0001) within the model, with

dbh also being a very good predictor (P = 0.0001).

nut trees � 10 cm dbh in Colocacao Rio de Janeiro, RESEX Chico

Crown form Crown position dbh

0.12** 0.51**** 0.61****

�0.23**** 0.10* 0.08

�0.31**** 0.24**** 0.27****

0.04 �0.10*

0.61****


Fig. 4. Analysis of the measured variables (crown vine load, trunk vine load, crown position, crown form, and dbh) that best explained nut

production variance. The maximum R2 improvement method of multiple regression model development was applied to all trees in the study area

(first column to the left) and to four diameter classes that were considered a surrogate for age and were constructed to facilitate exploration of

changes in variable importance over the life cycle of the species.

Fig. 5. Analysis of the measured variables (dbh, trunk vine load, crown position, and crown vine load) that best explained crown form variance.

The maximum R2 improvement method of multiple regression model development was applied to four diameter classes that were considered a

surrogate for age and were constructed to facilitate exploration of changes in variable importance over the life cycle of the species.


Fig. 6. Scatter plot of 2002 production of 140 reproductively mature

Brazil nut trees by diameter. Measured production in latas (�11 kg)

is compared to extractivist production estimates based on his

recollection from the previous 5-year collection period.

Table 4

Analysis of variance (ANOVA) of the multiple linear regression

model developed to test extractivist nut production class estimates of

all trees (dbh � 50 cm) with measured production data from a

subsample of 114 trees

Source of variation d.f. MS F P

Dbh 1 3.40 15.92 0.0001

Production class estimate 3 4.16 19.49 <0.0001

Crown vine load 3 0.70 3.28 0.0242

Crown form 4 0.80 3.76 0.0070

Crown vine load � crown form 8 0.42 1.97 0.0589

Whole model error 93 0.21

All model variables and interactions were tested for significance

against the model error MS.

6. Discussion

6.1. Spatial distribution, density, and size–class

structure

Few studies have examined the spatial distribution

of Brazil nut, though much literature about the species

asserts that it is found on terra firme in concentrated

groves (known as manchales or castanales in Spanish

and castanhais in Portuguese), often separated by

kilometers (Muller et al., 1980; Mori and Prance,

1990; Salomao, 1991; Peres and Baider, 1997). Peres

and Baider (1997) provide the only known quantified

report of Brazil nut distribution. They spot mapped

trees �10 cm dbh in two such groves slightly larger

than 25 ha, and using Morisita’s index, concluded that

spatial distribution was random within the groves,

with densities of 4.8 and 5.1 trees ha�1, respectively.

They also counted Brazil nut trees (apparently

�10 cm dbh) detectable from two trails of 3300

and 2300 m that ran north and south of a river. When

dividing these trails into 100 m segments, 13 of the 56

segments contained all Brazil nut trees detected.

Based on these data, apparently coupled with

observations made over several months in the larger

950 ha study area, the authors concluded that Brazil

nut trees were clumped at the wider landscape level.

In our study, we quantitatively evaluated an area of

420 ha, and our data do not show the existence of

groves. Although our nearest-neighbor analysis

resulted in rejection of a strictly random distribution,

it also clearly demonstrated a much closer approx-

imation to randomness than any type of clumped

distribution, especially when isolating reproductively

mature adults (Table 1). Furthermore, Brazil nut data

we collected on neighboring landholdings totaling

559 ha, using similar GPS-based mapping but not

reported in this study, showed very comparable

distributions to that encountered in the 420 ha of

our study site. Nor have we detected a pattern of

distinct Brazil nut groves separated by large distances

through more casual observations of other forests

within the same Rio Acre Valley watershed.

How might these and other reported differences in

Brazil nut population structure be explained? Some

differences are likely due to the application of

dissimilar methodologies. For example, Brazil nut

densities (�10 cm dbh) reported in the literature vary

widely from 1.3 to 23.0 individuals ha�1 (Salomao,

1991; Peres and Baider, 1997; Peres et al., 2003). The

disparity in these values can be partially attributed to

differences in area sampled (from 3 to 1350 ha),

sampling strategies (transects and plots of varying

lengths and sizes as well as full inventories), and

whether the researchers installed their study within a

previously defined Brazil nut grove or located it

randomly across the landscape.

