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This is to certify that the

dissertation entitled Undisturbed Short-term Growth of an in the Brazilian Tropical Moist Forest Amazon presented by

Niro Higuchi

has been accepted towards fulfillment of the requirements for

PhD degreein Forestry

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SHORT-TERM GROWTH OF AN UNDISTURBED TROPICAL MOIST FOREST

IN THE BRAZILIAN AMAZON

BY

Niro Higuchi

A DISSERTATION

Submitted to

Michigan State University

in partial fulfillment of the requirements

for the degree of

DOCTOR OF PHILOSOPHY

Department of Forestry

1987 IHEHRACT

SHORT-TERM GROWTH or AN uuoxsruaaeo TROPICAL uorsr FOREST IN THE BRAZILIAN amazon. by NIRO HIGUCHI

The main objective of this study is to provide basic

information for sustained yield management of the tropical

moist forest in the Brazilian Amazon. This was done by

quantification of diameter distributions, projections of

Idiameter distributions and of tree mortality, and by

development of short-term growth and yield models.

The data for this study were collected from an

undisturbed stand located some 90 kilometers north of

Manaus, the capital of Amazonas State - Brazil. Three

permanent plots were established in 1980 and remeasured in

1985. They are the control plots of a forest management

experiment randomly replicated within an area of about 2,000

hectares of pristine Amazonian forest. In each 4-hectare

plot (200 by 200 meters), all trees with dbh 25 cm or

greater were tagged and their dbhfls were recorded in 1980.

In 1985. all tagged trees were remeasured with special

attention to ingrowth and mortalityu The number of trees,

dbh and basal area of the study area averaged 158 trees/ha.

ii 38 cm, and 20 mZ/ha, respectively - in 1980.

Among three different hypothesized diameter distribution functions, the Weibull using the percentile approach best fit the observed data. 3

Since age and successive records from long-term permanent plots were not available, the first-order Markov chain approach was used to project diameter distribution and tree mortality and proved to be a realistic alternative.

The exponential Lotka growth model was adapted to predict future volume as an alternative for the traditional growth and yield models, and it behaved satisfactorily. The volume for 1990 was also predicted by models based upon the volume estimated in 1985 in relation to the dbh measured in

1980. There was a strong correlation between actual volume and past dbh, but not between past diameter and diameter growth.

iii To

Inezita and Chico, and Maria my children, my wife - my friends

iv ACKNOWLEDGEMENTS

I wish to express my gratitudetto Dr. Carl W. Ramm.

Chairman of my dissertation committee, for his insight, support, and guidance in the preparation of this work. I also wish to extend my gratitude to Dr. Lee M. James, Dr.

Kurt S. Pregitzer and Dr. Peter G. Murphy for serving on my guidance committee and assisting throughout my doctoral program.

I would like to extend my acknowledgements to Dr. Phu

Nguyen, Mr. T. W. W. Wood, Dr. Jurandyr C. Alencar, Dr. Kurt

S. Pregitzer, Dr. Lee M. James, and Dr. Carl W. Ramm. They provided helpful suggestions on an earlier version of specific chapters of this manuscript .

I would like to pay special tribute to my wife, my kids and my "pessoal" from Chavantes and Chapeco for their encouragement, patience and supportive "rezas".

I am indebted to many people whose friendship was

important during the course of this voluntary exile. Thank- you to Antonio & Lucia, Josmar & Fernanda, Carlos, Steve

Westin, Robert De Geus, Bill Cole, Don Zak and George Host.

I would like to express my sincere gratitude to Luis

Maurc>& Fatima Magalhaes for being my proxy in Brazil and

for their patient support during this time. Special thanks are due to the "peaozada" of DST

(Departamento de Silvicultura Tropical)-Aluizhm5Cabore,

Jesus, Barrao, Caroco, Paulista, Armando and other anonymous helpers - who have been my great masters in the forest and particularly for their help during field data collection. I also wish to thank the group of DST's "pica-pan" - Fernando,

Antenor, Jurandyr, Magalhaes, Benedito, Noeli and Joaquim - who played an important role during the preparation of this

research project. I am also indebted to many people from other departments of INPA for their support. Thank-you to

"turma" of administration and to Nakamura.

I gratefully acknowledge the support of many staff members of the Department of Forestry of Michigan State

University.

Finally, my sincere appreciation to my country - Brasil

- through CNPq (Conselho Nacional de Desenvolvimento

Cientifico e Tecnologi'co) for financial and administrative

support, and INPA (Instituto Nacional de Pesquisas da

Amazonia) for inspiration.

THANK YOU GOD !

vi TABLE OF CONTENTS

page

TITLE . .

ABSTRACT ii

DEDICATION iv

ACKNOWLEDG EMENT S O O O C

TABLE OF C ONTENTS O O O 0 vii

LIST OF TABLES . . . . .

LIST OF FI GURES O O O O xiii

CHAPTERS

1. INTRODU CTION O O O O O

Scepe of the Problem

Statement of the Problem . . . .

2. LITERATURE REVIEW ON THE MANAGEMENT OF NATURAL

REGENERATION IN THE TROPICAL MOIST FORESTS

2.1. Overview . . .

2.2. Introduction . .

2.3. Tropical America 03010101

2.4. Tropical Africa 12

2.5. Tropical Asia . 13

2.5. Tropical South Pacific . . . 16

2.7. Conclusion . . . 16

vii 3. THE BRAZILIAN AMAZON ...... 20

3.1. Introduction ...... 20

3.2. Climate ...... 21

3.3. Soils ...... 23

3.4. Vegetation ...... 25

Tropical moist forest on "terra firme" 27

Inundated forests ...... 30

"Campina" and "Campinarana" . . . 32

Tropical semi-evergreen forest . . 34

"Cerrado" (Savannas) ...... 35

4. DESCRIPTION OF THE STUDY AREA ...... 39

5. MODELLING THE DIAMETER DISTRIBUTION OF AN

UNDISTURBED FOREST STAND IN THE BRAZILIAN

AMAZON TROPICAL MOIST FOREST: WEIBULL VERSUS

EXPONENTIAL DISTRIBUTION ...... 47

5.1. Introduction ...... 47

5.2. Procedures ...... 48

The data ...... 48

The diameter distribution functions 49

The application of the functions . 52

5.3. Discussion of Results ...... S3

5.4. Conclusion ...... 55

6. A MARKOV CHAIN APPROACH TO PREDICT MORTALITY AND

DIAMETER DISTRIBUTION IN THE BRAZILIAN AMAZON .

6.1. Introduction ...... 68

6. 2. PrOCEdures O O O O O O O O O O O O 71 The Data ...... 71

The Markov model ...... 71

The application of the model ...... 73

6.3. Discussion of Results ...... 75

6.4. Conclusion ...... 76

7. SHORT-TERM GROWTH OF UNDISTURBED BRAZILIAN

AMAZON TROPICAL MOIST FOREST OF "TERRA FIRME" . . 92

7.1. Introduction ...... 92

7.2. Procedures ...... 93

The Data ...... 94

Model Development ...... 95

7.3. Discussion of Results ...... 97

7.4. Conclusion ...... 102

8 0 CONCLUSIONS 0 O O O O O O O O O O O O O O I O O O 110

APPENDIX 0 O O O O O O O O O O O O O O O O O I O O O 113

L I ST OF REFERENCES 0 O O O C O O O O O O O O O O O 1 2 2

ix LIST OF TABLES

Page

2.1. 1961 version of TSS - summary of operations . . 18

2.2. Malayan Uniform System (MUS) - summary of

activities ...... 19

4.1. Listed species for the NR management

project ...... 44

5.1. Diameter (cm) descriptive statistics for the

study area ...... 56

5.2. Parameter estimates used for diameter

distribution - hectare basis ...... 57

5.3. Diameter distribution for all 3 sample plots

together (Bacia 3) derived from 3 different

methods ...... 58

5.4. Diameter distribution for Bloco 1 derived from

3 different methods ...... 59

5.5. Diameter distribution for Bloco 2 derived from

3 different methods ...... 60

5.6. Diameter distribution for Bloco 4 derived from

3 different methods ...... 61

6.1. Bloco 1 - Transition between states during a

5-year period ...... 78

6.2. Bloco 2 - Transition between states during a 5-year period ...... '...... 79

6.3. Bloco 4 - Transition between states during a 5-year period ...... 80

6.4. Bloco 1 - Transition probability matrix from

one state to another during a 5-year period . . 81

6.5. Bloco 2 - Transition probability matrix from

one state to another during a S-year period . . 82

6.6. Bloco 4 - Transition probability matrix from

one state to another during a 5-year period . . 83

6.7. Bloco 1 Two-step transition probability

matrix ...... 84

6.8. Bloco 2 - Two-step transition probability

matrix ...... 85

6.9. Bloco 4 - Two-step transition probability

matrix ...... 86

6.10. Bloco l - Projection for 1990 ...... 87

6.11. Bloco 2 - Projection for 1990 ...... 88

6.12. Bloco 4 - Projection for 1990 ...... 89

6.13. Summary of one-step transition probability

matrix (1985) ...... 90

6.14. Summary of two-step transition probability

matrix - projection for 1990 ...... 91

7.1. Basic distributional characteristics of the

data used for individual volume regression

equations ...... 104

7.2. Regression summary for volume estimation

models ...... 105

7.3. Characteristics of the data used as yield information and yield prediction ...... 106

xi 7.4. The frequency distribution of the three

dominant families by status in 1980, mortality

and ingrowth, and by periodic increment

classes in cm ...... 107

7.5. Regression summary for increment models . . . . 108

7.6. Mean, standard deviation, minimum and maximum

for each (a) dbh classes and (b) increment

Classes 0 O O O O O O O O O O O I O O O O O O O 109

xii LIST OF FIGURES

Page

Index map for the Brazilian Amazon

vegetation map ...... 37

3.2. The vegetation of Brazilian Amazon . . 38

4.1. "Ecological Management" Project area . 45

4.2. Bacia 3 with 4 experimental blocks . . 46

5.1. Bacia 3 - The relationship between the

observed and estimated dbh frequencies,

the Weibull MLE function ...... 62

Bacia 3 - The relationship between the

observed and estimated dbh frequencies,

the Weibull PERC function ...... 63

5.3. Bacia 3 - The relationship between the

observed and estimated dbh frequencies,

Exponential function ...... 64

Bloco l - The relationship between the observed

and estimated dbh frequencies by Exponential,

Weibull PERC, and Weibull MLE . . . . . 65

5.5. Bloco 2 - The relationship between the observed

and estimated dbh frequencies by Exponential,

Weibull PERC, and Weibull MLE . . . . . 66

5.6. Bloco 4 - The relationship between the observed

and estimated dbh frequencies by Exponential,

Weibull PERC, and Weibull MLE . . . . . 67

xiii CHAPTER 1

INTRODUCTION

During the past twenty years, the future of tropical forests has been a matter of international concern.

Comprehensive reviews and evaluations are found in Gomez-

Pompa et a1. (1972), Budowski (1976a), Leslie (1977), Brunig

(1977), Spears (1979), Myers (1982), Myers (1983), Sedjo and

Clawson (1983), and Lanly (1983). The discussion is polemic and most concerned scientists have been very pessimistic about the future of tropical forests, especially tropical moist forest (TMF). Nevertheless, there is one facet which all the diverse approaches share to some extent: the TMF ecosystem is very complex and fragile. Therefore, more studies are required for a full understanding and definition of its role for the region.

In terms of forest management of TMF, sustained yield based on natural regeneration (NR) has been recommended by most scientists. Nevertheless, success with this silvicultural system is uncommon (Budowski 1976a).

While scientists and technicians are discussing the problems of managing the tropical forest, about 20 hectares per minute - an area equivalent to Puerto Rico per month - of tropical forest are being deforestated, according to Murphy (pers. comnh). Myers (1982) pointed out that the principal causes of depauperation and depletion of TM? are timber harvesting followed by slash-and-burn agriculture in

Southeast Asia, shifting cultivation in Africa, and cattle ranching in Latin America.

In the Brazilian Amazon TMF, about 8 million hectares

(approximately 2% of the total area) have been deforestated for the sake of agriculture and cattle ranching programs. By the end of this century more than 2 million hectares will be replaced by artificial lakes for energy generation. In addition, areas open to mineral exploration have also increased significantly.

In the face of increasing pressure on the definition of role and vocation of the Brazilian Amazon TMF, in 1979 the

Federal Government made a commitment to develop a forest policy for the region. All Amazonian research and educational institutions were engaged to support this policy. In the State of Amazonas two documents were produced at the same time, one by the University of Amazonas (EUA

1979) and another by the National Institute for Research in the Amazon (INPA 1979).

Inspired by the worldwide concern on the use of TM? and forest policy, INPA initiated a research project. The project, entitled "Ecological Management of Dry-land (terra- firme)‘Tropical Moist Forest” was approved in 1979 by the

Brazilian Federal Government. It was financed by INPA, the

Interamerican Development Bank and FINEP (Brazilian Financial Agency for Research). The main objective of this project was to evaluate the impact of forest management practices on the local environment. Basic ecosystem research began in 1976 and the preparation for forest management experiment effectively began in 1980.

This dissertation is based on observations of the forest management area over a 5-year period. Only trees with diameter at breast height (dbh) of 25 cm or greater were observed. This study was conceived to provide biological basis for sustained yield management based upon natural regeneration development.

ScoEe of the Problem

The Ecological Management project is as important to the Brazilian Amazon as the Hubbard Brook Ecosystem Study has been to the mixed-species forest ecosystem of

Northeastern United States.

In the 2,000-hectare project area, two major research studies have been carried out on ecology and forest management. The areas for each study are referred to as

"Bacia 1" or "Bacia Modelo" and "Bacia 3", respectively for ecology studies and forest management experimentation.

The initial results of "Bacia Modelo", including a collection of basic ecology research results, were documented by INPA in 1982 (INPA 1982). "Bacia 3" is the area involved in this dissertation. The results of this work will be used to help decision makers in prescribing silvicultural treatments for an experimental area subjected to a commercial timber harvesting.

Statement 9; the Problem

The present study will investigate three separate topics: quantification of diameter (dbh) distributions, projections of dbh distributions and of tree mortality, and development of short-term growth and yield models for natural unmanaged Amazonian forest.

The specific objective of the diameter distribution study is to find out which distribution function best fits the observed data. Three hypothesized models were compared:

Weibull by percentile approach, Weibull by maximum likelihood approach, and the exponential distribution

functions.

The second objective is to test the possibility of using the first-order Markov chain approach to project diameter distributions and to estimate tree mortality.

The third objective is to explore alternative ways to

model an undisturbed sample of TMF; Besides classical growth

and yield models, the Lotka's exponential model was tested. CHAPTER 2

THE MANAGEMENT OF NATURAL REGENERATION IN THE TROPICAL

MOIST FORESTS.

