FACET SIMULATION IN THE IMATACA FOREST RESERVE, …/67531/metadc... · Figuera, Dilcia, FACET...

FACET SIMULATION IN THE IMATACA FOREST RESERVE, VENEZUELA:

PERMANENT PLOT DATA AND SPATIAL ANALYSIS

Dilcia Figuera

Thesis Prepared for the Degree of

MASTER OF SCIENCE

UNIVERSITY OF NORTH TEXAS

May 2006

APPROVED

Miguel Acevedo, Major Professor Paul Hudak, Committee Member and Chair of

the Department of Geography Pinliang Dong, Committee Member Sandra L. Terrel, Dean of the Robert B.

Toulouse School of Graduate Studies

Figuera, Dilcia, FACET Simulation in the Imataca Forest Reserve, Venezuela:

Permanent Plot Data and Spatial Analysis. Master of Science (Applied Geography),

May 2006, 151 pp., 46 tables, 61 figures, references, 15 titles.

Tree diameter data from 29 years of observations in six permanent plots was

used to calculate the growth rate parameter of the FACET gap model for 39 species in

the Imataca forests in Venezuela. The compound topographic index was used as a

measure of differential soil water conditions and was calculated using geographic

information systems. Growth rate values and topographic conditions typical of hill and

valley were input to FACET to simulate dynamics at the species level and by ecological

and functional groups. Species shade-tolerance led to expected successional patterns.

Drought-tolerant/saturation-intolerant species grew in the hills whereas drought-

intolerant/saturation-tolerant species occurred in the valleys. The results help to

understand forest composition in the future and provide guidance to forest management

practices.

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ACKNOWLEDGMENTS

The National Science Foundation (NSF) through grants DBI-9615936, DEB-

0108563 and CNH BCS-0216722 supported this research. Many thanks are due to

INDEFOR, Universidad de Los Andes (ULA), Mérida, Venezuela; particularly to Hirma

Ramírez and Julio Serrano for supplying data from the Imataca permanent plots. The

support of the Universidad Nacional Experimental de Guayana (UNEG), Venezuela, is

also greatly appreciated, especially Luz Delgado, who provided maps, GIS files, data

and feedback. Many thanks are also due to the members of my committee, Dr. Paul

Hudak and Dr. Pinliang Dong, for their support and time that they dedicated to improve

my thesis. Especially, I am very grateful to my major professor, Dr. Miguel Acevedo,

who made this project possible by sharing his knowledge and also for all his help and

time invested.

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TABLE OF CONTENTS

ACKNOWLEDGMENTS...................................................................................................ii

LIST OF TABLES............................................................................................................ v

LIST OF ILLUSTRATIONS............................................................................................viii

INTRODUCTION............................................................................................................. 1

Objectives............................................................................................................. 3

LITERATURE REVIEW................................................................................................... 6

BACKGROUND ............................................................................................................ 12

STUDY AREA ............................................................................................................... 14

MATERIALS AND METHODS ...................................................................................... 17

Data Sources...................................................................................................... 17

Methodology Diagrams....................................................................................... 27

Program Execution ............................................................................................. 34

Steps Followed in Excel to Calculate and Graph Diameter Increment..... 34

Steps to Calculate and Graph Diameter Increment per Diameter Category................................................................................................................. 35

Step to Calculate Total Tree Density (Over All Spdecies) by Category an Year ......................................................................................................... 36

Steps Followed in R to Calculate the Growth Rate Coefficient (G) for Each Species .................................................................................................... 37

Steps to Generate the Digital Maps ......................................................... 37

Steps Followed to Generate the DEM...................................................... 38

Steps Followed for the Flow Accumulation .............................................. 39

Steps Followed to Generate the Compound Topographic Index (CTI) .... 39

Steps Followed in FACET and Excel to Analyze Basal Area ................... 41

Steps Followed in FACET and Excel to Analyze Tree Density ................ 43

RESULTS AND DISCUSSION...................................................................................... 45

Diameter Increment Analysis.............................................................................. 45

Diameter Increment per Diameter and Diameter Increment as a Function of Years ................................................................................................... 45

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Results of R to Analyze Diameter Increment per Diameter Category ...... 48

Tree Density Analysis......................................................................................... 51

Relation between Diameter Increment and Rainfall and Temperature ............... 53

Relation among Years, Diameter increment, and Annual Temperature and Precipitation ............................................................................................. 53

GIS Analysis ....................................................................................................... 65

Generating Digital Map for the Study Area .............................................. 65

Generating the DEM for the Study Area .................................................. 68

Hydrologic Study ................................................................................................ 71

Generating the Flow Accumulation and Flow Direction ........................... 71

Calculation of the CTI .............................................................................. 74

Basal Area Analysis from FACET runs............................................................... 77

Basal Area Analysis Using Sppima-cti.txt in the Valley with 180 cm Precipitation ............................................................................................. 78

Basal Area Analysis Using Grpima.cti.txt in the Valley with 120 cm and 180 cm Precipitation ................................................................................ 86

Basal Area Analysis using grpima.cti.txt on the Hill with 120 cm and 180 cm precipitation........................................................................................ 97

Tree Density Analysis Results using FACET.................................................... 105

Tree Density Analysis using Sppima-cti.txt in the Valley 180 cm Precipitation ........................................................................................... 105

Tree Density Analysis using Grpima.cti.txt in the Valley with 120 cm and 180 cm Precipitation .............................................................................. 109

Tree Density Analysis usin Grpima.cti.txt on the Hill with 120 cm and 180 cm Precipitation ..................................................................................... 115

CONCLUSION ............................................................................................................ 125

A. TREE SPECIES DATA .......................................................................... 129

B. DIAMETER INCREMENT PER SPECIES ............................................. 131

C. CALCULATION OF G USING R PROGRAM......................................... 137

D. FACET INPUT FILES ............................................................................ 143

REFERENCES............................................................................................................ 150

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LIST OF TABLES

Table 1. Plot Information (Delgado et al. 2005)............................................................ 17

Table 2. List of Species Used in this Study .................................................................. 19

Table 3. List of Species Organized by Functional Group: L1 = Small Shade Intolerant, L2 = Large Shade Intolerant, L4 = Large Shade Tolerant, L5 = Small Shade Tolerant. 20

Table 4. List of Species Present in El Dorado Study Plots ........................................... 21

Table 5. List of Species Present in Each Rio Grande Study Plot .................................. 22

Table 6. Parameters for 34 Species Grouped by Light Requirements and Maximum Height (from Delgado et al. 2005). ................................................................................ 23

Table 7. Hypothetical Species Groups ......................................................................... 25

Table 8. Hypothetical Species Groups Organized by Location .................................... 26

Table 9. Paper Map Information................................................................................... 27

Table 10. Species with Better Representation in the Data Set..................................... 49

Table 11. Maximum Diameter Increment for Species in Each Plot .............................. 50

Table 12. Values of G Used for Each Species Present in Each Plot............................ 59

Table 13. Values of G Used for Each Species Present in Each Dorado Plot ............... 60

Table 14. Values of G Used for Each Species Present in Each Rio Grande Plot.......... 61

Table 15. Values Used to Generate G Values for the Plot’s Species............................ 62

Table 16. Final Values of G (Growth Rate Coefficient) for the Plot’s Species .............. 63

Table 17. G values Used for the Species that Are Not Present in the Six Study Plots. 63

Table 18. Values Used for Generate G Values for the 39 Species .............................. 64

Table 19. Definition of Terrain Types ........................................................................... 77

Table 20. Species with Highest Basal Area by Year 500 Using 180 cm Precipitation.. 79

Table 21. Species with Lowest Basal Area by Year 500 using 180 cm Precipitation .... 80

Table 22. Basal Area in the Year 500 Using 180 cm Precipitation ............................... 81

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Table 23. Basal Area at the Beginning and at the End of the Simulation Using 180 cm Precipitation .................................................................................................................. 84