Grove versus more scattered distribution patterns

may also be partially due to differences in forest types

in which Brazil nut naturally occurs. Our study site is

located within an open forest landscape (ZEE, 2000;


INPE, 2002); perhaps open forest provides more

consistent favorable regeneration conditions, such as

higher light levels, resulting in a more scattered spatial

distribution pattern and more constant seedling

establishment over time. In contrast, in dense forests

where the canopy is high and often continuous with

higher basal areas (�40 m2 ha�1) and relatively open

understory, few grasses, shrubs, and vines (Daly and

Mitchell, 2000), Brazil nut may be more dependent

upon gap formation for sufficient light levels, as

suggested by Salomao (1991), resulting in more

spatially- and temporally-clumped distribution.

Indeed, the grove distribution literature was born

out of early Brazil nut research carried out almost

exclusively in Eastern Amazonia where dense forest is

more the norm (INPE, 2002).

In our open forest research site, we found relatively

high counts of smaller-diameter juveniles scattered

across the 420 ha that would support this hypothesis

that differences in diameter distributions reflect

differences among forest types. The large number

of juveniles is particularly significant because it is in

these smaller diameter classes that one would expect

underestimates of individual tree counts given the

greater difficulty in observing smaller-diameter trees

with the censusing method employed in this study.

Similar size–class distribution patterns were reported

by other demographic studies in open forests in

Southwestern Amazonia (Viana et al., 1998; Zuidema

and Boot, 2002).

Human interventions have also been implicated in

explaining Brazil nut spatial distribution. In our study

region, extractivists consistently describe greater

levels of Brazil nut regeneration in capoeira (fallow

forests of varying ages resulting from the traditional

shifting cultivation cycle) than in undisturbed forest.

Other reports suggest that the presence of ‘‘groves’’

provide evidence of anthropogenic forests, hypothe-

tically due to pre-Colombian human interventions

(Tupiassu and Oliveira, 1969, cited in Muller et al.,

1980; Posey, 1985; Balee, 1989).

Historic collection intensities have been found to

impact Brazil nut size–class structure. In a study of 23

Brazil nut populations (including data from our study

site) across the Amazon basin, Peres et al. (2003)

presented evidence that history and intensity of Brazil

nut exploitation were the two major determinants of

population size structure such that those populations

where nuts were collected intensively for decades had

fewer juveniles and were dominated by cohorts of

large trees. And while harvest intensity was the

parameter that most consistently explained differences

in population size structure, other variables such as

abundance of agoutis (main Brazil nut seed disperser

and predator) were also implicated.

6.2. Nut production

Nut production of Bertholletia is the single most

important ecological variable of economic interest.

For many extractivists, Brazil nut is the principal cash

crop in sustaining their livelihoods. This strong

economic relationship between Brazil nut and extra-

ctivists is illustrated in this study by the marked ability

of the resident extractivist to successfully categorize

nut production of each tree censused, as demonstrated

by the best fit multiple linear regression model.

Accordingly, in this preliminary stand characteriza-

tion study, we sought specifically to better understand

the relationships between Brazil nut production and

other ecological factors, including those that could be

manipulated for increasing nut production of existing

individuals.

Similar to findings of Viana et al. (1998) and

Zuidema and Boot (2002), production in our study was

most highly correlated with tree diameter (r = 0.61,

P � 0.0001) and crown position (r = 0.51, P �0.0001). While subsequent R2 improvement analysis

provided further insight into the relative importance of

ecological variables to production variance over the

life cycle of the species, much of the variance found

was not explained by the variables we measured. Still,

in analyzing the size–class of trees that includes

juveniles and those attaining reproductive maturity

(10 cm � dbh < 50 cm), tree diameter (probably a

relatively good proxy for reproductive maturity within

this size–class) explained 1/3 of production variance

(R2 = 0.3360). Diameter (or any other measured

variable) explained only a small portion of produc-

tion variance in the subsequent diameter category

(50 � dbh < 100 cm).

As these young Brazil nut adults move into larger

diameter classes, slightly more production variance

was explained by crown variables. Crown form in the

100 cm � dbh < 150 cm category, and both crown

form and position in the largest diameter category


(dbh � 150 cm) explained the most production var-

iance, albeit only a little over 20% (R2 = 0.2094 and

0.2281, respectively). At this latter stage, once trees

are well established, crown size and form becomes less

uniform (Oliver and Larson, 1996), and these crown

differences may play a relatively larger role in

production variance. Zuidema (2003) also notes that

a crown variable (in his case, crown area) was a better

predictor of fruit production than dbh based on

individual tree production data for 40 trees �40 cm

dbh in Bolivia.