2.1. OVERVIEW

This chapter reviews the management of tropical moist

forests (TMF) using natural regeneration, with or without classical silvicultural systems. A diagnosis of the recent

situation of the application and research on natural

regeneration management, discussion of methods used in some countries, and perspectives of sustained yield management using natural regeneration are presented.

2.2. INTRODUCTION

There is no doubt of the importance of natural

regeneration for the management of TMF's. Very little is

known of the response of these forests when subjected to

intensive timber-oriented management used in temperate

regions (Cheah 1978, Tang 1980, and Rio 1976). Without

exception, all countries which contain TMF are still

considered as "developing" or "less developed" countries

(iJL, a mean GNP/capita about 10% of the North American

GNP). Another common characteristic of these countries is

the complex floristic composition of their predominantly broadleaf evergreen forests.

Historically, natural regeneration management on a sustained basis began with the Malayan Uniform System (MUS) in Malaysia and the Tropical Shelterwood System (TSS) in

Nigeria (Fox 1976L.These two systems, modified and improved with the passage of time and experience, have been used extensively'in most tropical countries.lk>be meaningful, natural regeneration management must be regarded as a continuous process of silvicultural treatments to favor economically desirable species. According to Rio (1979), the objective of most treatments is the perpetuation of the existing stands by the replacement of exploited forests without a profound alteration of the characteristic structure of the forest.

This review divides the tropical world into tropical

America, tropical Africa, tropical Asia, and the tropical south Pacific. The current situation of natural regeneration management and its perspectives are presented separately for each region.‘The term tropical moist forest is based on the Holdridge classification: biotemperature above 24° C and annual precipitation between 2,000 and 4,000 mm.

2.3. TROPICAL AMERICA

According to Budowski (1976b), there is no example of mixed TMF in the American tropics being managed on a sustained yield basis.

In terms of research, however, Brazil (since 1980) and Suriname (since 1967) have commenced studies to test the possibility of TMF management on a sustained yield basis using natural regeneration. Venezuela started a similar project in the middle of the 19703, but no progress beyond initial field establishment of the experiment and the collection of pre-harvesting data was made. Recently Peru also entered the natural regeneration management era. With the assistance of British and Canadian technical aid programs, the Honduran Forest Service will initiate studies

into sustained yield management of the TMF resources using natural regeneration (Wood, pers. comnn). In Costa Rica, sustained yield management has been planned for the Nosara and Parrita river basins, with the assistance of FAO (Food and Agriculture Organization) (Wood, pers. comm.). In

Dominica, between 1968 to 1972, an area of approximately 60 hectares was logged and planted with desirable tree species.

After about 3 years it was found that this operation was very expensive to maintain, mainly due to the vigorous growth of climbers. Therefore, the option with natural

regeneration management was considered (Bell 1976). In

Puerto Rico, a timber-management plan was completed in 1966

(Wadsworth 1970). This plan consisted of natural

regeneration treatments of 2,700 ha during the next 4 decades.

(a) BRAZIL

The concept of managing the native forests under a system of sustained yield was introduced by FAO experts in

1958 in Santarem (State of Para) through an agreement with the Brazilian Government. In Manaus (State of Amazonas), researchers at INPA in 1964 initiated studies on enrichment of natural forests, phenology'of tree species, and nursery and plantation management of native and exotic species

(Higuchi 1981a).

In Santarem natural regeneration research was first carried out fortuitously in 1960 when, after an area was burned for species trial site preparation, copious regeneration of Goupia glabra Aubl. appeared spontaneously.

This area is still under observation by researchers of

EMBRAPA (Brazilian Enterprise for Agricultural and Animal

Husbandry Research) and SUDAM (Superintendency of

Development of the Amazon region). Today, besides Goupia glabra Aubl., species such as Vochysia maxima Ducke,

Didymopanax morototoni (Aubl.) Decne & P1anch., Manilkara

hubggi (Ducke) Standl., and Simaruba amara Aubl. are

abundant in an adjacent area.

Recent work with natural regeneration management in

Santarem is being carried out over blocks of 100 hectares.

The forest is harvested with diameter limits of 45 and 55 cm dbh for commercial species after climber cutting and underbrushing. The objectives of this project are to determine the effects of different levels of harvest

intensity on the residual stand and regeneration, and to

evaluate the growth and yield under natural regeneration management.

In Manaus the research with natural regeneration management effectively started in 1980 under an agreement among INPA (National Institute for Research in the Amazon), the Interamerican Development Bank, and FINEP (Brazilian

Financial Agency for Research). The main objective of this investigation was to test the possibility of managing the

TMF of the region under a system of natural regeneration. A second objective was to use theidata to determine felling cycles along with forecasts of yields by species. Within the experimental blocks (400 by 600 meters), harvesting will be carried out as the main silvicultural treatment. In designated sub-blocks (200 by 200 m) felling intensities will be applied to remove various levels of the basal area of some 40 listed species, 25 cm dbh and above. This project is based on multidisciplinary research involving all departments of INPA (Ecology, Botany, Wood Technology,

Pathology, Agriculture, Chemistry and Zoology), which will give scientific support to the Department of Tropical

Silviculture. The total area of this project is about 2,000 hectares while the area for silvicultural experimentation is

96 hectares, comprising 4 separate blocks of 24 hectares each.

The treatments to be randomized in each block are: (1) control; (2) removal of 25% of exploitable basal area

(b.a.); (3) removal of 50% of exploitable b.a.; (4) removal of 75% of exploitable b.a.; (5) removal of 100% of 10

exploitable b.a.; and (6) removal of 50% of exploitable b.a. with enrichment. In each 2 hectare plot a 1 hectare (100 by

100m) permanent sample plot will be established, in which the following studies will be carried out: growth of the

residual stand of listed species; recruitment and development of seedlings of the listed species; survival and growth of listed species; growth and mortality of poles and saplings; and studies of increment to determine felling cycles.

(b) SURINAME

Research into the management of TMF resources was

initiated by the Suriname Forest Service in the 19508. The

Malayan Uniform System was used but it was discontinued in

the early 19603 due to the high costs of silvicultural

treatments, the long rotation (70-80 years), and the lack of

species with the silvicultural characteristics of the

dipterocarps of SE Asia.

The need for a management system suited to the

conditions of Suriname was met in 1967 under the auspices of

the CELOS (Center for Agriculture Research in Suriname). Its

objectives were to find an economically and technically

feasible method to stimulate the valuable timber species

increment after a light harvesting, to improve the

regeneration of the valuable species, and to build a forest

with sustained yield. Here, light harvesting meant the 11 removal of some 30 trees from the 25-ha experimental area.

Besides the classic silvicultural treatments, a refinement was used wherein all non-valuable trees (non-commercial species) were killed with arboricide (2,4,5-T ester, 5% solution in diesel oil) using a‘diameter limit of 20 to 40 cm.

In this experiment the liberation treatments were: (1) elimination of competing lianas and non-valuable trees around the leading desirable tree selected on an area of 5 by 5 m; (2) elimination of competing species around the desired species with a diameter criterion (3 to 5 cm dbh), disregarding the location of the selected trees: and (3) elimination of competing species around the desired species in a strip 2 m wide, spaced 12.5 m apart, to provide accessibility.

In the sampling area (16 ha), over 1,000 valuable trees larger than 15 cm dbh are being measured yearly. Smaller valuable trees are recorded in a 17.5% subsample using 40 circular plots of 1,000 sq.m each. As a provisional result, de Graaf (1981) reported that the annual volume increment is

2.1 cu.m./ha for valuable trees above 15 cm dbh. According to Johnson (1976), the mean annual growth of the TMF's is about 1 to 3 cu.m./ha in South East Asia, 2 cu.m./ha in

Nigeria, and 2.9 to 4.3 cu.m./ha in the Philippines

(Dipterocarp forest). Even though there is not too much detail in terms of tree size, in a general sense the forests in Suriname are showing almost the same response to the 12

natural regeneration management as reported elsewhere.

2.4. TROPICAL AFRICA

According to Lowe (1978), the tropical shelterwood

system (TSS) was a major management preoccupation in Nigeria during the 19503. Altogether about 200,000 ha of forest land

were treated under this system. It was intended to obtain

sustained or improved yields. The TSS consists of canopy

opening to promote survival and growth of seedlings of

valuable species. This system has been changed and improved

since its introduction, and the last version of TSS in 1961

is presented in Table 2.1.

However, TSS has been abandoned in Nigeria, primarily

on the economical grounds that it did not make sufficiently

intensive use of the land to compete with other forms of

land use (Lowe 1978). Nevertheless Rio (1976) pointed out

that economically, TSS is more profitable than plantations

if the analysis is correctly applied without bias. He

related that too often the forest management analyst seems

to survey the list of variables and select only those that

will contribute positively to the desired end. It seems

certain that silvicultural arguments did not contribute to

the abandonment of TSS in Nigeria.

In Ghana the TSS was tried on an experimental scale

between 1948 to 1960. It was found to be unsuitable because

of the high maintenance costs and was abandoned (Britwunn

1976). According to this author, the selection system was 13

found to be suitable for Ghana forests although it induced only moderate regeneration. The treatments for this system were: (a) stock survey to map all economic trees with dbh >

66 cm; (b) weeding, cutting and poisoning all climbers and worthless trees which interfere with the development of young economic trees (10 < dbh < 47 cm); and (c) selection of trees to be felled from stock maps.

2.5. TROPICAL ASIA

A common characteristic in this region is the significant presence of species of the Dipterocarpaceae.

This family contains the most important tropical hardwood

timber species. Other important species also occur in this

region, exp, teak (Tectona grandis) in Burma, teak and

Pinus merkusii in Thailand, Pinus kesiya in the Phillipines,

and Pinus merkusii in Indonesia.

According to Tang (1980), natural regeneration is the basis for the regeneration of TMF in the region. The

silvicultural systems which have been developed for this

region are the Philippine Selective Logging System and the

Indonesian Selection Felling System for advanced growth, and

the Malayan Uniform System (MUS) and Indonesian modified MUS

- for seedling regeneration. Table 2.2 presents the sequence

of activities necessary for the MUS.

The MUS is, in fact, the most popular silvicultural

system in tropical Asia. It is mainly used in lowland l4

Dipterocarp forests when.adequate reproduction.i3 already established. There are restrictions in applying it in hill forests where enrichment planting is often necessary (de

Graaf 1981L.In West Malaysia about 300,000 hectares have been managed with MUS up to 1976.

Cheah (1978) discussed the differences between the new selective felling system and the MUS or the modified MUS. He determined that the first one is more appropriate for dipterocarp forests in Peninsular Malaysia. The selective felling system is a modification of the MUS, consisting of the MUS plus the following operations: pre-felling inventory which includes all trees below and above 15 cm, climber cutting, and marking of residual trees for retention.

In Sarawak, the liberation thinning system was introduced in 1975 by the Forest Department to evaluate the effects of different intensities of reduction of stand basal area as an alternative way to manage the natural regeneration (Higuchi 1981b). This system seeks to eliminate only trees which restrain the growth of a selected tree

(Hutchinson 1980). Modified MUS and removal of relics

(removal of all trees with dbh > 60 cm regardless of species) has also been tested in Sarawak (Lee 1982).

In Sabah, the modified MUS was abandoned and replaced in 1971 by the minimum girth system, which retains the basic principles of the MUS (Chai and Udarbp 1977). This new system includes three silvicultural treatments at three different occasions. The first involves climber cutting two 15 years before felling operation to reduce the risks of felling damage» The second combines the natural regeneration inventory by linear sampling of milliacre plot and poison girdling to eliminate competition. The third silvicultural treatment involves the natural regeneration inventory by linear sampling half-chain survey and a liberation treatment. Chai and Udarbp (1977) concluded that the second

treatment should be modified to suit the present conditions of logging in Sabah, and they recommended alternative

research to reduce logging damage.

In Indonesia, since 1972, the Indonesian selective

logging system has been used as a means of converting the virgin forest into an enriched managed stand (Soekotjo and

Dickmann 1978). This system consists of removal of trees

with dbh > 50 cm to favor the growth of residual trees and

seedlings of desirable species. Approximately 25 young and

healthy overstory trees per hectare are usually left. After

4-5 years, the initial results have shown that the

Indonesian system seems to be appropriate for forest

management of Indonesian TMF (Soekotjo and Dickmann 1978).

In the Philippines, the selective logging system has

been used in managing the dipterocarp forests since the

19503” Specifically, this system assures a future crop of

timber and forest cover for the protection and conservation

of soil and water after the removal of the mature,

overmature and defective trees (Virtucio and Torres 1978).

According to these authors, the preliminary evaluation of 16 the selective logging has shown positive results for the management of dipterocarp forests.

Other countries such as India, Burma and‘Thailand are using the selective felling system to manage their forests

(James, pers. comm.). Burma contains 75% of world's stands of natural teak. In India and Thailand, many species of dipterocarp and teak are very important to the country's forest economy.

2.6. TROPICAL SOUTH PACIFIC

Natural regeneration management was attempted in Fiji during the 19603. Five years later this project was abandoned (Higuchi 1981b) because the first results were not encouraging. Today the priorities in Fiji are planting Pinus caribaea var. hondurensis and management of Mahogony

(Swietenia macrophylla) plantations.

In Papua New Guinea, forest plantations seem to be the only long-term alternative for its forests and for the supply of its forest industries (Hilton and Johns 1984).

2.7. CONCLUSION

The utilization of natural regeneration as a tool for forest management on a sustained yield basis in the TMF mainly for dipterocarp forests is certain in almost all southeast Asian countries. Although the Tropical Shelterwood

System (TSS) was abandoned in Nigeria, there exists a future 17 for natural regeneration as a way to manage the TMF, mainly in well-stocked high forests (Kio 1976L.In South America the first results of research recently established in

Suriname and Brazil have shown that natural regeneration management is economically feasible and ecologically acceptable.

The greatest obstacles to success with natural regeneration management in tropical countries are the lack of continuity in funding, the inadequacy of the staff, and sometimes political factors. Tang (1980), for example, considers that the success of natural regeneration management depends on the implementation and monitoring phases which can be carried out only with a well-trained staff. According to Fox (1976), all mentioned problems are typical in developing countries, primarily because the anxiety to show progress is more important than anything else. Unfortunately, natural regeneration management requires long periods of time before results are known.

It is very important to maintain a cautious approach in using the tropical moist forests because, according to Myers

(1983), very little is known about these ecosystems. It will be better to find that we have been vaguely right than certainly wrong. 18

Table 2.1: 1961 version of TSS - summary of operations.

YEAR INSTRUCTIONS

-5 Op.I Milliacre sampling Op.Ia Demarcation Op.II Climber cutting and cutting uneconomic saplings if advance growth is inadequate Op.III Climber cutting only Op.IV 2nd. milliacre assessment following Op.II 0p.V Poisoning of shade casting trees of lower and middle storeys

-4 (if Op.II in year -5, then Op.IV followed by 0p.V)

-2 0p.VI Re-demarcation

-1 0p.VII Climber cutting

0 Harvesting

8 Op.Ix Re-demarcation Op.X Climber cutting Op.XI Removal of Shelterwood

15 Op.XII Re-demarcation Op.XIII 1/2 chain linear sampling

Source: partially reproduced from Lowe (1978). 19

Table 2.2: Malayan Uniform System (MUS) - Summary of

activities.