Table 24. Hypothetical Species Groups in the Valley with Highest Basal Area at Year 500 with 120 cm Precipitation ....................................................................................... 88

Table 25. Lowest Basal Area or Basal Area Zero at Year 500 using 120 cm Precipitation .................................................................................................................. 89

Table 26. Basal Area at Year 500 with 180 cm Precipitation........................................ 91

Table 27. Hypothetical Species Groups Basal Area in the Valley at Year 500 with 120 cm and 180 cm Precipitation ......................................................................................... 92



Table 30. Hypothetical Species Groups in the Valley with Highest Basal Area at Year 500 with 120 cm precipitation........................................................................................ 97

Table 31. Hypothetical Species Groups with Highest Basal Area at Year 500 with 180 cm Precipitation............................................................................................................. 99

Table 32. Hypothetical Species Groups Basal Area at Year 500 with 120 cm and 180 cm Precipitation........................................................................................................... 100

Table 33. Basal Area at the Beginning and at the End of the Simulation using 120 cm Precipitation ................................................................................................................ 102

Table 34. Basal Area at the Beginning and at the End of the Simulation Using 180 cm Precipitation ................................................................................................................ 104

Table 35. Species with Highest Tree Density at Year 500 with 180cm Precipitation.. 106

Table 36. Species with Lowest Tree Density at Year 500 with 180 cm Precipitation . 106

Table 37. Tree Density at the Beginning and the End of the Simulation with 180 cm Precipitation ................................................................................................................ 108

Table 38. Species with Highest and Lowest Tree Density at Year 500 with 120 cm Precipitation ................................................................................................................ 111

Table 39. Tree Densities at Year 500 with 180 cm Precipitation ................................ 112

Table 40. Tree Densities at Year 500 with 120 cm and 180 cm Precipitation ............ 113

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Table 41. Hypothetical Species Group Density at Year 500 with 120 cm Precipitation.................................................................................................................................... 117

Table 42. Species Group Tree Density at Year 500 with 180 cm Precipitation ........... 119

Table 43. Species Group Tree Density at Year 500 with 120 cm and 180 cm Precipitation ................................................................................................................ 120

Table 44. Hypothetical Species Group Density at the Beginning and End of the Simulation with 120 cm Precipitation........................................................................... 122

Table 45. Hypothetical Species Group Density at the Beginning and the End of the Simulation Using 180 cm Precipitation ........................................................................ 124

Table 46. Example of the Original Data File for Dorado 1 Plot.................................... 130

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LIST OF ILLUSTRATIONS

Figure 1. Map of the Imataca Region, Southeast ......................................................... 16

Figure 2. General Methodology Diagram ...................................................................... 28

Figure 3. Methodology Diagram Used in GIS to Generate Digital Maps and Hydrology Study ............................................................................................................................. 29

Figure 4. Methodology Diagram Used in R and Excel Analysis to Calculate G Value... 30

Figure 5. Methodology Diagram Used in FACET to Generate Basal Area and Tree Density of 500-Year Simulations ................................................................................... 31

Figure 6. Methodology Diagram Used in WinSCP2 and Excel to Export and Analyze Basal Area and Tree Density of 500-Year FACET Simulations..................................... 32

Figure 7. CTI Calculation............................................................................................... 39

Figure 8. Using CTI to Define Terrain Types for FACET Simulations............................ 41

Figure 9. Scenario Definitions for FACET Simulations ................................................. 42

Figure 10. Diameter Increment with Respect to Diameter for Aspidosperma marcgravianum (Canjilon Negro) Species in Dorado 4 Plot .......................................... 46

Figure 11. Diameter as a Function of Time (in years) for Aspidosperma marcgravianum (Canjilon Negro) Species in Dorado 4 Plot.................................................................... 47

Figure 12. Diameter Increment as a Function of Time (in Years) for Aspidosperma marcgravianum in Dorado 4 Plot ................................................................................... 48

Figure 13. Diameter Increment with Respect to Diameter for Each Species in Dorado 1 Plot ................................................................................................................................ 50

Figure 14. Total Tree Density of All Species in Dorado 1 Plot...................................... 51

Figure 15. Total Tree Density for Each Species in Dorado 1 Plot ................................ 52

Figure 16. Diameter Increment and Annual Rainfall in Dorado 1 Plot .......................... 53

Figure 17. Diameter Increment and Annual Temperature in Dorado 1 Plot.................. 54

Figure 18. Regression Analysis between Rainfall and Diameter Increment ................. 55

Figure 19. Regression Analysis between Temperature and Diameter Increment ........ 56

Figure 20. Growth Rate Calibration of Dorado 1 Plot (Tasa1.R) with Maximum Point (Line Goes through Maximum Value)............................................................................ 58

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Figure 21. Map of the Study Area Using Scanned Paper Maps ................................... 66

Figure 22. Map of the Study Area Using Satellite Image.............................................. 67

Figure 23. Contour Lines Layer.................................................................................... 69

Figure 24. 30m by 30m Meter DEM Generated from the Contour Lines Layer ............ 70

Figure 25. Flow Direction Layer Generated with ArcToolbox ....................................... 72

Figure 26. Flow Accumulation Layer Generated Using Hydrology Function in ArcToolbox .................................................................................................................... 73

Figure 27. Catchments Area ........................................................................................ 74

Figure 28. Slope Layer ................................................................................................. 75

Figure 29. Compound Topographic Index (CTI)........................................................... 76

Figure 30. Basal Area (m2/ha) with Respect to 39 Studied Species Using 180 cm Precipitation .................................................................................................................. 79

Figure 31. Basal Area (m2/ha) during 500 Years of Simulation with 180 cm Precipitation .................................................................................................................. 83

Figure 32. Percent of Relative Basal Area with Respect to Years with 180 cm Precipitation. ................................................................................................................. 86

Figure 33. Basal Area (m2/ha) with Respect to Hypothetical Species Groups with 120 cm Precipitation............................................................................................................. 88

Figure 34. Basal Area (m2/ha) with respect to Hypothetical Species Groups with 180 cm Precipitation............................................................................................................. 90

Figure 35. Basal Area of Hypothetical Species Group during a 500-Year Simulation with 120 cm Precipitation .............................................................................................. 93

Figure 36. Basal Area (m2/ha) of Hypothetical Species Group during 500 Years of Simulation with 180 cm Precipitation............................................................................. 95

Figure 37. Basal Area (m2/ha) of Hypothetical Species Groups on the Hill at Year 500 with 120 cm Precipitation .............................................................................................. 97

Figure 38. Basal Area (m2/ha) of Hypothetical Species Groups in the Year 500 with 180 cm Precipitation...................................................................................................... 98

Figure 39. Basal Area of Hypothetical Species Groups during 500 Years with 120 cm Precipitation ................................................................................................................ 101

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Figure 40. Basal Area of Hypothetical Species Groups during 500 Years with 180 cm Precipitation ................................................................................................................ 103

Figure 41. Tree Density per Species at Year 500 with 180 cm Precipitation.............. 105

Figure 42. Tree Density with Respect to Years during 500 Years of Simulation Using 180 cm Precipitation.................................................................................................... 107

Figure 43. Functional Group during 500 Years Using 180 cm.................................... 109

Figure 44. Tree Density at Year 500 with 120 cm Precipitation.................................. 110

Figure 45. Tree Density per Species in Year 500 with 180 cm Precipitation .............. 112

Figure 46. Tree Density per Species during 500 Years with 120 cm Precipitation ..... 114

Figure 47. Tree Density with Respect to Years during 500 Years with 180 cm Precipitation ................................................................................................................ 115

Figure 48. Hypothetical Species Group Density at Year 500 with 120 cm Precipitation.................................................................................................................................... 116

Figure 49. Hypothetical Species Group Density at Year 500 with 180 cm Precipitation.................................................................................................................................... 118

Figure 50. Tree Density with Respect to Years during 500 Years of Simulation with 120 cm Precipitation........................................................................................................... 121