Regarding the direct relationship between produc-

tion and crown vine load, the correlation data for the

entire population demonstrated that crown vine load

was positively, rather than negatively, correlated with

production (r = 0.13, P = 0.002) (Table 3). This

counterintuitive result (more vines, more production)

can be explained when honing in on the smallest

diameter class (10 cm � dbh < 50 cm) (Table 2).

While this class contained a high number of

individuals completely free of vines in the crown

(74%), this class also included very few individuals

that were reproductively mature (20%). And those that

were producing, produced little, such that few vines in

the crown statistically correlated with minimal

production. To test this confounding correlation

between small trees and reduced vine load, we

conducted a second correlation analysis after elim-

inating those trees in the smallest dbh class. Results

demonstrated a significant negative (though relatively

weak) correlation between crown vine load and

production of trees �50 cm dbh (r = �0.13, P =

0.008), suggesting that crown vine load negatively

affects nut production of reproductively mature

individuals. Thus, we conclude that overall stand

production may be negatively affected when indivi-

dual trees have higher crown vine loads. Mechanisms

by which vines affect production was not directly

studied, and it would be interesting to explore the

relationships between vine load and modification of

crown structure, impediments to flower and leaf

development, and the pollination process.

6.3. Guiding management

The general assumption behind this study is that

there is a need for greater scientific knowledge to

inform a transition from a traditional pattern of

exploitation of Brazil nut to more conscious manage-

ment of the species. The results of this current study

provide some guidance for advancing such a transition

for the natural Brazil nut stands within Acre’s

extractive reserves as well as other Brazil nut-rich

forests. First, in the short term, as was found by

Arredondo and Zonta (2000), our results suggest that

vine cutting may be an appropriate silvicultural

treatment for enhancing Brazil nut yields in this

region, and merits further study. Anecdotal reports by

local extractivists also support this conjecture. In

addition, the only known study that quantified a fruit

production–vine load relationship in a tropical tree

species demonstrated a negative relationship (Stevens,

1987).

Secondly, our data contribute to a more thorough

understanding of Brazil nut population structure.

Although, we did not quantify individuals <10 cm

dbh, our data tend to support Zuidema and Boot’s

(2002) findings from a study in nearby Bolivia,

suggesting viable populations and adequate recruit-

ment for species maintenance. Still, this cannot be

fully determined without analyses of population

viability, including rates of important demographic

processes such as survival, recruitment, growth, and

reproduction (Peters, 1996; Morris and Doak, 2002),

and an ongoing system for monitoring and fine-tuning

ecologically sustainable Brazil nut harvest levels is

warranted. Ultimately, sound management of this

ecologically and economically important NTFP could

increase the value of this species, perhaps reducing the

pressure to convert Brazil nut-rich areas to other types

of land use less amenable to protecting the array of

conservation values attributed to contiguous tropical

forests.

Acknowledgements

This research was supported by the Florida

Agricultural Experiment Station and grants from

FINEP/MCT/CNPq in Brazil, The William and Flora

Hewlett Foundation in the U.S., and the International

Science Foundation, Stockholm, Sweden through a

grant to Dr. Wadt, and approved for publication as

Journal Series No. R-10760. Embrapa Acre also fully

supported this research, and CNPT/IBAMA in Brazil

gave permission to conduct the research in RESEX


Chico Mendes. We also thank Paulo Rodrigues de

Carvalho for his superior assistance in the field,

Francisco Carlos Gomes for generation of software for

nearest neighbor calculations, Rodrigo Otavio Perea

Serrano for crafting the maps herein, and Ramon

Littell and Marinela Capanu for their much-appre-

ciated statistical counsel. Our thanks also to Pieter

Zuidema, Christie Klimas, Cara Rockwell, and two

anonymous reviewers for providing helpful comments

on earlier manuscript drafts. Finally, we are most

grateful to Valderi and Maria Alzenira for so

graciously sharing their home and extractivist insights.

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Documents

Population structure and nut yield of a Bertholletia excelsa stand in Southwestern Amazonia