======2= ACTIVITY DESCRIPTION

Pre-Felling Except in cases where enumeration data Inventory on trees 39 cm dbh and above is needed for premium determination only.

Pre-Felling Treatment of bertam in hill forest only. Treatment

Felling Limit All commercial and utilizable species with dbh = 45 cm and above.

Tree Marking May or may not be done by forest officers. Directional felling incorporated but essentially for checking completeness of felling only. No marking of residuals for retention.

Roading Layout Prescribed specifications and Construction

Post-Felling To determine fines on trees unfelled, Inventory royalty on short logs and tops, damage to residuals.

Silvicultural To determine correct treatment. Sampling

Source: Cheah (1978). CHAPTER 3

THE BRAZILIAN AMAZON

3.1. INTRODUCTION

The Amazon region includes the following countries in

South America: Brazil with 500 million (mi) hectares (ha),

Bolivia (65 mi.ha.), Colombia (62.5 mi.ha.), Peru (61 mi.ha.), Guyana (21.5 mi.ha.), Venezuela (17.5 mi.ha.),

Suriname (14.5 mi.ha.L, and French Guyana (9 mi.ha.)

(Volatron 1976). The name of this region comes from the

Amazon Basin and its main river, the Amazon, which originates on Mt. Huagra in Peru at 5,182 meters above sea level (a.3.l), 195 km from the Pacific shore. According to

Palmer (1977) in the first 965 km from its source, the

Amazon river drops 4,876 m while in the remaining 5,785 km the fall to sea level is only 306 m.

In the Brazilian territory the area of influence of the

Amazon Basin includes the following regions: Acre (AC),

Rondonia (RO), Amazonas (AM) and Para (PA) states, part of

Mato Grosso (MT), Goias (GO) and Maranhao (MA) states, and two federal territories, Roraima (RR) and Amapa (AP).

Hereafter, this area will be referred to as the Brazilian

Amazon or simply as the Amazon. This area is under geographical and political influence of Amazon Basin, even though it is known that the Amazon forest ecosystem covers

20 21 around 3/5 of this area. The Amazon region corresponds to about 55% of the Brazilian territory, but its population represents only 10% of its total. Fig. 3.1 shows the location of Brazilian Amazon within South America.

3.2. CLIMATE

The Brazilian Amazon region is characterized by homogeneity in climate conditions. In the interior of the forest of this region the microclimate is much more equable, especially on the ground itself where no direct sunlight falls (Walter 1979). Coastal and in-land temperatures do not differ greatly. Belem, some 100 km from the sea, has a mean annual temperature of 25° C. Manaus, nearly 1,000 km up- river on the Amazon, has an equivalent of 27° C and Taraqua some 2,000 km in-land has a mean annual temperature of 24.9° C. The maximum temperatures are around 37 to 40° C with a diurnal variation of some 10 degrees. According to

Salati & Vose’(1984), however, an important phenomenon to be considered is the "friagem' or cold spells that occur when air masses from the South Polar region hit the central and western parts of Amazon, causing the temperature to fall to about 14° C.‘This phenomenon occurs during the winter in the states of Acre and Rondonia, and in the southern parts of Amazonas state.

Rainfall shows greater variability than temperature across the region. There is approximately 3,000 mm annual rainfall on the coast, 3,497 mm at Taraqua (less than 100 km 22

from the limit boundary between Brazil and Colombia), 1,504 mm in Boa Vista (the Capital of Roraima), and 1,670 mm in

Conceicao do Araguaia.

According to Ranzani (1979) the dominant climatic types

(Koppen classification) in the region are Af (coolest month above 18° C and constantly moist) and Aw (coolest month above 18° C and dry period during the winter).

Including air moisture regime (presence of dry period with its duration), IBGE (1977) identified five climatic zones:

(a) Equatorial very moist without dry period: covers the northwest Amazon (about 30% of Amazonas state) and Belem

(the Capital of Para state).

(b).Equatorial very moist with short dry period.(less than one month): covers the surrounding areas of type (a)

(about 30% AM and 25% of AC).

(c) Equatorial moist with dry period ( one to two months): covers the western-center and the southeast of

4Amazon (50% of AC, 30% of AM, 30% of RR, 30% of PAmand 10% of north of MT).

(d) Equatorial moist with dry period (three months): covers the southwest and the eastern-center of Amazon (10% of AM, 100% of RD, 70% of PA, 40% of RR, 70% of AP, 10% of

G0, 40% of MT and 40% of MA).

(e) Tropical semi-moist with dry period (four to five months): covers part of RR and south and southeast of Amazon

(30% of RR, 50% of MT, 90% of Goland 60% of MA). 23

3.3. SOILS

The soils in the Brazilian Amazon are very old, reaching back as far as the Paleozoic era. Basically the region is composed of a sedimentary basin (Amazon Valley) located between two shields (Guiana and Brazilian).

According to IBGE (1977) these two shields are composed of igneous Precambrian and metamorphic rocks from Cambrian-

Ordovician, They contain some spots of sediments from the

Paleozoic/Mesozoic (60 to 400 million years ago). There are two Paleozoic strips of sediments where Devonian shales predominate, one at the Guiana shield boundaries (east of the 60 degrees of longitude) and another at the Brazilian shield boundaries (east of the 57 degrees of longitude) 30 to 50 km wide (Schubart & Salati 1980). The Amazon Valley is formed by fluvial sediments of coarse texture deposited from the Cretaceous to the Tertiary periods, originated from the erosion of the Precambrian shields (Schubart & Salati 1980).

In summary, this is the evolutive process of formation of

”terra firme" (non-flooded ground).

Another important formation in the‘Amazon region is the

"varzea", or temporarily flooded land. According to Schubart

5 Salati (1980) the ”varzeas" are constituted by the

Holocene flood plains of the Solimoes river (Amazon river above Manaus) and.the Amazon as well as their white water tributaries. "Varzeas" are the most recent formation in 24 from the deposition of sediments transported by the rivers

(Ranzani 1979). This kind of formation represents only 1.5% of the region, but its high agriculture productivity is significant to the Amazon economy. Ranzani (1979) pointed out that its fertility is not constant, as it depends upon the materials incorporated annually by flooding.

According to Cochrane & Sanchez (1980) the following soil orders are found in the Brazilian Amazon: Oxisol

"yellow Latosols" (45.5%), Ultisols "red yellow Podzolics"

(29.4%), Entisols "azonal, alluvial soils" (14.9%), Alfisols

"gray brown Podzolics" (4.1%), Inceptisols "hydromorphics, humic gley soils" (3.3%), Spodosols "Podsols or giant tropical Podzols" (2.2%), Mollisols "Chernozem, humic gley soils" (0.8%), and Vertisols "grumusols" (0.1%).

In general, the soils are extremely poor in nutrients and very acid. In fact, almost the entire nutrients amounts required by the forest are contained in the aboveground biomass (Walter 1979). Cochrane 3 Sanchez (1980) pointed out that only about 6% of Amazon has well drained soils with relatively high natural fertility. These soils are found in

Altamira (Para state), Porto Velho (the capital of Rondonia state) and Rio Branco (the capital of Acre).

Ranzani (1979) stressed that few Amazon soils are suitable for agriculture, grazing or even for reforestation. 25

3.4. VEGETATION

Using the Holdridge classification and the

climatological observations of IBGE (1977), there are two

major life zones in the Brazilian Amazon. These are the

tropical moist forest (mean annual biotemperature above 24°

C and mean annual precipitation of 2,000 to 4,000 mm) and

the tropical dry forest (mean annual biotemperature above 24° C and mean annual precipitation of 1,000 to 2,000 mm).

According to Schubart & Salati (1980) about 8% of the

Amazon is under secondary vegetation and/or agricultural

activitiesu‘Within the tropical moist forest only limited

areas on the coast, along the major tributaries of the

.Amazon and along the Amazon River have been used for food

production (Tosi 1983). The most significant deforestation

is located in the tropical dry forest, mainly along the

Belem-Brasilia highway, southern portions of MT, and in

Rondonia and Acre states.

It is well known that the main characteristic of the

.Amazon forest is its considerable vegetational diversity,

although at first sight it appears to be rather uniform

(France 1974).

The Amazon region is reported to contain about 6,000

different species of plants, of which one-third are tree

species growing to commercial size. The distribution of

these trees varies tremendously, particularly in relation to

soils and topography.

There are many theories to explain this diversity. 26

According in: Prance (1974) the genetic isolation into separate populations after a long dry period in the late

Pleistocene and post-Pleistocene was a major factor in the evolution of the species diversity within the lowland forest of Amazon. Schubart & Salati (1980) pointed out that the large number of species and the complexity of their interrelatioships are a function of evolutionary history which can be broadly described by three main categories of factors: proximal (or geographic factors), interactions within.the communities themselves, andtdynamic instability.

In spite the complexity and diversity of the Amazon vegetation, a broad classification - based on the Holdridge system plus part of the classification presented by Prance

(1974) - will be presented for the two major life zones

(Fig. 3.2).

1. Tropical moist forest

1.1. Tropical moist forest on "terra firme"

1.2. Inundated forests: "varzea" (seasonally flooded forest) and "igapo" (permanently water-logged)

lu3. Forest on white sand soils or spodosols: "Campina" and "Campina r ana" .

2. Tropical dry forest

2.1. Amazon tropical semi-evergreen forest

2.2. "Cerrado" (Savannas). 27

Tropical moist forest on "terra firme”

The superior stratum of this forest type is composed of trees whose heights may vary from 30 to 40 meters. Only a few species can grow above this height. Exceptions are

Cedrelinga catenaeformis and Dinizia excelsa with, on some

sites, 50 and 60 meters height respectively. For trees with dbh greater than 20 cm, the forest on "terra firme" has a mean commercial volume of 150 to 300 cu.m./ha and a basal area of 20 to 40 sq.m./ha.

IBGE (1977), Braga (1979), Silva et a1. (1977), Higuchi et al. (1983a), and four forest inventories carried out by

Department of Tropical Silviculture of INPA (National

Institute for Research in the Amazon) in different parts of

Amazon are the guide for the description of floristic composition of this type of forest. Here the emphasis is only (”1 those species which can characterize specific regions.

In a broad sense the following phanerophytes can be considered as typical species of "terra firme": Dinizia excelsa, Bowdichia nitida and Cedrelinga catenaeformis

(Leguminosae), Anacardiug gigagtggm (Anacardiaceae),

Bertholletia excelsa "Brazilian nut" (Lecythidaceae),

Caryocar villosum (Caryocaraceae), Minquartia guianensis

(Olacaceae), and two species of Palmae, Oenocarpus bacaba

and Astrocaryum mumbaca. The characteristic epiphytes of

”terra firme" are: several species of Phillodendron

(Araceae), Clusia insignis and Clusia grandiflora

28

(Guttiferae), several species of Operculina (Convolvulaceae) and Bauhinia macrostachya (Leguminosae).

IBGE (1977) divided the ”terra firme" into seven sub- regions to show the characteristic tree species of these areas, in contrast to the previous group of species which is common to all sub-regions.

The sub-regions are:

(a) Delta of Amazon river: In this area the following species characterize the "terra firme": several species of

Parkia, Vatairea guianensis and several species of Ormosia -

(Leguminosae), Erisma fuscum and Vochysia guianensis -

(Vochysiaceae), several species of Manilkara and Pradosia -

(Sapotaceae), and several species of Virola -

(Myristicaceae).

(b) Northeast Amazon: several species of Micropholis,

Ecclinusa, Chrysophyllum and Manilkara - (Sapotaceae),

several species of Eperuaz Swartzia, Ormosia, Tachigalia and

Inga - (Leguminosae), Goupia glabra (Celastraceae), several

species of Iryanthera - (Myristicaceae), and several species of Qualea - (Vochysiaceae).

(c) Tocantins & Gurupi rivers: Swietenia macrophylla "Mahogany", Cedrela odorata and 935323 guianensis -

(Meliaceae), Hevea brasiliensis - (Euphorbiaceae),

Platymiscium duckei, Vouacapoua americana, and several species of Piptadenia and Peltogyne - (Leguminosae), Cordia

29

goeldiana - (Boraginaceae), Mezilaurus itauba - (Lauraceae),

several species of Astronium - (Anacardiaceae), and

Jacaranda copaia - (Bignoniaceae).

(d) Xingu and Tapajos rivers: The floristic composition

of this sub-region is almost the same as the sub-region (c).

(e) Madeira and Purus rivers: Hymenolobium excelsum, Peltogyne densiflora, several species of E2353; and

Elizabetha - (Leguminosae) Swietenia macrophylla and Carapa

guianensis - (Meliaceae), Euterpe oleracea - (Palmae),

several species of Theobroma - (Sterculiaceae), Cordia

goeldiana - (Boraginaceae), Manilkara huberi - (Sapotaceae), Cariniana micrantha - (Lecythidaceae), Hevea brasiliensis.

(f) Occidental "Hileia" - Jurua to Brazilian territory

limits: several species of Theobroma "Cocoa tree" and

numerous palms, and several species of Leguminosae,

Myristicaceae, Bombacaceae, Lauraceae, Vochysiaceae and

Rubiaceae.

(9) Northwestern "Hileia" - Negro to Trombetas river:

Leguminosae is the dominant botanical family in this sub-

region, mainly species of genera Dimorphandra, Peltogyne,

Eperua, Heterostomon and Elizabetha. The genena Dicorynia,

Aldina, Macrolobium and Swartzia are endemic:in this sub-

region. Other characteristic species are: Carapa guianensis,

Cedrela odorata and Cariniana micrantha. 30

(h) Acre: Torresea acreana - (Leguminosae), Hevea

brasiliensis, Swietenia macrophylla and several species of

Cedrela.

Inundated forests

This type of forest represents an area of about 7

million hectares, or 1.5% of the Amazon region (Braga 1979).

Within this type, the best and the biggest portions are the

seasonal "varzea" and tidal "varzea". They are considered

very important for the development of the Amazon region

because of their soil quality and also because they supply

most of the raw material to forest industries.

Prance's (1980) key for the classification of inundated

forest types was used to describe the vegetation. The author

pointed out that the three different types of water (white,

black and clear) of Amazon basin are very important to the

floristic composition. There are peculiar species for

specific water types, mainly due to differences in acidity

and nutrient contents. For example, Victoria amazonica is

found only in white water.

The seven inundated forest types are:

(a) Seasonal ”varzea": this type is characterized by a

relatively high aboveground biomass and represents the most

common type of inundated forests. According to Prance (1980)

its herb layer is rich in species of Heliconia (Musaceae)

and Costus (Zingiberaceae). The following species can 31

characterize this type of forest.cPrance 1980, Braga 1979, and IBGE 1977): Carapa guianensis, several species of

Cecropia - (Moraceae), Ceiba petandra - (Bombacaceae),

Couroupita subsessilis - (Lecythidaceae), Euterpe oleracea -

(Palmae), Hura crepitans and Piranhea trifoliata -

(Euphorbiaceae).