Figure 51. Tree Density with Respect to Years During 500 Years of Simulation with 180 cm Precipitation........................................................................................................... 123

Figure 52. Diameter Increment with Respect to Diameter for Each Species in Dorado 2 Plot .............................................................................................................................. 132



Figure 55. Rio Grande 5 Plot Diameter Increment with Respect to Diameter for Each Species ....................................................................................................................... 135

Figure 56. Diameter Increment with Respect to Diameter in Rio Grande 6 Plot......... 136

Figure 57. Growth Rate Calibration of Dorado 2 Plot (tasa.R) ................................... 138


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Figure 60. Growth Rate Calibration of Rio Grande 5 Plot (tasa.R)............................. 141

Figure 61. Growth RFate Calibration of Rio Grande 6 Plot (tasa.R)........................... 142

1

INTRODUCTION

Studies of environmental systems are very complex because many variables and

relationships must be taken into account. Carey et al. (1994, p.264) point out that it

takes many years of observations to understand and predict forest dynamics. Long

term monitoring renders large amounts of data, which lead to a deeper understanding of

variables and relationships in permanent forest plots. These data are analyzed with

tools such as geographical information systems (GIS), computer models, and remote

sensing. These tools facilitate data processing, analysis, and predictions in forest

ecosystems.

To help understand the dynamics of forests, there are modeling softwares such

as FACET and ZELIG that simulate forest ecosystems. FACET is an extension of

ZELIG that adjusts environmental variables (temperature and rainfall) for topographic

location (Acevedo et al. 1995, 2001a,b). In this study, these physical variables are

considered together with biological parameters such as allometric relations and growth

rate coefficient (G) of the species.

GIS are useful for analyzing forest dynamics at the landscape level by enabling

terrain analysis using different layers such as elevation, soils, precipitation, and

geomorphology. The generation of a digital elevation model (DEM) allows for spatial

hydrologic studies and the calculation of important variables like the compound

topography index (CTI).

Important to forest modeling is the growth rate of the trees and the relationship

between forest growth and the many environmental variables that affect growth. The

growth rate of trees of each species helps to predict the structure and composition of

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the forest in the future. Studies of modeling and simulation of tree diameter growth

show that growth rate is dependent on the tree’s diameter. Botkin points out (1993,

p.32) that it is possible to derive an equation for diameter change as a function of height

if it is accepted that diameter is related to tree height.

This thesis focuses on 29 years of observational data from the Imataca forest

reserve, Venezuela. The data include circumferences of tree species in six permanent

plots in the study area. Also included are precipitation and temperature data from

meteorological stations near the study area. GIS layers include rivers, roads, elevation

contour lines, and digital satellite images. Contour lines were derived from topographic

maps from the upper Botanamo watershed located in the Imataca forest reserve.

The principal purpose of this study is to generate various required parameters of

FACET version 2.4 modified by acevedo March 2006 forest modeling. Also, use these

varialble to run FACET to analyze the tree density and basal area of the Imataca forest

in simulations of long-term (500 years) behavior. The parameters generated in this

study are diameter increment, G, flow accumulation, and slope.

Fernandez (1995) and Delgado (2000) conducted earlier efforts to calculate

growth rates and perform ZELIG and FACET simulations in this area. Fernandez

(1995) executed the ZELIG model finding the parameters values for growth rate of each

species in the studied forest, but she did not use spatial analysis and it was not possible

to use detailed growth data. Therefore in her recommendation the author suggested

that future research should use better data to improve the calibration of growth rates

(Fernandez, 1995, p.142). Delgado (2000) worked with the FACET modeling program

adding the topographic factor and scaling to the landscape. She used GIS to analyze

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forest dynamics at the landscape level. However, her study was conducted at low

spatial resolution (500 x 500 m cells) for a large study area. For this purpose, she

employed maps at a scale of 1:250,000. Also, her study did not calculate the growth

rate from tree level data because the data were not available (Delgado, 2000, p.122).

Therefore, the present study uses more complete data to re-calculate the growth

rate tree (G) by species improving the results of the previous studies. In addition, it

generates a finer spatial resolution DEM, which is then used to calculate the flow

accumulation and the slope. These two variables are used to calculate the CTI and to

input terrain parameters to run FACET to simulate the Imataca forest dynamics in a

period of 500 years.

To evaluate the differential response due to growth rate and shade tolerance,

FACET is executed at the species level using conditions of abundant water availability

(180 cm of precipitation in typical valley terrain types). Different simulation scenarios

are then defined to run FACET, using 120 cm and 180 cm of annual precipitation

together with typical valley and hill terrain types, by defining hypothetical species group

that respond differentially to soil water moisture. The results of the simulations are used

to analyze the dynamics of tree density and basal area of the studied forest. This study

is important because it can help understand the future dynamics of the forest in this

area and provide guidance to forest management practices.

Objectives

General Objectives:

Generate the growth rate coefficient (G) for each species, and use GIS to

generate flow accumulation, and slope to run FACET in order to analyze the tree

4

density and basal area dynamics for the Imataca forest reserve in 500 years of

simulation. This objective can be divided in two groups of specific objectives.

1. Generate required variables to run FACET

1.1 Determine the dynamics of diameter change for the species during the study

period

1.2 Determine diameter increment with respect to diameter

1.3 Determine diameter increment with respect to years

1.4 Determine G for each species

1.5 Create a digital elevation model (DEM) for the study area

1.6 Generate the flow direction and the flow accumulation for the study area

1.7 Generate the slope

1.8 Generate the CTI

1.9 Take values of slope and flow accumulation where the CTI value is high, as

in valley terrain, and where the CTI value is low, as in hill terrain;

2. Run FACET using species and groups of species for several scenarios

2.1. Use 39 species with the calculated G values using 180 cm precipitation in

valley terrain

2.2 Use 16 hypothetical tree groups under four different conditions or scenarios:

120 cm and 180 cm precipitation in valley terrain types and 120 cm and 180 cm

precipitation in hill terrain;

3. Analyze data generated by FACET

3.1. Analyze FACET results using 39 species with the calculated G value and

180 cm precipitation and determine the following:

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3.1.1 Basal area per species and as a function of time

3.1.2 Relative basal area (in percent) per functional group

3.1.4 Tree density per species and as a function of time

3.1.5 Relative tree density (in percent) per functional group

3.2 Analyze FACET results using 16 hypothetical tree groups with 120 cm and

180 cm of precipitation and determine the following

3.2.1 Basal area per species

3.2.2 Basal area as a function of time

3.2.3 Tree density per species

3.2.4 Tree density as a function of time

In the process of generating the required parameters to run FACET other

important and relevant analyses were performed. For example, temperature and rainfall

data were used to generate diameter increment with respect to temperature and

precipitation. In order to test the previous results, the following analyses were

performed:

• An exploratory data analysis by means of charts

• A quantitative statistical analysis using regression

In addition, to improve the understanding of the study area other results were

generated, such as producing a digital map from scanned paper maps and generating

the hillshade display of DEM.

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LITERATURE REVIEW

It is difficult to establish the age and growth of trees without growth ring data

(Carey et al. 1994, p.255-256). The purpose of several studies, research, and modeling

is to understand the growth rate of a specific forest. Some studies are based on long-

term permanent plots; as for example Harcombe et al. (2001) have studied a hardwood

forest in Texas using data from 18 years of observation.

The rate of growth for young trees under optimum condition is high, but low for

old ones. The G parameter represents growth under optimum conditions, i.e., when no

environmental factor reduces growth (Botkin, 1993). Metabolic processes of the tree

are affected by temperature. It changes the dynamics of the reactions resulting in

changes of photosynthesis rates and respiration, which affects the growth of the tree

(Botkin, 1993, p.46). Forest composition change can be predicted from gradual

changes in tree diameter and height. However, disturbance may vary the expected

changes of the forest, making it less predictable (Harcombe et al. 2001). Strong

environmental changes like hurricanes, droughts, and ecological disasters could affect

the normal behavior of the forest. Tree mortality is an important factor to consider. As

Carey et al. (1994) point out, mortality data is helpful to predict forest dynamics and help

guide more effective management strategies.