(b) Seasonal "igapo" - swamp forest: Usually dominated by sand soils supporting a vegetation much poorer than the seasonal "varzea". According to Braga (1979), the vegetation

is very specialized with little specific diversity and very

rich in endemism. Characteristic species of this type are:

Aldina latifolia - (Leguminosae), several species of Couepia

- (Lecythidaceae), some species of Licania - (Chrysobalanaceae), and Macrolobium acaciifolium -

(Leguminosae).

(c) Mangrove: This type is typical in the estuary of

the Amazon. According to Braga (1979) the mangrove type

involves an area of about 100,000 hectares with a low and

uniform aboveground biomass. This type is characterized by

the presence of Avicennia nitida (Verbenaceae), Laguncularia

EEEEEQEE (Combretaceae) and BEEEQREQEE ‘1‘. “91.2.

(Rhizophoraceae).

(d) Tidal "varzea": This type is very similar to the

seasonal "varzea" in both species composition and

aboveground biomass. Prance (1980) stressed that where the 32 tide is daily, the vegetation is similar to the swamp. Where the spring tide is dominant, is more similar to the seasonal

"varzea". The most common palm species are: Mauritia

flexuosa, Euterpe oleracea, Raphia taedigera and Manicaria saccifera. Species like Virola surinamensis (Myristicaceae),

Ceiba petandra, Mora paraensis, Pithecolobium huberi, Derris liEiEQliiL EXEEBEEE 222222 and lflflé BEEEQQBE '

(Leguminosae), and Tabebuia aquatilis (Bignoniaceae) have also a significant presence in this type of forest.

(e) Flood plain: Species from seasonal "varzea" and also from "terra firme" can be found in this forest type.

(f) Permanent swamp forest: According to Prance (1980) there are few permanent swamp forests or permanent ”igapo” in the Amazon. This type contains very few species, although trees are usually very big and similar to their counterparts of seasonal "varzeaF. The canopy is usually more open than the seasonal "varzea" and the ground is rich in Cyperaceae.

"Campina" and "Campinarana"

The soil of these two types is almost the same, but their floristic composition and the stand density are different. According to Lisboa (1975), the tropical moist forest on "terra firme" is commonly interrupted by ”islands” with contrasting tree size, structure and physiognomy. Such

”islands” are oommon.in the Rio Negro river basin and in 33

other areas north of the Amazon river, but almost absent in the southern parts of this river. "Campina" and

"Campinarana" are evergreen.

(a) "Campina":‘According to Braga (1979), this forest type presents a low aboveground biomass with sclerotic vegetation, and covers an area of 3.4 million hectares (0.7% of Amazon).

Although the "Campina" soils are excessively drained, acid and poor in nutrients, there is no problem with water availabilityu Lisboa (1975) pointed out that without this characteristic the actual vegetation could be replaced by

Gramineae, Cyperaceae and small shrubs.

Thee"Campina" floristic composition is variable, but the following species could be considered as characteristic species of this forest type (Braga 1979): Aldina heteroghylla and Ormosia costulata - (Leguminosae), Clusia aff. columnaris (Clusiaceae), Glycoxylon inophyllum

(Sapotaceae), Humiria balsamifera (Humiriaceae), Matayba

923 a (Sapindaceae), and Protium heptaphyllum (Burseraceae).

According to Lisboa (1975) the epiphytes are abundant in

"Campina" because the high intensity of light, e.g., many genera of Orchidaceae (Sauticaria, Octomeria, Rodrfigzia and Maxillaria) and also many species of Bromeliaceae

(Aechmea and Tillandsia).

(b) "Campinarana" (false "Campina"): In this forest type the trees are larger and the stands are denser in 34

comparison to the "Campina" type. According to Braga (1979),

"Campinarana" represents an area of approximately 3 million hectares distributed as small islands in the central Amazon and as bigger portions north of Amazon river (Negro basin).

"Campinarana" is also very rich in epiphytes, mainly

Hymenophyllaceae and Bryophytae. The following species characterize this forest type (Braga 1979): Aldina discolor,

Eperua leucantha and Hymenolobium nitidum - (Leguminosae),

Bactris cuspidata (Palmae), Clusia. spathulaefolia

(Clusiaceae), Qggm§_ gatigga£_ (Apocynaceae), ‘ggggg

rigidifolLa (Euphorbiaceae), Sacoglottis heterocarpa

(Humiriaceae) and Scleronema spruceanum (Bombacaceae).

Amazon tropical semi-evergreen forest

This forest is considered as a transition from Savannas and tropical semi-evergreen to tropical moist forests. It occurs in part of MA, portions of eastern, southern and northern PA, northern MT, almost 90% of Rondonia, portions of AC, small portions at northern and southern AM, a significant portion of the federal territory of Roraima and a small portion of Amapa.

In general, according to IBGE (1977), the trees are relatively tall, with medium diameter and under-developed crowns. Lianas are abundant, but epiphytes are almost absent. The species most characteristic of this forest type

is Orbignya martiaga (Palmae). Hevea brasiliensis is abundant mainly along the southern tributaries of the Amazon 35

river.

In the MA portions and eastern PA the species which characterize this forest type are: Bertholletia excelsa, Ceiba petandra, Vouacapoua americana, Castilloa ulei

(Moraceae), Hymenaea courbaril (Leguminosae), Lecythis

paraensis (Lecythidaceae), and several species of Palmae, e.g., Oenocarpus bacaba, Maximiliana £2933 and Euterpe oleracea.

According to IBGE (1977) the best known portion of

Amazon tropical semi-evergreen forest is that in the southern part of PA which partially covers the Brazilian shield. The characteristic species are: Calophyllum

Riééilififlfifi (Guttiferae), some species of £9222;

Aspidosperma and Moutabea, Apuleia praecoxi Hymenaea stilbocarpa, Lucuna lasiocarpa, Simaruba amara, etc.

At the eastern of'the Tapajos river, between Santarem and Belterra, and the northern of the Amazon river, the northern part of PA, the following species are characteristic: Qualea grandiflora and Vochysia ferruginea - (Vochysiaceae), Sclerolobium paniculatum, Dalbergia

spruceana and Centrosema venosum - (Leguminosae).

"Cerrado" (Savannas)

The "Cerrado" trees are relatively short (around 10 meter height) and less abundant than shrubs. Basically there are two strata: the superior which is composed of trees and 36

shrubs, and the inferior which is composed of grasses. The

tree stratum is characterized by individuals with crooked

stem and branches, thick bark, and thick leaves with rough grained texture with surfaces of 30 by 20 cm.

According to IBGE (1977), the characteristic species of

"Cerrado" are: Hancornia speciosa (Apocynaceae), Curatella

americana (Dilleniaceae), Qagyggar brasiliensis

(Caryocaraceae), Salvertia convallariaedora, Kielmeyera

coriacea, and Stryphnodendron barbatimao. 37

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CHAPTER 4

DESCRIPTION OF THE STUDY AREA.

The data were collected on the control plots of an experiment on natural regeneration management of an uneven- aged mixed stand of the Amazonian forest. This experiment is being carried out by DST (Department of Tropical

Silviculture) of INPA.(Nationa1 Institute for Research in the Amazon). The experiment is a branch of the project

"Ecological Management of the Dry-land Tropical Moist

Forest". This multidisciplinary research involves all departments of INPA: Ecology, Botany, Wood Technology, Plant and Human Patology, Agriculture, Chemistry and Zoology.

These departments will give scientific support to DST in its future evaluations of the environmental impact of the forest management.

The study area is located within the domain of the

Tropical Silviculture Experimental Station of INPA, some 90 kilometers north of Manaus, the capital of Amazonas State,

Brazil. The total area of the Station is 23,000 hectares and the project area is approximately 2,000 hectares. The geographical coordinates of the project area are 2° 37' to 2° 38' of south Latitude and 50° 09' to 60° 11' of west longitude. Figure 4.1 shows the location of the study area

39 40 within the Experimental Station.

According to Ranzani (1980) the climate is type Am,

Koppen classification, warm and moist all year long. The annual rainfall is approximately 2,000 mm without accentuated dry period, even though the wettest period is

December to May (Ribeiro 1977).

The oxisol soil order "yellow latosols" is predominant in the area. This research was set up only on non-flooded ground, iue., on "terra firme". The soils are extremely poor in nutrients and very acid.

The relief is smoothly undulated and it is formed by small plateaus which vary from 500 to 1,000 m in diameter.

Most of the experimental treatment areas are located on those plateaus.

The vegetation is typical of the Amazonian tropical moist forest on "terra firme". The superior stratum of this forest is composed of trees whose heights vary from 30 to 40 meters. Basically three botanical families dominate the floristic composition of the area, Lecythidaceae,

Leguminoseae and Sapotaceae. Individually Micrandropsis scleroxylon W.Rodr. (Euphorbiaceae) and Scleronema

micranthum Ducke (Bombacaceae) have an impressive presence in the study area. Several species of Eschweilera ,

Holopyxidium latifolium R. Knuth, Corytophora alta R. Knuth and Lecythis usitata Miers var. paraensis R. Knuth are the

most frequent species of Lecythidaceae. However,

Bertholletia excelsa Humb. and Bonpl. "Brazilian nut” 41

(Lecythidaceae) is absent from the area. The most frequent

Leguminosae are several species of Inga, Tachigalia,

Swartzia, Parkia and Pithecolobium. Within the Sapotaceae the most frequent are several species of Chrysophyllum,

Micropholis, Pouteria, Labatia, Ecclinusa, and Manilkara.

The floristic composition of the area is presented in the

Appendix.

The ecological project area is in the Tarumazinho watershed. The project was divided into three parts, referred to as bacia l, bacia 2 and bacia 3. Respectively, these are areas reserved for basic studies, buffer, and harvesting and forest management.

Bacia 3 is the basis of this study. Figure 4.2 shows

BaciaLB in more detail, Originally this experimental area covered 96 hectares, consisting of 4 blocks (bloco l, bloco

2, bloco 3, and bloco 4) of 24 ha each. After the commercial inventory, bloco 3 was reserved for research on artificial regeneration and, therefore, it was not included in this study. Within each block (400 by 600 m), harvesting will be carried out as the main silvicultural treatment. In designated sub-blocks (200 by 200 m each), different felling intensities will be applied to reduce basal area of some 40 listed species with dbh ; 25 cm.

The treatments randomly distributed in each block were:

(1) control, (2) removal of 25% of the exploitable basal area (b.a.), (3) removal of 50% of the exploitable b.a., (4) removal of 75% of the exploitable b.a., (5) removal of 100% 42 of the exploitable tha., and (6) removal of 50% of the exploitable txa. with enrichment. In each four-ha sub-block a one-ha plot (100 by 100 m) was established to evaluate the growth of the residual stand of listed species, recruitment and development of seedlings of listed species, survival and growth of listed species, growth and mortality of poles and saplings, and increment evaluation for determining the felling cycles. The listed species for this project are presented in Table 4.1.

After the randomization of the blocks, the control sub- blocks were 2, 3 and 5, respectively for blocks 1, 2 and 4.

Those sub-blocks, then, were used in this study. Hereafter they will be referred to as bloco l, bloco 2, and bloco 4, and collectively they will be called bacia 3.

In 1980, two different inventories were carried out in bacia 3: commercial (complete enumeration of trees with dbh

> 25 cm within the experimental blocks), and diagnosis of natural regeneration by sampling.

From the commercial inventory (Higuchi et al. 1983a) the following data were obtained: (a) the listed species represent 1/3 of the population, (b) overall means per ha: number of trees = 155, b.a. = 19 sq.m., and volume with bark

= 190 qum" (c) block 3 is statistically different from the others in terms of stand stocking and also in terms of floristic composition.

From the natural regeneration inventory (Higuchi et a1.

1985) the following summaries were obtained: (a) the 43 stocking index of seedlings averaged 15.6%, (b) the stocking index of poles and saplings averaged 72.8%, and (c) the number of trees smaller than 25 cm dbh and greater than 0.30 m height averaged about 40,000 per hectare. The "milliacre” and "half chain square" methods were used for data collection of the diagnostic inventory, respectively for seedlings (tree species with dbh < 5 cm) and for poles and saplings (5 < dbh < 25 cm).

In 1985, all trees tagged in 1980 from the control plots were remeasured. This was done to evaluate the growth of diameter of those trees (increment), to record new trees that moved to the first merchantable dbh class (ingrowth), and to record trees which died during the period 1980-1985

(mortality). 44

Table 4.1: Listed species for the NR management project.

Spec1e Family

Virola calophylla Warb. Myristicaceae Virola multinervia Ducke Myristicaceae Virola venosa (Bth. ) Warb. Myristicaceae Ocotea cymbarum H. B. K. Lauraceae Dialium guianensis (Aubl. ) Sandw. Leg. Papil. And1ra micrantha Ducke Leg. Papil. D1plotropis purpurea (Rich. ) Amsh. Leg. Papil. Manilkara huberi (Ducke) Standl. Sapotaceae Calophyllum angulare A. C. Smith Guttiferae Nectandra rubra (Mez.) C.K. Allen Lauraceae Mezilaurus synandra (Miq.) Kostermans Lauraceae Licaria guianensis Aublet. Lauraceae Platymiscium duckei Huber Leg. Papil. Caryocar villosum (Aubl.) Pers. Caryocaraceae Goupia glabra Aubl. Calastraceae Aniba duckei Kostermans Lauraceae Naucleopsis caloneura (Hub.) Ducke Moraceae Scleronema micrantha Ducke Bombacaceae Minquartia guianensis Aubl. Olacaceae Copaifera multijuga Hayne Leg. Caesalp. Qualea paraensis Ducke Vochysiaceae Diniz1a excelsa Ducke Leg. Mimos. P1thecolobium racemosum Ducke Leg. Mimos. Hymenolobium excelsum Ducke Leg. Papil. Astronium lecointe1 Ducke Anacardiaceae Clarisia racemosa R. et P. Moraceae Hymenaea courbaril L. Leg. Caesalp. Dipteryx odorata (Aubl.) Willd. Leg. Papil. Lecyth1s usitata Miers Lecythidaceae S1maruba amara Aubl. Simarubaceae

Caryocar pallidum A. C. Smith Caryocaraceae Erisma fuscum Ducke Vochysiaceae Holopyxidium latifolium R. Knuth Lecythidaceae Vouacapoua pallidior Ducke Leg. Caesalp. Eschweilera odora (Poepp) Miers Lecythidaceae Eschweilera longipes (Poit) Miers Lecythidaceae Anacardium spruceanum Benth. ex Engl. Anacardiaceae Aniba canellila (H. B. K. ) Mez. Lauraceae Park1a pendula Benth. ex Walp. Leg. Mimos. Corythofora r1mosa Rodr. Lecythidaceae Cariniana micrantha Ducke Lecythidaceae Cedrelinga catenaeformis Ducke Leg. Mimos. Peltogyne catingae M. _da F. Silva Leg. Caesalp. Bros1mum rubescens Taub. Moraceae 4S

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CHAPTER 5

MODELLING THE DIAMETER DISTRIBUTION OF AN UNDISTURBED FOREST

STAND IN THE BRAZILIAN AMAZON TROPICAL MOIST FOREST:

WEIBULL VERSUS EXPONENTIAL DISTRIBUTION

5.1. INTRODUCTION

Since total tree height is very difficult to measure accurately, diameter is the most powerful simple tree variable for estimating individual tree volume in the

Brazilian Amazon. Therefore, the quantification of diameter distributions is fundamental to understanding the structure of the growing stock and as a baseline for forest management decisions. In‘addition, regardless of the species of tree,

Amazonian timber commercialization is commonly based only upon the diameter distribution.