As Acevedo, et al. 1996 point out, there are two major forest modeling

approaches, which are: JABOWA-type simulators (or gap models) and Markovian or

patch transition models. The JABOWA or gap model approach simulates forest

dynamics linking environmental, demographics and growth parameters on a tree-by-tree

basis (Botkin, 1993). Patch transition models describe changes in forest type using

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transition probabilities, which can be determined from aerial photographs, remote

sensing imagery or historical data.

This thesis will use a gap model, FACET, which simulates the forest by

calculating the diameter growth and tree mortality in a small area but which can be

scaled-up to larger areas as a patch transition model (Acevedo et al. 1995, 2001a,b).

This type of landscape modeling could be very complex; therefore, to simplify this

simulation, trees could be grouped by their functional role, their structural role or a

combination of both. For example, functional groups can be defined according to

shade-tolerance and tree size (Acevedo et al. 1995).

FACET is derived from ZELIG, which is a spatially explicit gap model, based on a

grid of cells. In this model each cell corresponds to a gap model plot. “The development

of this model stems from a project that aims to use a generic forest simulator as a

framework for cross-site comparison” (Urban, 1993). FACET simulates the forest,

adjusting the temperature, rainfall and radiation for topographic location. ZELIG and

FACET are executed on Unix or Linux workstations, where hundreds of model grids are

run (Urban et al. 1999). FACET requires the growth rate coefficient for each species in

order to simulate the dynamics of the forest.

Because environmental conditions vary geographically, it is important to use

spatial analysis tools, such as geographical information systems (GIS). This tool can

assist in the solution of a variety of geographic problems. They provide effective tools

to assist in decision-making, the explanation of patterns, and the predictions of spatial

arrangements or distribution during long periods of time (DeMers, 2002, p.1). In fact,

GIS is a useful tool to perform hydrological analysis. Many hydrologic features, such as

8

flow accumulation, flow direction, and watershed definition are extracted through terrain

analysis from a digital elevation model (DEM).

A DEM allows constructing a model of the watershed and stream network, and it

can be used to show where the water flow will begin and where it will arrive (DeMers,

2002). Commonly, DEMs are used to model the terrain shape and they are important

determinants in the over land flow of water. DEMs, satellite images, and digital pictures

are raster files. A raster file consists of grid cells used in GIS to model continuous

features of the earth. Each grid cell within a raster file is considered to have

homogeneous values, but the values for individual cells can vary. From the DEM it is

possible to calculate the flow direction and flow accumulation of a watershed.

Watershed or drainage basins can be delineated based on water flow direction.

The watershed and stream networks can be defined after the aspect of the slope for

each cell is calculated. This process requires several spatial analysis options, such as

contour, slope, hill shape, reclassification, raster calculator, and conversion from

features to raster. The delimitation of watersheds is a necessary element of nearly all

surface hydrological modeling.

DeMers (2002) explains some relevant steps to define watershed and basins and

how to determine the accumulation of flow and model the length of flow within a

watershed.

• First, evaluate the slope and aspect to determine the flow direction for cells in the

grid.

• Second, determine if sinks exist, if they do, they must be filled. Therefore, they

do not interfere with the overall flow-modeling process.

9

• Third, apply three different functions that can be applied, which are:

* Accumulated flow is cumulative weight of all grid cells flowing into each

down slope cell in the grid.

* Watershed function: determine the contributing area (basin) to the

overall flow.

* Stream network function: Evaluate the number of cells and the stream

ordering of the overall stream network.

• Fourth, apply the flow direction function. It is a key to perform the rest of surface

hydrological functions. This involves calculation of the direction of flow for every

grid cell in the grid by using a DEM as the input grid. The software searches the

eight surrounding grid cells and evaluates the direction of the maximum drop.

* Higher accumulation zones could easily be used to identify stream channel

cells.

* Stream ordering is a method of assigning numerical values to streams based

on their position in the network.

Running water tends to accumulate in some areas of the surface such as sinks.

However, when soils are saturated or sinks are filled, the remaining water runs off to

other areas. The run-on coefficient, the storage coefficient, and the run off coefficient

can be used to study the dynamics of the water in specific areas, in the following

manner:

• Run-on: represents the rainwater that runs onto a low-lying plot from

surrounding areas.

• Storage: represents the pooling of surface water.

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• Run-off: Once the soil is saturated, the remaining water runs off and is no

longer accounted for.

FACET’s surface water input includes rainfall (minus canopy interception) and

run-on from upstream areas. A fraction of this total input is removed to represent run-off

and the rest is allowed to percolate into the soil at a rate that depends on soil type and

percent of saturation.

Storage, calculated from the topographic position of the plot, is used to prevent

all of the remaining water from running off once the soil has reached saturation. If the

plot has non-zero storage, then some portion of that water will remain on the plot,

keeping the soil saturated. Contributions from run-on can be determined through a

series of GIS hydrological calculations that start with the digital elevation model (DEM)

layer, and based on a secondary topographic attribute known as the compound

topographic index (CTI).

This index is calculated using the values for the accumulated water in any area

and the tendency of this water to move out because of gravity. It is also known as the

steady state wetness index that calculates the catenary landscape position (Gessler et

al. 1996). The CTI is defined using the catchments area and the slope for each cell in

the study area. This relation is shown in the following equation:

lntan( )

AsCTIβ

⎛ ⎞= ⎜ ⎟

⎝ ⎠ (1)

Where:

As = the specific catchment area

tanβ = the slope angle

11

Catchment area and slope are derived by the hydrologic analysis in GIS using

the DEM and the stream layer. CTI is calculated using the GIS raster calculation

properties. Slope is calculated directly from the DEM, while flow accumulation is

derived from slope and aspect. Flow accumulation for each cell represents the number

of cells in the DEM layer that contributes run-on to that cell, and specific catchment area

is calculated from flow accumulation. Large catchment area and flat terrain produce

high values of CTI.

12

BACKGROUND

Two theses mentioned above (Fernandez, 1995, and Delgado, 2000) constitute

important background and reference for this study. Fernandez (1995) calculated growth

rates in order to execute the ZELIG model. However, as discussed above, her study

was not able to use species level data (Fernandez, 1995, p.142). In the second thesis,

Delgado worked with the FACET modeling program adding the topographic factor and

scaling to the landscape. The data available for her study were trees already lumped by

diameter category (Delgado, 2000, p.122). Her research did not calculate the growth

rate from tree level data. Therefore, it is necessary to use field data to re-calculate the

growth rate tree by tree to improve the results of the previous studies.

A postulate of models of forest dynamics is that growth rate is dependent on the

tree’s diameter. The rate of growth for young trees under optimum condition is high, but

low for old ones (Botkin, 1993, p.46). Accepting the assumption that the diameter is

related to tree height, it is possible to calculate an equation for diameter change as a

function of diameter (Botkin, 1993, p.32). Bark area is approximately proportional to the

product of tree diameter (D) and tree height (H); assuming a cylindrical stem, this area

is πDH. Growth rate is the difference between productivity and maintenance. Because

maintenance costs are proportional to bark area, growth rate decreases as diameter

and height increase. In addition, it is well known that diameter, which is easy to

measure and monitor, responds to environmental changes (Botkin, 1993, p.32). The

influence of environmental conditions on growth rate could be studied taking in

consideration the temperature and soil moisture data in the study area. Soil moisture

data are not readily available, but rainfall is related to it. Metabolic processes of the tree

13

could be affected by changes of conditions, such as different values of temperature and

soil moisture. As Botkin (1993, p.46) points out, different kinds of environmental

changes produce reactions in the trees, which results in changes of photosynthesis

rates and respiration, which affects the growth of the tree.

Some studies take their required data from long-term permanent plots, as for

example those in bottomland hardwood forests in Texas. Data from 18 years of

observation were used to study the development and change of the forest during 1980

to 1998 after some disturbances in the area (Harcombe et al. 2001). Detailed studies of

forest changes help put empirical bounds on the complexity and uncertainty, and thus

promote the progress of satisfactory and robust synthetic theory of forest dynamics

(Harcombe et al. 2001, p.19).

The present study uses data from six permanent plots in the Imataca forest

reserve. The tree circumferences in theses plots have been measured during 29 years

(1971 – 2000).

14

STUDY AREA

The study area is located in the Imataca forest reserve, which is located in the

Bolívar State in Venezuela (Figure 1). Venezuela is one of the countries in South

America with a great portion of natural forest reserves. It shares the forest Amazon

world reserve with Brazil and Colombia. This fact makes these forests an interesting

study area. Several studies have been carried in this area, with the goal of knowing

more about the ecosystem dynamics of the tropic humid forests.

As Delgado (2000) points out, Bolívar state is occupied by 17,000,000 hectares

of natural forest. This forest is distributed into three forest reserves, which are Imataca,

Paragua, and Caura. 37.8% of the total forest reserve is used for forest permanent

production. The Imataca reserve has 32.1% of its total area on production management

plan. The two most important commercial activities in the Imataca reserve are mining

and forestry. Twelve forest concessionaries and about 300 formal mining

concessionaries are present in the area.

Imataca is one of the four largest forest reserves in Venezuela. It is located in the

Guayana region of Northeast of Venezuela, on the border with the reclamation with

Guyana zone and south of the Orinoco River. The limits of the reserve are in the north

with the Orinoco River, Caño Piacoa, Brazo Imataca and Punta Playa in the

international limit with reclamation zone with English Guyana. In the east it extends

from Punta Playa following the border with the international reclamation zone with

English Guyana until the Escalera mountain range, in the west with the Yuruani, Cuyuni,

and Grande rivers until the Nuria high plateau and Grande river, by the south-west by

Dolomita hill and Guayana castle.

15

Imataca covers a total area of 3.2 million hectares. Forests with valuable timber

and genetic resources cover about 80% of its surface. Humid tropical forests are

predominant with about 25m of height. There are also forests with medium and low

height but in less proportion. As a forestry reserve, Imataca is an "Area of Special

Management Regime" (area bajo régimen de administración especial – ABRAE), which

means it must be managed under a special environmental management plan.

Venezuela’s forest service (SEFORVEN) grants concessions to harvest wood within the

Imataca reserve. Typically, these concessions range from 80,000 to 160,000 hectares

and are granted for periods that range from 20 to 40 years (World forest movement,

2004).

Forest ecosystems of the Guayana region include semi-deciduous, deciduous,

evergreen forests, and numerous plant, insect, and animal species. An important

number of species at risk of extinction are present in Imataca forest. Also, part of the

reserve has five native Indian ethnic, which are Warao, Arawako, Karina, Akawaio and

Pemon.

Delgado (2000) summarizes some general and important characteristics of the

Imataca area. It includes many streams and rivers that provide water for communities

in the States of Bolívar and Delta Amacuro. Air temperature is relatively constant, with

monthly average of 26˚C (the maximum is 27.1˚C and the minimum 24.4˚C). The

annual mean precipitation is 1950mm, showing largest precipitation in May and June

(220.57mm and 248.51mm respectively). Mean solar radiation is between 319

(cal/cm2/day) in January and 443 (cal/cm2/day) in September. Topographically, the

relief is varied, with a mosaic of valleys and hills. Hills have elevations ranging from

16

50m to 250m, but reaching 500m of elevation towards the western limit of the reserve.

Basic intrusive rocks and alluvial sediments constitute the major geological formations.

Soils are of residual origin (ultisols, inceptisols and entisols), affected by a strong

weathering process which has caused the lost of mineral and soluble elements.

Figure 1. Map of the Imataca Region, Southeast

17

MATERIALS AND METHODS

Data Sources

Permanent plot data was provided by INDEFOR (Institute of forestry development) of

the Universidad de los Andes (ULA), Merida, Venezuela. These data consist of tree

diameter from six permanent plots. This information was monitored for a period of

observation of 29 years from 1971 to 2000. Table 1 was adapted from Delgado et al.

2005. It shows the total area, location and number of trees in the studied plots.

Table 1. Plot Information (Delgado et al. 2005)

Position # Study Plot

Name Location Latitude Longitude Area Number of Trees

1 Dorado 1 2 Dorado 2 3 Dorado 3 4 Dorado 4

El Dorado Km 98

Bolivar State Venezuela

06° 05’ 16.8’’ 61° 24’ 41.9’’ 1 ha 725

5 RioGrande5 6 RioGrande6

Rio Grande El Palmar

Bolivar State Venezuela

08° 06’ 37.9’’ 61° 41’ 23.4’’ 0.5 ha 346

Adapted from Delgado et al. 2005

Data for each plot include (See Appendix A):

• Plot data, such as:

- Name of the plot

- Location (Altitude, State, Km)

- Size

• Tree data:

- Identification number of the tree

- Common species name

- Scientific species name

18

- Circumference data of 21 observation years from June 1971 to

June 2000

In addition, this study used the temperature and rainfall data from the Anacoco

weather station, supplied by the hydrologic and meteorological information department

in Venezuela (dirección de hidrología y meteorología, sistema nacional de información

hidrológica y meteorológica, SINAIHME). This is the nearest weather station to the

study forest plots. Unfortunately, as in many other studies, there were not weather

stations in the plots. In fact, such convenient correspondence of weather and growth

data is rare (Botkin, 1993, p.52).

There are about 50 species in each plot. However, this study only took in

consideration the species that were studied in the FACET model developed in the

Master’s thesis of Delgado (2000), which are the 39 species shown in Table 2.

19

Table 2. List of Species Used in this Study

In FACET these 39 species were organized by functional group (L1, L2, L4, and

L5) when entered in the input file sppima-cti.txt (Appendix D). See Table 3.

# Scientific Name Common Name 1 Alexa imperatricis Leche de cochino 2 Aspidosperma marcgravianum Canjilon Negro 3 Carapa Guianensis Carapa 4 Castostemma commune Baraman 5 Chimarrhis microcarpa Carutillo 6 Clathrotropis brachypetala Caicareño 7 Couratari pulchra Capa de tabaco 8 Erisma uncinatum Mureillo 9 Eschweilera decolorans Cacao

10 Eschweilera grata Cacaito 11 Licania alba Hierro 12 Licania densiflora Hierrito 13 Manilkara bidentata Purguo 14 Mora excelsa Mora 15 Pouteria egregia Purguillo 16 Protium neglectum Azucarito 17 Sterculia pruriens Majagua 18 Toulicia guianensis Carapo blanco 19 Anaxagorea dolichocarpa Yara yara negra 20 Apeiba aspera Cabeza de negro 21 Brownea coccinea Rosa de montaña 22 Cecropia sciadophylla Yagrumo 23 Coccoloba caurana Arahueque 24 Cordia fallax Alatrique negro 25 Himatanthus articulata Mapolo 26 Hirtella triandra Ceniza negra 27 Inga splendens Guamo 28 Protium heptaphyllum Tacamajaca 29 Rollinia exsucca Anoncillo 30 Schefflera morototoni Sun-Sun 31 Simarouba amara Cedro blanco 32 Sloanea guianensis Aleton 33 Talisia hexaphylla Cotoperi 34 Trichilia schomburgkii Suipo 35 Eschweilera subglandulosa Majaguillo 36 Pentaclethra macroloba Clavellino 37 Protium decandrum Caraño 38 Especie1. Jobo 39 Especie2. Canelo

20

Table 3. List of Species Organized by Functional Group: L1 = Small Shade Intolerant, L2 = Large