Bailey and Dell (1973) and Clutter et al. (1983) gave a comprehensive review of diameter distribution models.

According to Clutter et a1. (1983), among various statistical distributions, the Weibull distribution has been used the most to model diameter distributions. These results support Lawrence and Shier (1981), who stated that after the exponential, the Weibull distribution is possibly the most widely used distribution for population dynamics applications.

47 48

The introduction of the Weibull distribution function

to problems related to forestry is attributed to Bailey and

Dell in 1973 (Zarnoch et al. 1982, Little 1983, Clutter et a1. 1983, and Zarnoch and Dell 1985). Since then, this distribution function has been used extensively for diameter distribution of both even-aged and uneven-aged stands in the

USA.

The Weibull distribution has not yet been introduced in

tropical moist forests, especially in the Brazilian Amazon.

There, one of the most common models for diameter

distribution is still the exponential (Barros et a1. 1979

and Hosokawa 1981).

A comparison was made between the Weibull probability

density function and the exponential distribution as

diameter distribution models for Amazonian forests. The

hypothesized distribution functions were tested to see how

well they fit the observed diameters randomly taken from the

study area.

5.2. PROCEDURES

The Data

The data for this study were collected on the research

area of Forest Management Project conducted by the

Department of Tropical Silviculture of the National

Institute for Research in the Amazon (INPA) - described

earlier in Chapter 4. 49

The basic descriptive statistics of the study area are presented in Table 5.1.

The diameter distribution functions

The Weibull probability density function and the exponential were chosen for testing as a diameter distribution model for the experimental area.

In this study, the estimators of Weibull parameters were computed using the percentile (Zarnoch and Dell 1985) and maximum likelihood (Cohen 1965) approaches. The estimation of the exponential model parameters was done according tx: Einsensmith (1985). These approaches are described separately.

(1) Weibull Maximum Likelihood (MLE)

The Weibull distribution, which has the probability density function:

f(x) = (c/b)x°"1 exp(-(x)°/b); x20, c>0, b>0

= 0, otherwise has the following likelihood function for a sample of n observations

L(xi, ..., xn; c, b) = n(c/b)xi°"l exp(-xi°/b) (1)

Taking the logarithm of (l) 50

1n L zln [(c/b)xi°"1 exp (-xi°/b)]

{[ln (C/b) + 1n Xic-l ‘ (Xic/b)]

n 1n (c/b) + 2(c-1) 1n x- l - (1/b)‘3xiC

By differentiation with respect to g and b in turn and equating to zero, the following equations are obtained

d ln L/d c n/c + Zln xi - (l/b) zxic 1n xi (2)

d In L/d b -(n/b) + (l/b2) r xic = 0 (3)

Taking b from (3)

b = ( zxi°)/n (4) and substituting in (2) produces:

n/c + zln xi - [l/( ixic/n)] zxic 1n xi = 0

n [(l/c) - (xxic ln xi)/zxi°] = - zln x-

[inc 1n Xil/[inc] ' (l/C) = (l/n) zln Xi (5)

The coefficient 2 can be estimated by any iterative procedures or by a simple trial-and-error approach to equalize both sides of equation (5). The coefficient _b can be estimated by (4). 51

(2) Weibull Percentile (PERC)

The Weibull function using the percentile»estimators has the probability density function

f(x) (c/b)[(x-a)/b]°‘1 exp {-[(x-a)/b]°}; x3a30,b>0,c>0

0, otherwise

The parameters a, b, and g are estimated as follows:

- I [xlxn - xzzl/[xl + xn - 2x2] 3»

b = “a + X[.63n]

ln {[ln (1 - pk)l/[ln (1 - pi)]}

where: xi (1 = 1, 2, ... n) = the ith ascendent ordered diameter; pi = 0.16731, and pk = 0.97366.

(3) Exponential

The parameter estimates of the first order exponential function

Y = a*eb"'x can be obtained by the linearization method (or Taylor series). This is an iterative approach using the results of linear least squares in a succession of stages. According to 52

Draper and Smith (1981), the steepest descent and

Marquardt's compromise can also be used. Here the linearization method was used to estimate parameters a and

E.

The application pf the models £9 the data

In this context, 5 is the diameter in centimeters measured at breast height (1.30 m) in 1980. The Weibull parameters are defined as: a, the location parameter, which can be the smallest dbh measured, b, the scale parameter, which shows the relative range of values the dbhfs may assume; and c, the shape parameter, which determines the general form of the distribution (Zarnoch et a1. 1982). For MLE estimators, (x - 24.9999)*was used to compute p and 9 based on Cohen's two-parameter Weibull distribution, after assuming a = 25 (the smallest dbh measured). The value

24.9999 was used instead of 25 only to avoid the logarithm of zero, since no significant differences on the general computation was detected.

The parameter estimates for all three models, Weibull

MLE, Weibull PERC and exponential, are presented in Table

5L2. Estimates are shown for the combined three sample plots

(bacia 3) and separately for each sample plot.

The Weibull cumulative distribution function was determined by integrating the probability density function for both the MLE and the PERC which provided the probability 53

for each dbh class. Then, the absolute frequency for each

dbh class was obtained by the product of the total number of

trees per hectare and its probability. The estimated

frequency using the exponential distribution was obtained by

the simple substitution of each dbh class as independent

variable in the equation.

To see if the three hypothesized distribution functions

fit the data in the sample, the chi-square test was used for

goodness of fit (Conover 1980). The null hypothesis was that

the distribution function of the observed random variable is

the Weibull MLE, or the Weibull PERC, or the exponential;

and the alternative hypothesis, otherwise.

5.3. DISCUSSION OF RESULTS

The diameter distribution for bacia 3 and for each

.sample plot (bloco l, bloco 2 and bloco 4) are presented

respectively in Tables 5.3, 5.4, 5.5 and 5.6.

Except for the Weibull MLE in bloco l and bloco 2, the

remaining computed chi-square's are not significant even for

°= .25, i.e., the null hypothesis cannot be rejected. The

best fit with the Weibull MLE model occurred in bloco 4

where the highest sample variance (32 = 224.86) was

observed. On the other hand, the best fit for the

exponential model was in bloco 2, which had with the lowest

sample variance (32 = 114.76), and the worst fit was in

bloco 4.

In this study, the Weibull PERC model was not seriously 54

affected by variation within the sample plot. It was very consistent in fitting the observed data to all three sample plots across a range of diameter Classes. The Weibull MLE model, in contrast, consistently overestimated the frequency of the first dbh class and underestimated the next four classes.‘The Weibull MLE was consistent, after the first dbh class, only in bloco 4. The exponential model, on the other hand, was very consistent where the sample variance was low, and inconsistent with higher variance mainly in estimating the frequency of higher dbh classes. Bailey and Dell (1973) pointed out that c < 1 should occur in all-aged stands of tolerant species. Here the estimate of shape parameter was greater than one, c = 1.02, only for bloco 2. For the other sample plots and for the combined plots the 3's are smaller than one.

When the three sample plots are analyzed together on a per hectare basis, representing the entire experimental area

(bacia 3), all three hypothesized distribution functions fit

the observed data. This is demonstrated by the non-

significant chi-square values even for a==.25, although

the chi-square of the Weibull MLE model is about ten times

greater than the others.

Graphically the results of each hypothesized model for

the experimental area are presented in Figures 5.1, 5.2 and

5.3, respectively for the Weibull MLE, the Weibull PERC, and

the exponential. Figures 5.4, 5.5 and 5.6 represent the

relationships between the frequencies of observed dbh 55

classes and the frequencies estimated.by the hypothesized models for each sample plots, respectively for bloco 1, bloco 2 and bloco 4. As expected, except for the Weibull

PERC in bloco 2, the curves produced by the Weibull distributions are reversed J because of the c parameter

(c<1). These graphics demonstrated that the Weibull PERC produced the lowest dispersion of the observed data around the hypothesized curves for the combined plots and also for

individual plots.

5.4. CONCLUSION

The results indicate that the Weibull PERC model is the best model of the models tested to quantify the diameter distribution of natural stands in the Brazilian Amazon.

The simplicity in estimating the Weibull PERC parameters and its insensibility to the sample variation demonstrated in this work are valuable attributes to be

considered. This distribution function can be used with a

reliable individual tree volume equation for modelling

forest growth and yield for the study area, or for similar

areas in other portions of the Amazon.

The exponential model also performed adequately in

fitting the observed data. However, it may be inconvenient

to estimate its parameters by the iterative approach. The

ease of this method will depend upon the available computing

capabilities. 56 4 38.80 38.65 25.00 113.00 224.86

605 area.

BLOOD study

2 the 36.58 25.00 91.00 29.29 114.76 667

BLOOD for l 38.27 36.03 25.00 116.00 190.13 619

BLOOD statistics 3 37.84 25.00 35.00 116.00 175.39 1891

BBOIB descriptive mean

variation Oiaeeter(ca) variance of

cases 5.1: of a UBLUES

Hiniaua Hauiaua arithmetic Saaple Ooef. Tabla

57 basis.

.QJIBO .99 -.07668

13.5

$7.169

hectare -

.99

1.01510 -.07250

12

$6.185

distribution diameter

.9225) .% -.0730

14

359.245

For used

.94419 .99 -.07409

13

375.534 estimates

a

a b

b c Parameter

r"2 5.2:

coeF

com-F coeF. coeF.

Table mm

mnmmmmmmmmmammwmmwmmm". mm .1

.meeeeemeenzeemym mmmmu together

EMHBWML _aflaahlalZLL ...... mm EF plots 3.xammmepmmewemmmsemmie

methods. i _. sample emmm.emsaw.moem...mmx

three n&&25w753211 .mfi different

HUWUJEE

up» centime

all

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For m_»meemeemmmeemw.e.sfiwm

1.52111 “

From

m. class

man 5d” mdmfl¥= ”.meemmmmeemeemmemmme a

IEEELHI E_93W185432111 fi derived

diameter

distribution 3) .nmemmeums_.maemzeeut musn ..... momma ... ma of a»:

(bacia

Diameter center

lesseeseeeseeseeeeeeua =

5.3: asyeaeeeanneeesmmmmmm

08H(X)m Table

IOBH(X) mo

a mmmmfiymmmwfiammym..mmgmm different m .zaammzaaaun umaammnma7m ...... mmmm “a three from m mm1111mmw1mflmmmmflfifimmm$

mm 1 .s derived

1 m nammmmu wm3$55a7§amsum

E afimelalzprhh ...... “a

Bloco

for ".5 .mfiwzxz m:s mws1 Zn 5:

tEIflLL $_$55m754322111

distribution 152.3? “nammmnwmammmmma omOfi 5393177331111 [Fun 4

154.75

methods. Diameter

“5555555555555555555_ 5.4: _a yaaasaannamuwmmm TDTFI.

[84003: Tfile Table 5.5: Diameter distribution for Bloco 2 derived from three different ”flak.

IEWELH£ EflulFflE EWUGWML

UEH(X)u “It!” 1.73423 ”mommmsmm...wfimm.fl w. 3' mm mnmfi.mm.fiflflflfiaflwmn ”2.2....mxflmflafi“. m _wawaaawewnnamn_ .&%572&54&&L...mfl _mfl..fiw..mmmgwa $356m74311

‘5 ml 25 (D '5 1

00 mo a) 1 a! 25 4 25 O

25 . 25 m. m%

“a _4 mfll manilarm a

I08H = center of diameter class in centimeter. Table 5.6: Diameter distribution for Bloco 4 derived from three different metfods.

Film llE Elm PERC EXPCIENTIR.

UBHI u-‘mm m_2 .mmmmmfiflmflflmmmomwflmmfle m_ mmmwmmmfimwfiflmmmmwmmmnfl E_wwwu864322L1L m“ I. mmmmmmmemmumamaa mmflmmflfim..flmm.mnam.a EF .mmmuaz&1&&LL _manmamaaanh.n.n ”maflmm&L&ZLL “mammumnmww .mnwnmnmmmamnwnaoma _aawenaweannmmuwmmm mmmmmmunnmmmmmmaawma 555£5£5£5fifififififijfifini ...... 1 my cl" ..... mnyawmwne ...... 1 «oh-mm manna .. .9 “m “1 mm

m a % & m

62

70- L1 L1 3 1 14 {tree/ha 13‘ L L A l N O I L L . L4 ..e 0 A I

-..--___._V,, c211. 10 20 30 40 50 50 70 80 90 100 110 120 DBH Gun) PREDICTION BY WEIBULL MAX. UKEUHOOD

Figure 3.1: Becia 3 - The relationship between the observed and estimated dbh frequencies, using the Heibull HLE function. 63

r111

troo/

I

"5b"'eb"'7b” so so TM 110 120 DBH Own) PREDICTION B'Y WEIBULL PERCENTILE

Figure 5.2: Bacia 3 - The relationship between the observed and estimated dbh frequencies, using the Heibull PERC function. f tree/ha 70-1 Figure

‘1'er 102030405000703'0901001101 5.3: VVY'VVV‘f'

Exponential Bacia and PREDICTION estimated 3 r'7111lvv'vrf - DBH(cm) The BY function. 64 relationship dbh EXPONENTIAL frequencies, between using the the observed 65

70 e m a. o—e M

2 SD \g .. . "" so

3

ID

D 5 ...... 2,0 ..... 4'0 ..... i ..... *mgh".¥b

DDH (cm) (’0

3 § a can-us a can-us

fl-E H m Ii!- “q .1“? H m I 2 “9 ad \ 3 g 3 . g “5 2 “H L ‘Vwé ‘Kué afi afi 0-3 01

40 I 1 20 40 u I 1 DB" (cm) . OBI-I (cm) (a) (C)

Figure 5.4: Bloco l - The relationship between the observed and the estieated dbh frequency distributions by (A) Exponential, (B) Heibull PERC, and (C) Heibull MLE functions.

66

: - m 79.5 O-e see-Its ”E 2 E \ a; 8 : b{wj - s ...; aé mi 113 -

6 IIIIII g IIIII 1% sssss 3 iiiii B'vagwsvvv‘k

DDH (cm) (A)

‘H : I “I“

mi 2 i 3 ”‘1 of“? 3% 20-5 11.: .3 '

40 I l DBH(un)

Figure 5.5: Bloco 2 - The relationship between the observed and the estimated dbh frequency distributions by (A) Exponential, (B) Ueibull PERC, and (C) Ueibull MLE functions. 67

70 e m .0 v-e “I.“

2 SD \ g 0 "3

ID m , o .

N 1 1 DDI-I (cm) (A)

70 a dame -.un— fl' wwlfllun weaken

3‘” 3:» \ \ 9 ‘° 2 in” In

w . 1 .

40 I l 40 u l 1 mfiIknd CBH(ufi) (B) (C)

Figure 5.6: Bloco 4 - The relationship between the observed and the estieated dbh frequency distributions by (A) Exponential, (B) Heibull PERC, and (C) Heibull "LE functions. CHAPTER 6

A MARKOV CHAIN APPROACH TO PREDICT MORTALITY AND DIAMETER

DISTRIBUTION IN THE BRAZILIAN AMAZON.

6.1. INTRODUCTION

Little is known about forest structure and stand dynamics of Amazonian tropical moist forests. Successive records from representative long-term permanent plots practically do not exist. The problem of reconstructing forest history is greatly compounded by the fact that trees can not be reliably aged, species diversity and spatial heterogeneity are high, and fallen logs decay rapidly. It is important to understand and report the natural changes that occur in representative examples of pristine Amazonian forests, because their composition and structure can be altered by man as the demand for tropical timber species increases.

The main objective of this chapter is to report S-year changes in the overstory structure of an undisturbed tropical moist forest. This will be done by the transition probabilities of the overstory diameter distribution and mortality of this forest, using a first-order Markov chain.

Diameter distribution and tree mortality will be projected

68 69 ahead to 1990 (t+2), based upon a 5-year period of observations completed in 1985 (t+l) and its immediate past in 1980 (t).

A first-order Markov chain is a stochastic process in which the transition probabilities during the time interval

(t and t+l) depend only upon the state an individual is in at time tior upon the knowledge of the immediate past at til, not upon any previous state (Horn 1975, Chiang 1980, and Bruner and Moser 1973). Shugart (1984) pointed out that the time-invariant nature of each of the transition probabilities is an important characteristic of the Markov approach.

Shugart and West (1981) stressed that the importance of understanding forest ecosystems is based not on their age, but on known changes at present. Deterministic models consisting of a single mathematical function (linear trend, polynomial, sinusoids, or exponential growth or decay) have not proven adequate when time series are involved (Morrison 1976).

In tropical moist forests, size may be more important than age. One reason for this is that size may be more ecologically informative than age when it is difficult to make accurate estimates of age (Enright and Ogden 1979).

Division of life-cycles into developmental stages may allow prediction of future behavior more accurately than division into true age-classes. Usher (1966) used size attributes instead of age to develop a model for the management of 70 renewable resources. He stressed that an organism which is in i-th class at time t can be in the same class at time £11, or it can be in a next class of that attribute, or it can have died.

According to Enright and.Ogden (1979), the transition matrix models in general are suitable for the analysis of many biological problems, mainly in studies related to the forest dynamics.

These models have been used intensively in studies of dynamics of populations of plants or animals in many parts of the world. Some examples are: the demography of jack-in- the-pulpit in New York (Bierzychudek 1982); forest dynamics of a population of Araucaria in a tropical rain forest in

Papua New Guinea, and Nothofagus in temperate montane forest in New Zealand (Enright and Ogden 1979); termite succession in Ghana (Usher 1979); forest succession in New Jersey (Horn 1975); the application, although without success. of this model in secondary succession in coastal British Columbia

(Bellefleur 1981); the discussion of some extensions and application of Hornfls Markov approach for forest dynamics in tropical forests (Acevedo 1981); and the application of

Markov model to predict forest stand development (Usher

1966, Usher 1969, Bruner and Moser 1973, Peden et a1. 1973. and Buogiorno and Michie 1980). Alder (1980) also described the transition matrix as a possible tool for analysis of growth and yield data for uneven aged mixed tropical forests. Most of these works include a reasonable review 71 about the theory behind the Markov approach.

Turner (1974), Chiang (1980), and Anderson and Goodman

(1957) are very useful supplemental readings.

6.2. PROCEDURES

The data

The data for this study were collected on the research

area described in Chapter 4.

The Markov model

According to Bierzychudek (11982), a transition matrix

model is a size-classified model or a form of the Leslie

matrix model. The only requirement of this model is that the

population can be divisible into a set of states, and that

there exist probabilities for movement from one state to

another over time (Enright and Ogden 1979).

Here let the states be i, j = l, 2, .u., m. Let the

times of observation be t = 0, l, ...., T, and let Eli (t+1)

(i, j = 1, 2, ...., m) be the probability of state 1 at time £11, given state i at time t. A Markov process {X(t), t E [0,a>]} is said to be

homogenous with respect to time, or time homogenous. if the

transition probability

Pij(tvt+1) = Pr lX(t+1)=j|X(t)=il. i.j = 1, 2, ...., m. 72 depends only on the difference between t and t+1, but not on t or t+l separately (Chiang 1980).

The computation of this probability can be done as follows. First, calculate

Pij = "ii/"i

where: nij = number of individuals in class j.at time t+1, given class i at time 3, and r1- = total number of individuals in class i at time t.

The transition probability matrix of a Markov chain for a n-state process can be set up as:

j=l j=2 j=3 ..... J=m r- “_I 1‘1 P11 P12 913 -°-°- Pim i=2 P21 922 923 -°°-- sz P = (pij) i=3 P31 P32 P33 °°°°° 93m

1:” Lfml sz pm3 "°°° pmm [ The probabilities Pij are nonnegative and the sum p11 + p12

+ pi3 + see Pim = 1a

The transition probability Pij can be of n-step transition probability, pijI“). as the probability that the population goes from state i on one trial to state i 3 trials later.1According to Bruner and Moser (1973), the n- step transition probabilities matrix may be obtained by the 73 equation

PI“) = P“ where PI“) is the matrix of n-step transition probabilities and Pn is the initial transition matrix raised to the n-th power.

In this work, 15 states (i, j = l, 2, 3, u. 15) were established as follow: state 1 = ingrowth (1), states 2 to

14 were defined as dbh classes, from 25 to the generalized class next > 80 cm, in 5-cm interval, and state 15 = mortality (M). Ingrowth is defined as those trees not tagged in 1980 which in 1985 reached dbh >, 25 cm. The time interval t and 511 are respectively, 1980 and 1985.

Tables 6.1, 6.2 and 6.3 present the transition of the absolute frequency of individuals from the state i to state

1 during a 5-year period, respectively for bloco 1, bloco 2, and bloco 4. The state ingrowth does not appear at time 1980 because it means only the movement to the higher dbh class from the generalized dbh < 25 cm class.

The probability for transition among states was based on the frequency of trees which either remained in the same class, moved to a higher class, or died during a 5-year period. Tables 6.4, 6.5 and 6.6 present the transitional matrices for blocos 1, 2 and 4, respectively. These tables were set up using their counterparts, Tables 6.1, 6.2 and 74

6.3, as bases for the computation of probabilities. For

example, the probabilities for the state 25 cm dbh class for

bloco 1 (Table 6.4) were calculated as follows: pq'z = 155/183 = 0.8470, 122,3 = 16/183 = 0.0874, and 112,15 = 12/183

= 0.0656. From all trees in 25 cm dbh class measured in

1980, 84.7% remained in the same class, 8.74% moved to the

30 cm dbh class, and 6.56% died during the period 1980-

1985. The probabilities for other dbh classes and blocks

were similarly determined with the respective counterpart

tables with absolute frequency distribution.

The tw0wstep transition matrix for each block (Tables

6.7, 6.8 and 6.9) were obtained by squaring their

counterparts (Tables 6.4, 6.5 and 6.6), respectively for

blocos l, 2 and 4. These tables represent the probability

for dbh and mortality distribution after two 5-year periods, i.e., for t_+3, year 1990. The two-step transition matrix is

the basis for predicting the distribution of diameter and

mortality for the study area in 1990.

The eigenvalues (111-) of the transition matrix of each

sample plot were determined according to Anton (1973). The

dominant eigenvalue (Al = 1 since each matrix is non-

negative and row sums are 1) and the next largest modulus (

12) were determined. These values provide the ratio (Al/12)

which, according to Usher (1979), indicates the speed with

which the system will approach the ”climax" state. 75

6.3. DISCUSSION OF RESULTS

The projections for 1990 of number of survivors from

1980, the frequency distribution of dbh classes, and the mortality by dbh classes, respectively for blocos 1, 2 and 4 are presented in Tables 6.10, 6.11 and 6.12. These projections were determined based on the product of the two- step transition matrix and the initial values of each state.

Volume stocking in 1990 can be estimated by applying a

reliable individual tree volume equation to the projected diameter distributions. The frequency of individuals per dbh classes is available for each sample plot.

The plot ratios (Al/A2) were 1.15, 1.00 and 1.00,

respectively for blocos 1, 2 and 4. The mean ratio, 1.05, suggests that the studied area will approach the "climax" state slower than the two systems discussed by Usher (1979), mixed hardwoods in Connecticut (ratio 1.34) and in New

Jersey (ratio 1.57). This result makes sense if compared with the distribution of changes in dbh classes and mortality which occurred over a 5-year period. Using Table

6.13, which is the summary'of one-step transition matrix,

the mean estimates of the probability of changes and mortality per plot are respectively 0.1205 and 0.0918. This means that 12.05% of the total number of trees in a plot changed dbh classes, and that 9.18% died during a 5-year period. In an absolute basis, using the mean number of

trees/plot = 631, 76 trees changed classes, 58 died, and 23

(the mean ingrowth/plot) grew into the measurable dbh 76

classes. Thus, these results suggest that the studied area

is not a static population.

The projection for 1990, based on the summary presented

in Table 6.14, also does not show any trends that this population is not changing. The average of the probability

of changes of dbh classes increased to 0.1895 and mortality

to 0.1717.

6.4. CONCLUSION

In the study area, the average rates of mortality and

ingrowth, during a 5-year period of observation, are

respectively 9.18% and 3.72% in relation to the total of

initial number of trees recorded in 1980.

There is no evidence that the probability of mortality

increases as dbh increase. The same trend is observed for

changes in dbh classes (the movement from one class to

another), i.e, the changes are occurring independently of

the diameter size.1As this study dealt with only the control

plots, it will be very interesting to compare these plots

with other experimental plots to see how effective were the

silvicultural treatments to change the rates of mortality

and ingrowth.

The ratio (Al/A2) leads to the conclusion that the

population under investigation is not static, that changes

are still taking place, and that the rates of ingrowth and

death are not perfectly balanced. However, it is also 77 necessary to keep in mind that this population is truncated by size, i.e., only trees with dbh ; 25 cm were involved.

The Markov approach has a lot of potential. It can be used as a baseline to project the mortality and diameter distribution, or at least to predict the direction of future trends, for forest management purposes in natural stands of the Brazilian Amazon. It provides a general insight into the nature of the dyamics of a sample of pristine Amazonian forest which, consequently, will be very helpful to assist decision makers in exploring and understanding the Amazonian forest issues.

In 1990 this procedure will be repeated. Then, the

Markov chain approach will be evaluated and, if necessary, refined based upon a 10-year of observatitun If valid, the projection ahead to year 2000 will be possible. 78

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CHAPTER 7

SHORT-TERM GROWTH OF UNDISTURBED BRAZILIAN AMAZON TROPICAL

MOIST FOREST OF "TERRA FIRME"

7 . 1 . INTRODUCTION

Based upon the available literature about growth and yield studies, the mixed uneven-aged stands of the Amazonian forest are condemned to stay where they have always been.

These forests are not an attractive forest investment because little is known of the past growth and future growth potential. Age and site index, two fundamental variables used in developing even the simplest growth and yield models

(Sullivan and Clutter 1972, Ferguson and Leech 1978, Alder

1980, Smith 1983, and Clutter et al. 1983) are not available. In addition, long-term successive measurements on permanent sample plots are non-existent.

The main objective of this work is to give'a starting point for growth and yield studies in the Brazilian Amazon based on a 5-year period of observation. A major constraint

is that age, site index and total height of trees are not available or even practical to obtain. Therefore, only diameters measured in 1980 and their 5-year increments will

be used in an attempt to project growth and yield. Another

objective is to avoid the problems that the tropical

92 93 countries of southeast Asia have faced in terms of divulgation of their experiences with growth and yield studies. In Peninsular Malaysia, for example, although growth and yield studies were established back in the

1950's, almost thirty years later few analyses have been reported (Tang and Mohd 1981). Revilla (1981) also pointed out that the growth and yield studies reported in Malaysia and Philippines do not reflect the abundance of growth data available.

7.2. PROCEDURES

This work focused on two separate aspects of growth and yield, the current volume estimation for individual trees and prediction of future volume.

In individual current volume estimation, the emphasis was on the selection of the best model to estimate the 1980 and 1985 merchantable volumes based on either single-entry

(dbh as independent variable) or double-entry (dbh and height as independent variables) regression equations. The variable height (H) from double entry models was estimated from diameter-height equations. Several regression equation models were tested. The selection of the best model for volume estimation was based on the Furnival index (Furnival

1961) - adjusted standard error of estimate (SEE) used to compare logarithm equations with non-logarithm equations - residual analysis and the coefficient of determination (R2).

In yield information and prediction, the emphasis was 94

on the development of individual volume growth models based on diameter or basal area increments, the development of a model for volume of 1985 as a function of the diameter or volume measured in 1980, and the use of the exponential

Lotka's growth model for volume prediction on a hectare basis.

The data

The data used to develop models for current individual volume estimations came from Higuchi and Ramm (1985). For

this study, trees with dbh < 20 cm were excluded leaving a

total of 654 cases. Table 7.1 presents the basic distributional characteristics of the data.

For yield information and prediction, the data came

from the three four-hectare permanent plots described in

detail in Chapter 4. I

The quantitative information for all three sample plots

are summarized in Table 7.3. These data refer to 52

botanical families found in the study area, including about

350 different tree species with dbh ; 25 cm. Three families

(Lecythidaceae, Leguminosae and Sapotaceae) contributed 50%

of the total number of trees. Table 7.4 presents the

distribution of frequencies of the three dominant families

in terms of status in 1980, mortality (M), ingrowth (I), and

various classes of periodic increment (PI). 95

Model development

From the available literature, models were selected which matched the variables available for the study area.