Shade Intolerant, L4 = Large Shade Tolerant, L5 = Small Shade Tolerant

# Functional Groups Scientific Species Name Common Species

Name

1 L1 Apeiba aspera Cabeza de negro2 L1 Cecropia sciadophylla Yagrumo3 L1 Cordia fallax Alatrique negro4 L1 Himathantus articulata Mapolo5 L1 Inga splendens Guamo6 L1 Scheflera morototoni Sun-Sun7 L1 Simarouba amara Cedro blanco8 L2 Chimarrihis microcarpa Carutillo9 L2 Clathrotropis brachypetala Caicareno

10 L2 Sterculia pruriens Majagua11 L2 Toulicia guianensis Carapo blanco12 L2 Protium heptaphyllum Tacamajaca13 L2 Sloanea guianensis Aleton14 L2 Talicia hexaphylla Cotoperi15 L2 Trichilia schomburgkii Suipo16 L4 Aspidosperma marcgravia Canjilon amaril17 L4 Eschweilera grata Cacaito18 L4 Pouteria egregia Purguillo19 L4 Protium decandrum Caraño20 L4 Alexa imperatricis Leche de cochino21 L4 Carapa guianensis Carapa22 L4 Catostemma commune Baraman23 L4 Couratari pulchra Capa de tabaco24 L4 Erisma uncinatum Mureillo25 L4 Eschweilera decolorans Cacao26 L4 Licania alba Hierro27 L4 Licania densiflora Hierrito28 L4 Manilkara bidentata Purguo29 L4 Mora excelsa Mora30 L4 Protium neglectum Azucarito blanco31 L4 Eschweilera subglandulosa Majaguillo32 L4 Pentaclethra macroloba Clavellino33 L4 Especie1. Jobo34 L4 Especie2. Canelo35 L5 Anaxagorea dolichocarpa Yara yara negra36 L5 Brownea coccinea Rosa de montaña37 L5 Coccoloba caurana Arahueque38 L5 Hirtella triandra Ceniza negra39 L5 Rollinia exsucca Anoncillo

21

Some species are not present in the six study plots. The species present in each

Dorado plot are shown in Table 4 and the ones present in Rio Grande plots in Table 5.

Table 4. List of Species Present in El Dorado Study Plots

Plot Name # Scientific Name Common Name Dorado1 1 Protium neglectum Azucarito 2 Eschweilera decolorans Cacao 3 Mora excelsa Mora 4 Clathrotropis brachypetala Caicareno 5 Chimarrihis microcarpa Carutillo 6 Aspidosperma marcgravianum CanjilonNegro 7 Simarouba amara CedroBlanco 8 Protium heptaphyllum Tacamajaca Dorado2 1 Protium neglectum Azucarito 2 Eschweilera decolorans Cacao 3 Mora excelsa Mora 4 Trichilia schomburgkii Suipo Dorado3 1 Eschweilera decolorans Cacao 2 Chimarrihis microcarpa Carutillo 3 Inga splendens Guamo 4 Licania densiflora Hierrito 5 Sterculia pruriens Majagua 6 Cecropia sciadophylla Yagrumo 7 Protium neglectum Azucarito 8 Eschweilera grata Cacaito 9 Trichilia schomburgkii Suipo 10 Protium heptaphyllum Tacamajaca Dorado4 1 Eschweilera decolorans Cacao 2 Aspidosperma marcgravianum CanjilonNegro 3 Licania densiflora Hierrito 4 Licania alba Hierro 5 Pouteria egregia Purgillo 6 Trichilia schomburgkii Suipo 7 Protium neglectum Azucarito 8 Protium heptaphyllum Tacamajaca

22

Table 5. List of Species Present in Each Rio Grande Study Plot

Plot Name # Scientific Name Common Name RioGrande5 1 Catostemma commune Baraman 2 Eschweilera decolorans Cacao 3 Inga splendens Guamo 4 Licania densiflora Hierrito 5 Alexa imperatricis LecheCochino 6 Sterculia pruriens Majagua 7 Eschweilera subglandulosa Majaguillo 8 Protium neglectum Azucarito 9 Eschweilera grata Cacaito 10 Toulicia guianensis CarapoBlanco 11 Pentaclethra macroloba Clavellino RioGrande6 1 Eschweilera decolorans Cacao 2 Carapa Guianensis Carapa 3 Toulicia guianensis CarapoBlanco 4 Licania densiflora Hierrito 5 Licania alba Hierro 6 Alexa imperatricis LecheCochino 7 Sterculia pruriens Majagua 8 Trichilia schomburgkii Suipo 9 Eschweilera grata Cacaito 10 Protium heptaphyllum Tacamajaca

In order to run FACET several parameters are required, such as G, precipitation,

and elevation. For example, FACET has a file called sppima-cti.txt (Appendix E) that

contains the species parameters, such as G, maximum tree height (Hmax), maximum

diameter (Dmax), exponential coefficient (b2) and curvature empiric coefficient (b3).

Some of these parameter values were updated (Table 6) using the results from the

recent study by Delgado et al. (2005).

Table 6 was adapted from Delgado et al. 2005. In the table, species are grouped

by their respective functional group. In FACET these ecological groups are classified as

medium shade intolerant (L1), large shade intolerant (L2), large shade tolerant (L4), and

medium shade tolerant (L5).

23

Table 6. Parameters for 34 Species Grouped by Light Requirements and Maximum Height (from

Delgado et al. 2005).

# Scientific name Functional Group

Hmax (m)

Dmax (cm) b2 b3

1 Alexa imperatricis 20 25 -0.0366 0.80 2 Aspidosperma marcgravianum 25 45 -0.0242 0.80 3 Carapa Guianensis 25 40 -0.0135 0.40 4 Castostemma commune 30 65 -0.0278 0.80 5 Chimarrhis microcarpa

L5

25 50 -0.0302 0.60 6 Clathrotropis brachypetala 28 65 -0.0504 1.20 7 Couratari pulchra 30 55 -0.0610 1.20 8 Erisma uncinatum 25 35 -0.0638 1.40 9 Eschweilera decolorans 20 30 -0.0790 1.00 10 Eschweilera grata

L

25 35 -0.0766 1.60 11 Licania alba 35 80 -0.0352 1.00 12 Licania densiflora 40 96 -0.0073 0.60 13 Manilkara bidentata 40 105 -0.0129 0.60 14 Mora excelsa 40 85 -0.0238 1.00 15 Pouteria egregia 40 90 -0.0188 0.80 16 Protium neglectum 55 180 -0.0090 0.80 17 Sterculia pruriens 40 90 -0.0167 0.80 18 Toulicia guianensis 35 75 -0.0274 1.00 19 Anaxagorea dolichocarpa 35 95 -0.0194 0.60 20 Apeiba aspera 35 85 -0.0178 0.60 21 Brownea coccinea 40 95 -0.0202 0.80 22 Cecropia sciadophylla 45 120 -0.0160 0.80 23 Coccoloba caurana 45 120 -0.0250 1.00 24 Cordia fallax 40 100 -0.0160 0.60 25 Himatanthus articulata 40 65 -0.0175 0.60 26 Hirtella triandra

L4

40 60 -0.0150 0.80 27 Inga splendens 35 80 -0.0202 0.80 28 Protium heptaphyllum 35 65 -0.0617 1.60 29 Rollinia exsucca 40 60 -0.0343 1.20 30 Schefflera morototoni 30 85 -0.0387 0.80 31 Simarouba amara 35 50 -0.0288 0.80 32 Sloanea guianensis 40 100 -0.0295 1.00 33 Talisia hexaphylla 35 70 -0.0477 1.40 34 Trichilia schomburgkii

L2

35 45 -0.0606 1.60 Adapted from Delgado et al. 2005

Delgado et al (2005) excluded some species due to the lack of trees for this

species. The species that she left out were:

- Apeiba aspera

24

- Cecropia schiadophylla.