For individual tree volume estimation, the following models were tested:

(a) Single-entry models (Loetsch et al. 1973)

v = a + b*02 (1)

v = a + b*D + c*02 (2)

log V = a + b*log D (3)

log V = a + b*log D + c*(l/D) (4)

(b) Diameter/Height models

H=a + b*D + cm2 + d*D3 (Clutter 1963) (5)

log H a + b*(l/D) (Loetsch et al. 1973) (5)

l/H II + b*(l/D) (Rai 1979) (7) a:

log H a + b*log D (Schreuder et al. 1979) (8)

(c) Double-entry models (Loetsch et al. 1973)

log V = a + b*log D + c*log H or V = a*Db*H° (9)

v = a + b*DZ*H (10)

For all models:

log denotes logarithm to base 10. 96

D denotes diameter at breast height (dbh) outside bark

in centimeters (cm). It is measured at 1.3 m above

ground level.

H denotes merchantable height in meters (m), i.e., the

length of stem from the ground to the crown.

V denotes merchantable volume in cubic meters (cu.m.).

For individual yield prediction, the following models were tested:

(a) Increment

d0 = a + b*D + c*D2 (West 1980) (11)

dBA = a + b*D + c*02 (West 1980) (12)

d0 = a + b*(D - 25)2 (West 1981) (13)

Where: do = periodic diameter increment in cm.

dBA = periodic basal area increment in squared meters (sq.m.).

These three models were weighted using the inverse of the estimated sample variance as weight for each diameter class. The weighted models will be equations (14), (15) and (16).

(b) Volume in 1985 = f (1980 vol. or 1980 dbh)

V(85) a + b*D(80) + c*(D(80))2 (17)

0(85) a + b*D(80) (Soekotjo 1981) (18) 97

Where: V(85) = volume estimated in 1985 in cu.m.

0(85) dbh measured in 1985 in cm.

0(80) dbh measured in 1980 in cm.

(c) Lotka equation (Pielou 1977)

Adapting this model to predict volume growth produced

V(t) = V(0)*ert (19)

Where: V(t) = volume at time t (for t = l, 2, n 5-year

periods) in cu.m./ha.

V(0) = volume at time 0 (1980) in cu.m./ha.

r = b - d = the intrinsic rate of natural increase.

b= [I + Increment]/V(t) = ingrowth (I) and increment

rate (flow-in quantity).

d=M/V(t)= mortality (M) rate(flow-out quantity).

7.3. DISCUSSION OF RESULTS

Only 1/3 of the total of species belonging to the three dominant families are considered commercial species by local markets in Manaus. There exists no occurrence of the two most valuable species for exportation of the Amazonian forests, Swietenia macrophylla King (Mahogany) and Cedrela odorata L"

The individual dbh periodic increment (PI) of the study area averaged 1.06 cm, equivalent to 0.21 cm/year. This mean

P1 was estimated based on a population from which 31.3% did 98 not have any increment at all (Table 7.4). The mean dbh for each increment class is also presented in Table 7.4. Note that the maximum mean increment occurred in trees with 40.9 cm dbh, and that zero-increment occurred in trees with 42.5 cm dbh. More than 80% of trees had a PI less than 2 cm.

The average periodic annual increment (PAI), 0.21 cm/year, can be compared with the long-term PAI of 0.10 to

0.12 cm/yr obtained in Puerto Rico, Maricao and Luquillo forests (Weaver 1982), and with the PAI of 0.22 to 0.48 cm/yr from the southern Ontario hardwood forest (West 1979).

The PAI for ”pau-rosa” (Aniba duckei Kostermans) at Ducke

Reserve, about 20 km north of Manaus, was 0.38 cm/year

(Alencar and Araujo 1981). In southeast Asia, however, the dbh PAI's for virgin or managed forests are at least twice as large as the PAI of the study area (Miller 1981 and Tang and Mohd 1981).

Although the PAI is positive, the stand stocking decreased during the period 1980-1985 in terms of number of trees, basal area and volume (Table 7.3). This is explained by a mortality rate which was twice the ingrowth rate.

Another explanation for the decreases is the mean dbh's for mortality and ingrowth, which were 39 cm and 26.3 cm, respectively (Table 7.3).

The regression models for individual trees volume estimation were developed using the ordinary least squares method. The regression summary for these models is presented

in Table 7.2. 99

The diameter/height models did not perform adequately and, therefore, they were not used. Probably the reason for failure in fitting the diameter/height curve is because merchantable height was used instead of total height. All proposed models used total height. In Rad's (1979) work, for example, a R2 = 0.956 was obtained, while the highest R2 of this work was 0.154.

To estimate the current volume of 1980 and 1985, the following equation was used

log V = -3.4033 + 2.2673*log D (3)

This equation was chosen because it presented an appropriate residual distribution, had an acceptable R2 and SEE, and because it was as precise as the equation (4) with three coefficients.

Before developing the proposed regression equations for increment and growth' studies, a contingency table was developed to test the differences in probabilities among sample plots (Conover 1980).‘This test was carried out to test the possibility of pooling the sample plots. In this case let the probability of a randomly selected value from the i-th bloco being classified in the j-th category be denoted by Pij' for i = l, 2, 3, and j = l to 5.

The hypotheses were:

HO: All of the probabilities in the same sample plot are

equal to each other (i.e., plj = p2j = p3j for all j). 100

El: At least two of the probabilities in the same sample

plot are not equal to each other.

The chi-square test for differences in probabilities

was carried.out based on the tabulated data from Table 7.3

for number of trees and the mean diameter for each sample

plot. For contingency tables, the rows (j) were constituted

of different categories (status in 1980, status in 1985,

ingrowth, mortality, and periodic increment), and the

columns (i) by the sample plots (bloco l, bloco 2 and bloco

4). For both number of trees and mean diameter, the null

hypotheses could not be rejected. All sample plots were

combined therefore for further development of the growth and

yield models. .

All increment models developed had very poor fits, as

demonstrated by the low R2 values and high SBEs in Table

7.5. Model fit was not improved through the use of weighted

least squares. Based on these results S-year periodic

increments should not be used as a baseline for projection

of growth and yield of the Amazonian forests. A possible

explanation of this result is shown in Table 7.6a, which

contains the mean, standard deviation, minimum and maximum

increment.by dbh classes.‘The same procedure was used for

dbh when increment classes were considered (Table 7.6b). The

contingency table was used to test the differences in

probabilities for mean, standard deviation and maximum

increment and dbh. There were no differences among these

values for both diameter classes and increment classes. 101

Statistically, this means that the mean increment is not significantly different between.dbh classes, and that the mean dbh is equal for all increment classes.

In contrast, the two models, equations (17) and (18), developed for individual volume and diameter growth performed very well. The explanation for this successful fit can be found in the explanation for the failure of increment models. In model (16), for example, the objective was to study the relationship between the dbh measured in 1985 and

the same dbh measured in 1980. As the increments were very small, non-negative and non-significant, the dependent and

independent variables were approximately equal. In both equations the regression coefficients were highly

significant. With these models one may now predict the

individual volume or dbh growth for another period of time,

for 1990, based on the dbh measured in 1985. In 1990, these

models can be validated and refined using, then, a lO-year period.

Finally, fitting Lotka's model (19) to the data

produced

V(t) = v(o)*e(-0.0347*t)

The intrinsic rate of increase, r, was obtained on a

hectare basis based on the data from Table 7.3 as follows: ll

0' (3.8197 + 12.8010)/ 273.6415 = 0.0617 II

D: 26.1019/273.6415 = 0.0954 102

r = 0.0617 - 0.0954 = - 0.0347

Using this equation, the estimated volume in 1985 is

V(1) = v(o)*e('0.0347*1)

V(1) = 274.7256 cu.m./ha and for 1990

V(2) = V(0):e('0.0347*2)

V(2) = 265.3458 cu.m./ha

Based on the intrinsic rate of natural changes in a 5- year period, the yield estimation for 1985 is very close to

the observed yield (V(85) = 273.642 cu.m). The projection

for 1990 also looks acceptable. This means that Lotkafis model seems very promising to predict the future volume

yield. However, a longer period of observation is necessary

to fully validate this model.

7.4. CONCLUSION

In the study area the dbh increment for individual

trees during the period 1980-1985 averaged 1.06 cm. The

mortality rate was twice the ingrowth rate, and the stand

stocking decreased around 4% during this period. Two models, V(85) = a + b*D(80) + c*(o(80))2 and Lotka's model, could be used to estimate individual tree

volume for the next period with an acceptable reliability. 103

In 1990, then, these models can be retested, validated, and refined, if necessary, using a lO-year period of observation.

There was no indication that dbh could be used to predict either the merchantable height or short-term diameter/basal area increment.

Traditional growth and yield models can not be applied to the Brazilian Amazon forests since age and site index are not available. The only alternative remaining seems to be the use of simpler models based on stand structure monitoring at successive occasions.

The findings of this study suggest that the growth and yield studies are not possible only on temperate forests, but they are also feasible on an undisturbed tropical moist forest in the Amazon. To achieve this goal, however, the best estimation of the current volume and the dependability of the permanent sample plots must be pursued. 104

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c

summary

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159 159

c

c

d

(170)

+

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b b

Regression

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(b) 159

(a) 1002

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Table 9 W U 106

Table 7.3: Characteristics of the data used as yield information and for yield prediction.

STATUS # CASES MEAN TOTAL TOTAL N dbh(cm) ba(sq.m.) vol(cu.m.)

BLOOD 1

1900 620 30.4 02.100 1165.974 1905 594 30.7 70.690 1111.470 Ingrowth 26 26.2 1.403 16.910 Mortality 52 41.3 0.507 127.000 P1 560 1.1 3.695 56.475

BLOCO 2

1900 667 36.7 77.060 1073.065 1905 642 37.3 77.300 1070.691 Ingrowth 20 26.6 1.109 13.421 Mortality 45 37.9 5.463 74.971 Pl 622 1.0 3.001 50.375

BLOCO 4

1900 605 30.0 02.210 1173.139 1905 567 39.1 70.163 1101.529 Ingrowth 24 26.1 1.200 15.506 Mortality 62 37.0 7.053 110.372 P1 543 1.0 2.517 30.762

MEANS

1900 631 30.0 00.726 1137.659 1905 601 30.4 70.056 1094.566 Ingrowth 23 26.3 1.267 15.279 Mortality 53 39.0 7.274 104.400 P1 570 1.0 3.330 51.204

MEANS/hectare

1900 150 20.102 204.415 1905 150 19.514 273.642 Ingrouth 6 0.317 3.020 Mortality 13 1.019 26.102 P1 145 0.034 12.001

P1 - Periodic increment a only trees measured in both occasions, i.e., number of remaining trees in 1905 excluding ingrowth which was not counted in 1900.

107 5

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41 105

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Table 7.6: Mean, standard deviation, minimum and maximum for each (a) dbh classes and (b) increment classes.

(0) Periodic increment (PI) by dbh classes.

INCREMENT (cm)

DBH CLASSES(cm) N MEAN STD.DEV. MIN MAX

25-29.9 531‘ 0.9177_ 0.9854 0.0 8.0 30-34.9 397 1.0091 1.1246 0.0 6.0 35-39.9 252 0.9274 1.1108 0.0 6.0 40-44.9 163 1.1313 1.2349 0.0 8.0 45-49.9 122 0.8852 0.9474 0.0 4.2 50-54.9 84 1.0964 1.3398 0.0 8.0 55-59.9 65 0.9862 1.4453 0.0 8.0 >60 119 0.7483 1.5173 0.0 7.2

overall 1733 0.9573 1.1400 0.0 8.0

(b) 00H by periodic increment (PI) classes.

00H (cm)

PI CLASSEStcm) N MEAN STD.DEV. MIN MAX

0.0-0.49 672 40.6706 16.3201 25.0 116.0 0.5-0.99 303 33.7709 9.3157 25.0 92.0 1.0-1.49 312 35.5929 10.3603 25.0 94.0 1.5-1.99 136 35.6610 0.9577 25.0 62.0 2.0-2.49 130 30.4615 11.4764 25.0 76.0 2.5-2.99 55 39.0727 11.5002 25.0 73.0 >3.00 125 30.7200 12.7459 25.0 91.0

overall 1733 37.0697 13.5655 25.0 116.0

CHAPTER 8

CONCLUSIONS

The forest resources of the study area are declining with respect to number of trees per unit area, growing stock volume and basal area.

The dominant latent root, A.= 0.97, supports the previous affirmation. According to Enright and Ogden (1979). when the intrinsic rate of natural increase (r) is equal to zero, A is equal 1, i.e., the population under investigation is perfectly balanced or the birth rate is equal to the mortality rate; when r > 0, A > 1, the population is increasing and the birth rate is greater than the mortality rate; and when r < 0, A < l, the population is declining or the birth rate is smaller than the mortality rate.

Another reason for the decline is that mortality and

ingrowth are not balanced.'These components averaged, during a 5-year period of observations, 9.18% and 3.72%

respectively, in relation to the total recorded in 1980.

Mortality appears to be independent of diameter size.

However, the survivors are growing at a mean rate of 0.21 cm/year in dbh.

The ratio Al/Az averaged 1.05, suggesting that this population will approach the ”climax” state slower than at

110 111

least two mixed temperate hardwood systems, one in

Connecticut and another in New Jersey.

In terms of utilizable volume stock, the forest of the study area,(control plots of NR experiment) is relatively poor. The total volume including bark averaged 284 cu.m/ha

for all tree species with dbh >, 25 cm. From this total, only about 40% fulfills the minimum size requirements (dbh > 40

cm) for Amazonian forest industries, and from this only about 30% qualifies as economically desirable species. Thus,

the remaining utilizable volume is no more than 35 cu.m/ha.

This is much less than half of the average volume reported

for growing stock of North American temperate forests,

Brazilian temperate forests, or even tropical moist forests

in SE Asia.

In contrast, the area is very important in species

richness. About 350 different tree species from 52 different

botanical families were identified. Lecythidaceae,

Leguminosae and Sapotaceae are the dominant families

contributing more than 50% of all trees tallied.

Among the three hypothesized diameter distribution

functions tested in this study, the Weibull PERC

(distribution whose parameters were computed using the

percentile approach) showed the best fit for the observed

data. The Weibull MLE (the maximum likelihood approach) and

the exponential distributions were very sensitive to

variation within sample plots. When the Weibull shape

parameter, 0, equals 1, an exponential distribution results, 112 but even in this case, the Weibull PERC fitted the observed data as precisely as the exponential function. In addition, the Weibull PERC function is very simple in estimating its parameters; it does not require sophisticated computer capabilities.

The first-order Markov chain analysis allowed projection of the overstory mortality and frequency distribution by dbh classes. Since age and successive records from long-term permanent plots are not available, the Markov approach is a realistic alternative to predict the direction of future trends in the study area.

Traditional growth and yield models cannot be applied to the study area since age and site index are not available. The 5-year increments did not show any indications that they can be correlated with dbh. From this study, Lotka's model appears to be a powerful alternative to replace the traditional growth and yield models. Another alternative is the equation, V(85) = a + b* 0(80) + c*(D(80)f2, which presented a very good fit for the observed data.

All mathematical models developed in this study have as output the abstract model for a management strategy to be implemented in the real world. In 1990, these models should validated and, if necessary, refined based then on a 10-year period of observations. If valid, they will be very helpful to exercise the simulation as a means of determining model time response for a longer period. APPENDIX APPENDIX

Floristic composition of bacia 3 by botanical family

(Developed by Department of Botany of INPA)

1. ANACARDIACEAE

Anacardium spruceanum Benth ex Engl. Astronium - l sni(*) Tapira retusa Ducke

2. ANONACEAE Anaxagorea - l sni Anona ambota Aubl. Bocageopsxs multiflora (Mart.) R.E. Fr. Bocageopsis - l sni nguetia flagelaris Huber Duguetia - l sni Ephe ranthus amazonicus R.E. Fries Ephedranthus - l sni Quatteria olivacea R.E. Fries Guatteria - l sni gseudoxandra cariaceae R.E. Fries Bollinia insignia R.E. Fries var. pallida R.E. Fries Unono sis - l sni Xylopia benthami R.E. Fries Xylopia - l sni

3 . APOCYNACEAE Ambelania acida Aubl. Anacampta - l sni Aspidosperma album (Vahl.) R. Ben. Aspidosperma obscurinervius Azamb. AspidOsperma carapanauba Pichon Aspidosperma - l sni gouma macrocarpa Barb. Rodr. geissospermum ar enteum R. Rodr. Himatanthus sucqua (Spruce) Woodson

4. ARALIACEAE Didymopanax morototoni (Aubl.) Decne. & Planch.

* sni = species not identified for determined genus.

113 114

BOMBACACEAE Bombacopsis - 2 sni's gatostemma milanezii Paula Nov. §cleronema micranthum Ducke Scleronema - l sni

BIGNONIACEAE Jacaranda copaia D. Don. Jacaranda - l sni Tabebuia serratifolia (D. Don.) Nichols.

BORAGINACEAE Cordia - 1 sni

BURSERACEAE gemicrepidospermum rhoifolium (Bth.) Swart. grotium aracouchili (Aubl.) March. grotium heptaphyllum (Aubl.) March. grotium subserratum Engler Protium - 4 sni's Tetra astris unifoliata (Engl.) Cuatr. Tetragastris - 2 snI's Trattinickia - l sni

9. CARYOCARACEAE garygcar pallidum A.C. Smith Caryocar villosum (Aubl.) Pers. 10L CELASTRACEAE Goupia glabra Aubl.

11. CHRYSOBALANACEAE gouepia leptostachya Benth. ex Hook Coue ia - l sni Birtella glandulosa Spreng. Licania a1 a (Ben.) Cuatr. Licania canescens R. Ben. Licania gracilipes Taub. Licania heteromorphg Licania hypoleuca Benth. fiicania kunthiana Hook f. Licania latifolia Benth. ex Hook Licania micrantha Miq. Eicania oblongifalia Standl. Licania reticulata Prance Licania - l sni Parinari montana Aubl.

12. COMBRETACEAE Buchenavia parvifolia Ducke Buchenavia - 2 sni s

115

13. CONNARACEAE Connarus - l sni

14. DICBAPETALACEAE Tapura amazonica

15. DUCKEODENDRACEAE Duckeodendron cestroides Ruhlm

16. EBENACEAE Diospyros bullata A.C. Smith

17. ELAEOCARPACEAE Sloanea - l sni

18. ERYTHROXYLACEAE Erythroxylum - lsni

19. EUPHORBIACEAE Anomalocalyx - l sni Qpnceveiba guianensis Aubl. Qroton lanjouwensis Jablonski Croton - l sni erpetes variabilis Vittien Qavarretia - 1 sni glycidendron amazonicum Ducke Hevea guianensis Aubl. Mabea caudata Pax. ex K. Holhm. Mabea - l sni Micrandra rossiana R.E. Schultes Micrandra siphonioides Bth. Micrandropsis scleroxylon W. Rodr. gausandra macropetala Ducke Pera - 1 sni ngonophora schomburgkiana Miers. ex Bth.

20. FLACOURTIACEAE gasearia combaymensig Tul. gasearia ulmifolia Vahl. ex Von. Casearia - l sni Carpotroche - l sni ____,_Laetia procera (Poepp.) Eichl. Ryania - l sni

21. GUTTIFERAE Qalophyllum brasiliense Camb. Carai a - l sni Clusia - l sni Havetiopsis - l sni Moronobea coccinea Aubl. Moronobea pulchra Ducke Rheedia 2 - sni‘s Symphonia globulifera Linn V smia uckei Maguire 116

Vismia guianensis (Aubl. ) Choisy Tovomita - l sni

22. HIPPOCRATEACEAE Salacia - l sni

23. HUMIRIACEAE Duckesia verrucosa (Ducke) Cuatr. Endopleura uchi (Huber) Cuatr. Humiria balsam1fera (Aubl. ) St. Hill Sacoglottis ceratocarpa Ducke Sacaglott1s - l sni Vantanea macrocarpa Ducke Vantanea parviflora Lam. Vantanea - l sni

24. ICACINACEAE Emmotum - 2 sni's Poraqueiba - l sni

25. LAURACEAE Aniba canelilla (B. B. K. ) Mez. Aniba duckei Kosterm. Aniba ferrea Kubitzki Aniba rosaedora Ducke Aniba terminalis Ducke Aniba - l sni Endlicheria - 4 sni's Licaria canela (Meissn. ) Kosterm. Licaria guianensis Aublet Licaria rigida Kost. Licaria - 3 sni' s Mezilaurus decurrens (Ducke) Kost. Mezilaurus synandra (Mez.) Kosterm. Mezilaurus - l sni Nectandra rubra (Mez.) C.K. Allen Nectandra - l sni Qcotea canaliculata Mez. Ocotea neesiana (Miq.) Kosterm. Ocotea - 9 sni's

26. LECYTHIDACEAE Cariniana decandra Ducke Car1niana micrantha Ducke Corytophora alta Knuth Corytophora rimosa Rodr. Couratari - l sni _schweiIera fracta R. Knuth §schweilera odora (Poepp.) Miers. Eschweilera - 8 sni's Gustavia au usta L. GustavIa elliptica Mori Holopyx1dium latifolium (A. c. Smith) R. Knuth. Lecythis usitata Miers var. paraensis R. Knuth. 117

27. LEGUMINOSAE CAESALPINIODEAE Aldina hetero h lla Spruce ex Bth. Bocoa viridirora (Ducke) Cowan Cassia rubriflora Ducke Copaifera multijug_ Hayne Elizabetha bicolor Ducke Elizabetha princeps Schomb. ex Bth. Elizabetha - l sni Eperua bijug_ Mart. ex Benth. var. glabriflora Ducke §pgrua duckeana Cowan Eperua schomburgkiana Benth. Hymenaea pArvifolia Huber Hymenaea - 4 sni' s Macrolobium limbatum Spr. ex Benth. Macrolobium microcalyx Ducke Peltogyne cat1nggg subsp. labra (W. Rodr. ) M. F.Silva Peltogyne paniculata subsp. pAniculata Benth. Swartzia ingifolia Ducke Swartzia pgnacoco (Aubl. ) Cowan §wartzia polyphyl1g D.C. §wartz1a recurva Poepp. & Endl. Swartzia reticglata Ducke Swartzia ulei Harms Swartzia - 3 sni's §g1erolobium - l sni Vouacapoua pallidior Ducke Tachigalia myrmecophilla (Ducke) Ducke TAch1gAl1a pAn1culata Aubl. Tach1gA11a - l sni

28. LEGUMINOSAE MIMOSOIDEAE Dimorphandra parviflora Spr. ex Bth. Diniz1a excelsa Ducke Enterolobium schomburgkii Benth. Hymenolobium - l sni Inga aff. brevialata Ducke Inga paraensis Ducke Inga cayennensis Benth. Inga - 4 sn1' s Parkia multiju a Bth. Parkia opposit olia Spr. ex Bth. Parkia pendula Benth. ex Walp. Parkia - 2 sn '3 Piptadenia psilostachya (D. C. ) Bth. Piptaden1a suaveolens Miq. g1ptadenia - 1 sni Eithecolobium racemosum Ducke Pithecolobium - 2 sni‘s Sgryphnodendron racemiferum (Ducke) W. Rodr. Stryphnodendron - l sni

29. LEGUMINOSAE PAPILIONOIDEAE Andira parviflora Ducke 118

Andira unifoliata Ducke Andira - l sni DIpteryx alata Vogel Dipteryx ma nifica Ducke Dipteryx odorata (Aubl. ) Willd. DApteryx oppositifolia (Aubl. ) Willd. Dipteryx polyphylla (Ducke) Hub. fiipteryx - 1 sni gymenolobium sericeum Ducke gymenolobium cf. pulcherrimum Ducke gymenolobium - l sni Ormosia sm1thii Rudd. Diplotrop1s - l sni Platymiscium duckei Huber

30. LINACEAE Roucheria callophylla Planch

31. MALPIGHIACEAE gyrsonima stipulacea Adr. Juss. B rsonima - l sni Pterandra arborea Ducke

32. MELASTOMATACEAE Bellucia grossularioides (L. ) Triana Miconia elaeagnoides Cogn. Micon1a re e111 Cogn. Mouriria angulicosta Morley MourirIa - l sn1

33. MELIACEAE Guarea - 2 sni's Trichilia - 2 sni's-

34. MORACEAE Brosimum guianensis Aubl. Brosimum pgtabile Ducke Brosimum pgr1nar1oides Ducke subsp. parinarioides Brosimum utile (H. B. K. ) Pittier Brosimu rubescens Taub. Cecropia scyadophylla Mart. var. juranyana Snethlage Claris1a racemosa R. et P. Cousapoua - 1 sni cusP clusiaefolia Schott F1cus guianensis Desv. HelicostyAis - l sni Maggira calophylla (P.A.E.) C.C. Berg. Magu1ra sclerophylla (Ducke) C. C. Berg. NaucleOpsis caloneura (Hub. ) Ducke Naucleopsis glabra Spruce ex Baill Naucleopsis macrophylla Miq. Perebea mollis (P. E. ) Huber subsp. mollis Perebea mollis (P. S. C. ) Huber Pourouma ovata Trecul. 119

Pseudolmedia - 1 sni Sorocea - 1 sni

35. MYRISTICACEAE gpgpsoneura ulei Warb. lryanthera - 1 sni Osteophloeum lat s ermum (A.D.C.) warb. Virola calophylla Mgf. girola carinata (Bth.) Warb. ViroIa elon ata (Bth.) Warb. Virola c . m1chelii Beckel girola multinervia Ducke girola pavonis (A.D.C.) Smith girola venosa Warb. Virola venosa (Benth.) Warb.

36. MYRTACEAE Eu enia - 3 sni's M rc1a ma na Legrand Mxrc1a a ax (Rich.) D.C.

37. MONIMIACEAE Siparuna dicipiens (Tu1.) A.D.C.

38. NYCTAGINACEAE Neea cf. altissima P. et E. Neea - 2 sni‘s

39. OCHNACEAE Ouratea discophora Ducke Ouratea - 1 sni

40. OLACACEAE Agtandra - 1 ani Qhaunochiton - 2 sni's Beisteria acumitetg (B.B.) Engl. BEISteria barbata Cuatr. . Beisteria - 2 sni's ginquartia guianensis Aubl. Ptychopetalum olacoides Benth.

41. PROTEACEAE Rougala - 1 sni

42. QUIINACEAE Quiina abovata Tul. Quiina - 1 sni Touro ia guianensis Aubl.

43. RHABDENDRACEAE Rhabdodendron amazonicum (Spr. ex Bth.) Bub.

44. RBIZOPHORACEAE Anisophyllea manausensis Pirea 5 W. Rodr. 120

Sterigmapetalum obovatum Kuhlmann

4S. RUBIACEAE Amaioua - 1 sni Duroia fusifera Hook f. ex K. Schum Duroia - 1 sni Elaeagia - 1 sni Faramea - 1 sni Perainandusa - 2 sni' s PTlicourea anisoloba M. Arg. Palicourea cf. leggiflora (Aubl. ) A. Rich.

PTgamea - 1 sni Psychotria prancei Steyermark Remijia - 3 sni' s

46. SAPINDACEAE Mata ba - 1 sni M1cropholis - 1 sni Talis1a - 1 sni Toulicia - 1 ani

47. SAPOTACEAE Achrouteria Egmifera Eyma Achrouter1a - 2 sn1 s Chrysophyllum oppositum (Ducke) Ducke Chrysophyllum anomalum Piree Diplocem venezuelana Aubr. Ecclinusa Bacuri AfiBr. et Pellegr. Ecclinusa ucugu1rana Aubr. 5 Pellegr. Ecclinusa - 2 sn1 s Pranchetella platyphylla (A. C. Sm.) Aubr. Pranchetella - 1 sni Glycoxylon pedicellatum (Ducke) Ducke Lafiétia - 4 sni' s Manilkara amazonica (Huber) Standley Manilkara huberi (Ducke) Chev. Manilkara cavalcantei Pires et Rodr. Manilkara surinamenEis (Miq.) Dubard Micropholis truncifiora Ducke Micropholis guyanensis Pierre Micropholis venulosa Pierre Micropholis rosaainha-brava Aubr. et Pellegr. Microphol1s mensalis (BTehni) Aubr. Microphol1s - S sni' s Myrtiluma eu eniifolia (Pierre) Baill Neoxxthecec uaantha (Sandw.) Aubr. Pouteria ggyanensis Aubl. L. O. A. Teixeira 82 Pradosia verticillata Ducke gr1eure11a manaosens1s Aubr. Pseudolabatié - 1 sni RafilkoEerelIa - 1 sni Ra ala spuria (Ducke) Aubr. R1charde11a manaosensis Aubr. et Pellegr. Richardella macrophylla (Lum.) Aubr. 121

szxgiopsis oppositifolia Ducke SarcauIis brasiliensis (A.D.C.) Eyma

48. SIHARUBACEAE §imaruba amara Aubl. Simaba guianensis Aubl. subsp. guianensis SImaEa cuspidata Spruce

49. STERCULIACEAE §terculia speciosa K. Schum. Sterculia - 1 sni TheoEroma sylvestris Aubl. ex Mart.

SO. STYRACACEAE Stxrax - 1 sni

51. TILIACEAE Apeiba echinata Gaertn. Apeiba burchelii Sprague Luehea - 1 ani

52. VERBENACEAE Vitex triflora Vahl.

'53. VIOLACEAE geonia glycicarpa Ruiz et Pav. ginorea guianensis Aubl. var. subintegrifolia Rinorea racemosa (Mart. et Zucc.) O. Ktze. égphirshox sufihamensis Eichl.

54. VOCHYSIACEAE Erisma bicolor Ducke Erisma fuscum Ducke Qualea clavata Staflen Qualea paraensis Ducke QuaIea cassiquiarensig (Spr.) Warm. Qgglea labourianana Paula nglea brevipediceilata Staflen Vochysia obiflénsis (355.) Ducke LIST 01? REFERENCES LIST OF REFERENCES

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