- Simaruba amara

- Anaxagorea dolichocarpa

- Species1.

- Species2.

Furthermore, she includes two additional species, which are:

- Tetragastris altissima

- Mabea piriri

The previous tables contain real data for 39 species located in six different plots

in the study area. However, it was not possible to find data on soil water response for

each species. In order to study the water response of species in different locations and

rainfall values, it was necessary to use 16 hypothetical species groups (Table 7.)

These groups of species are based on their shade tolerance (pioneer or

intolerant, intermediate, and tolerant), tree size (small, medium, large) and soil water

stress response (drought-tolerant/saturation-intolerant, and drought-

intolerant/saturation-tolerant).

The drought-tolerant/saturation-intolerant species are typical of sites with low CTI

such as hills (or “lomas” for their name in Spanish), whereas drought-

intolerant/saturation-tolerant are typical of sites with high CTI such as valleys (or “valles”

for their names in Spanish).

Combining shade-tolerance (three classes), tree size (three classes) and soil-

moisture response (two classes, valley and hill) we obtain a total of 3x3x2=18 possible

25

species groups. However, pioneers are only of size small and medium, so we obtain

only 16 species groups (Table 8)

Table 7. Hypothetical Species Groups

# Hypothetical Species Groups Nomenclature 1 Intermediate large valley INgrva 2 Intermediate medium valley INmeva 3 Intermediate small valley INpeva 4 Tolerant medium valley PImeva 5 Tolerant small valley PIpeva 6 Tolerant large valley TOgrva 7 Tolerant medium valley TOmeva 8 Tolerant small valley TOPeva 9 Intermediate large hill INgrlo 10 Intermediate medium hill INmelo 11 Intermediate small hill INpelo 12 Tolerant medium hill PImelo 13 Tolerant small hill PIpelo 14 Tolerant large hill TOgrlo 15 Tolerant medium hill TOmelo 16 Tolerant small hill TOPelo

26

Table 8. Hypothetical Species Groups Organized by Location

# Soil-moisture Response (High CTI – Valley) (Low CTI – Hill)

Shade Tolerance Tree Size Nomenclature

1 Intermediate Large INgrva 2 Medium INme 3 Small INpeva 4 Valley Pioneer (Intolerant) Medium PImeva 5 Small PIpeva 6 Tolerant Large TOgrva 7 Medium TOmeva 8 Small TOPeva 9 Intermediate Large INgrlo 10 Medium INmelo 11 Small INpelo 12 Hill Pioneer (Intolerant) Medium PImelo 13 Small PIpelo 14 Tolerant Large TOgrlo 15 Medium TOmelo 16 Small TOPelo

The data used in GIS study was acquired from the laboratory of geographical

analysis and ecological modeling (GEOECOLAB) of the University of Guayana (UNEG)

in Venezuela. These are maps at scale 1:250,000 of the Botanamo watershed area.

These maps were used to create study area maps. Some of the digital files obtained

were:

• Isoline.dbf (Contour Lines Layer)

• Hidrologia.shp (Rivers Layer)

• Vialidadbotanamo.shp (Roads Layer)

Topographic paper maps were acquired from the geography department at the

University of North Texas (UNT). These are maps at scale: 1:100,000 and with a

contour interval of 40m for the Bolivar state area in Venezuela where the Botanamo

Watershed is located. The specific information for the three maps is shown in Table 9.

27

Table 9. Paper Map Information

Index Adjacent Sheets

Name Scale Contour Intervals

Projection Year

7838 Guasipati, Venezuela

1:100,000 40 meters Transversal Mercator – Horizontal Data; La Canoa

1977

7938 Cabezeras del Rio Botanamo, Venezuela.


1982

7937 Boca de Corumo, Venezuela


1982

Methodology Diagrams

The following figures summarize the methodology employed in this thesis. Figure

2 is the general approach followed. Then

Figure 3 illustrates the GIS approach to generate digital maps and the hydrology study.

Figure 4 shows the steps in R and Excel Analysis to calculate the G value. Figure 5

contains the steps followed in WinSCP2 and Excel to export and analyze basal area

and tree density of 500-year simulations generated by FACET. Lastly, Figure 6 displays

the steps followed in FACET to generate basal area and tree density for 500-year

simulation runs.

28

Figure 2. General Methodology Diagram

Use of Excel

Use of R

Diameter increment per year

D = ∆D/∆t

Use of GIS

Use of FACET

Growth rate coefficient (G)

Flow direction

Flow accumulation

Compound Topography Index

CTI

Basal area analysis Density analysis

Slope Catchment

Low CTI Large CTI

Take values of Flow accumulation &

Slope

29

Figure 3. Methodology Diagram Used in GIS to Generate Digital Maps and Hydrology Study

River Layer

Create digital DEM using the Contour Lines

Layer

Contour Lines Layer Scan Paper Maps

Georeference the map using control points in

the River Layer.

Create Maps of the area using scanned maps, contour lines and river

layer.

Contour

Lines

Use DEM and Stream Layer to perform the

Hydrology Study.

Perform the compound topographic index (CTI)

CTI = ln (As / tan β) As = Catchment Area

Create the Slope Layer

Slope

1. DIGITAL FILE * River Layer * Contour Lines (40m contour interval)

2. PAPER MAP (with contour line) Map scale: 1:100.000. 40m Contour Intervals

(Botanamo Watershed Area)

CTI values

Flow direction layer

Catchment area

Flow accumulation layer

River Layer

30

Figure 4. Methodology Diagram Used in R and Excel Analysis to Calculate G Value

Select Species used in Delgado (2000) (Excel file with 39 Species)

Use excel to convert tree circumference

in Diameter D = C / (10π)

Use excel to obtain “Diameter increment“

per year D = ⌠D/⌠t

Graph: Diameter increment with respect to diameter

Graph: Diameter as function of years

Diameter increment with respect to years for each species

Graph: Annual rainfall and diameter increments Graph: Annual

Temperature and diameter increments

Function: Fdiam cat.R

Program: Clasediam-dens1.R

Function: Fdiam_cat_dens1

Excel file (e.g: Dorado1) with Species data for tree level.

Years (1971-2000)

Excel file with Rainfall Data.

Years (1972-2000)

Excel file with Temperature Data. Years (1972-2000)

Text file: “Diameter increment for each species”

Text file: “Parameter_ combined.txt” (hmax, dmax, b2, b3)

Program: Tasas.R

Graphs of “G” Parameter

Choose the best G values and save in excel file

Excel file with selected “G” values

Graph: Total Density

Graph: Density of the each plot per species

Program: Calibra.R

Use a macro in Excel to convert excel document (D per each plot) in .txt file to be

used in R (e.g: Dorado3CacaoDiam.txt)

Use R to perform different program (Data used: e.g; Dorado1MoraDiam.txt, parcela1-spp.txt)

Program: Clasediam.R

31

Figure 5. Methodology Diagram Used in FACET to Generate Basal Area and Tree Density of 500-

Year Simulations

Actualize the sppima.cti.txt file in FACET

Precipitation (120 or 180

cm)

Excel file (parameter.txt) with G, b2, b3, Hmax, Dmax parameter

Values of Flow accumulation & Slope, elevation taken from areas

with low CTI

Values of Flow accumulation & Slope, elevation taken from areas

with high CTI

Text file (grpima) with group’s

parameters in hill and valley

Actualize the file entvalle120, entvalle180, in FACET

Actualize the file entloma120, entloma180,

entspcloma180 in FACET

RUN facet with entspploma180

RUN facet with

entloma120

RUN facet with

entloma180

RUN facet with

entvalle120

RUN facet with

entvalle180

Basal Area (ztracer) Density (density)





32

Figure 6. Methodology Diagram Used in WinSCP2 and Excel to Export and Analyze Basal Area and

Tree Density of 500-Year FACET Simulations

The process illustrated in Figure 6 was followed for each z.tracer and z.density

FACET file sets. It was not needed to generate percent of relative basal area and

density per hypothetical species group (Table 8).

Copy the result files (z-tracer, z-density) using

WinSCP2 to an explorer folder

Export the z-tracer file into Excel

Calculate the SUM of Basal Area for all

Species (SUM AB)

Calculate the Relative Basal Area (BA/SUM all BA)

Calculate the % of Relative Basal Area ((BA/∑ BA )* 100 )

Group the % of Relative Basal Area per Functional

group (L1, L2, L4, L5)

% Relative Basal Area per Functional

Graph Basal Area per species in year 500

Graph Basal Area per specie during 500 years

Export the z-density file into Excel

Calculate the SUM of density for all Species

(SUM AB)

Calculate the Relative Density (BA/SUM all BA)

Calculate the % of Relative Density

((Den / ∑Den) * 100 )

Group the % of Relative Basal Area per Functional

group (L1, L2, L4, L5)

% Density Basal Area per Functional Group

Graph: Density per species in year 500

Graph: per species during 500 years

Basal Area (ztracer) &

Density (density) for each scene

33

Selected Programs

Two software tools were used to process and analyze the 29 years of observation data.

• Microsoft Excel 2000. (Graphs were generated using this tool)

• R 1.6.2. (Programs were written to graph and analyze the data)

Two software tools were used to process and analyze the maps and GIS layers.

• ArcGIS (Different layers and hydrologic studies were generated using this tool)

- ArcInfo or ArcMap

- ArcCatalog

- ArcToolBox

One software tool, FACET, was used to simulate the forest ecosystem.

• FACET (The parameter values calculated in this thesis were used in this program

to simulate the forest. FACET runs under the Unix or Linux Operating Systems.

Excel is a popular and easy to use Microsoft Office worksheet. Calculations and graphs

can be performed using this program. The first set of graphs and calculations in this

study was made using Microsoft Excel.

However, the use of a faster and more flexible program was required to graph all

the species for each plot in one presentation sheet to perform regressions. R is a

powerful statistical program, which is also very useful to compose graphs. Programs

were written in R to plot required graphs with all the species in each plot and to provide

an automatic tool to other users.

GIS is a useful tool to perform hydrological and terrain analysis, such as DEM,

CTI, flow accumulation, flow direction and stream order. ArcMap or ArcInfo use the

34

raster calculator option to calculate the different layers used in the hydrologic analysis.

However, other software tools, such as ArcHydro can also perform this type of analysis.

Program Execution

Steps Followed in Excel to Calculate and Graph Diameter Increment

Initially, the data were processed and analyzed in Excel. The following steps were

implemented:

1. The tree circumference (C) in cm for each species during the years 1972-2000

was converted to diameter (D) in mm with the following formula

10 /D C π= × (2)

2. From the diameter data and the range of the years, the diameter increment per

year ∆D/∆t was obtained using the following equation:

( ) ( )D D t t D tt t

Δ + Δ −=

Δ Δ (3)

3. The following graphs in Excel were produced for each species:

- Diameter increment with respect to diameter for each species. (Figure 10)

- Diameter as a function of time in years. (Figure 11)

- Diameter increment with respect to time in years (Figure 12)

4. The diameter increment per year obtained in step two and the annual rainfall and

mean annual temperature taken from the Anacoco weather station for the study

period, February 1972 to July 2000, were used to calculate the relationship of

35

diameter increment to annual rainfall and annual mean temperature. As a result,

the following graphs were plotted:

- Diameter increment and annual rainfall as a function of time (Figure 16)

- Diameter increment and annual temperature as a function of time (Figure 17)

5. The diameter increment values were regressed against rainfall data and

temperature data for each species:

- Regression analysis of annual rainfall and diameter increments (Figure

18).

- Regression analysis of annual temperature between diameter increments

(Figure 19).

Steps to Calculate and Graph Diameter Increment per Diameter Category

R was used to obtain diameter increment per diameter category, performing the

following tasks:

1. Tree diameter for each species were taken from the Excel file and saved as a

text file. This text file was used as input to the R program.

2. Calculation and graphs, two programs were written to be executed in R which

are:

- Clasediam.R

- Fdiam-cat.R

The Clasediam.R program calls the function Fdiam-cat.R. This function defines

the years of observation, which are 21 years for the Dorado plots and 20 years for the

Rio Grande plots. Fdiam-cat.R uses nineteen (19) diameter categories, from 10 cm to

36

100 cm in steps of 5cm. The code sentence in the Fdiam-cat.R function was established

as a vector:

categ <- c(10,15,20,25,30,35,40,45,50,55,60,65,70,75,80,85,90,95,100)

Clasediam.R generates two result files: one is a text file and another is an Excel

file. These files contain the diameter increment for each species per diameter category

and the graphs of the diameter category for each species in each plot (Figure 13).

Clasediam.R also calls the graph_write_function to graph the information.

Fdiam-cat.R, called by clasediam.R, returns the average of the diameter

increment per category. Results and graphs generated from these programs were

analyzed to understand the forest changes during 21 years of observation.

Step to Calculate Total Tree Density (Over All Spdecies) by Category an Year

Two programs were created to calculate the tree density by diameter category for each

plot:

• Fdiam-cat-dens1.R

• clasediam-dens1.R

The years of observation for Dorado’s plots are 21 years and for Rio Grande’s

plots are 21 years. Nineteen (19) diameter categories were established: from 10 cm to

100 cm in intervals of 5 cm. That is to done with the following line of code:

categ = (10,15,20,25,30,35,40,45,50,55,60,65,70,75,80,85,90,95,100)

Two types of graph were obtained: one for the total tree density of the plot

(Figure 14) and another one for the tree density of the plot for each species (Figure 15).

37

Steps Followed in R to Calculate the Growth Rate Coefficient (G) for Each Species

1. The text file generated by Clasediam.R using the R program containing the

diameter increment for each species per diameter category was used to calculate

G. (e.g., Dorado1catdiam.txt)

2. Also, it was necessary to use a file called paramsp-combined.txt, which was

taken from Delgado (2000). This file contains the allometric coefficients:

maximum diameter of the tree species (dmax), maximum height of the tree

species (hmax), the exponential coefficient (b2), and curvature empirical

coefficient (b3).

3. To estimate the values of G two programs were executed in R. These are:

- tasa.R and

- calibra.R

The tasa.R program calls the function called calibra.R. Also, tasa.R reads the

values “hmax, dmax, b2, b3” from the file called paramsp_combined.txt and the

diameter increment for each species. The diameter increment was generated by

calibra.R and saved as a text file, such as Dorado1catdiam.txt or

RioGrande6catdiam.txt. The values of G calculated are shown in Figure 20. Initial

values of G in tasa.R were varied until the line reaches the maximum diameter

increment value. Finally, the calculated G values were written in an Excel file called

parameter.xls (Table 15)

Steps to Generate the Digital Maps

Three maps (Table 9) were scanned into three JPEG files. In order to use only the study

area, which is located at Latitude (DMS): 7° 45' 0’’ N Longitude (DMS): 61° 0' 0’’W, the

38

required areas were cut and saved in new files. Then, georeferencing was performed in

GIS for all the new raster or digitized maps with a reference dataset, such as the rivers

and the roads of the area.

In order to provide spatial reference information and to correct the shifted digital

maps, the three images were added to ArcMap together with the roads and rivers

layers. To georeference the maps, approximately 12 control points or links were created

from the scanned map and the river layer. Subsequently, the image was rectified using

Update Georeferencing. Finally, the maps were matched together with river and roads

using Update Display.

These newly rectified maps files did not have spatial reference. Therefore,

Arctoolbox was used to assign the same spatial reference of river layer, which is

WGS_1984_UTM_Zone_20N.

To generate the map of the study area, the digital maps were overlaid with the

river and road layers in ArcMap (Figure 21).

Steps Followed to Generate the DEM

Hydrologic features can be extracted from the terrain using digital elevation

model (DEMs). The contours lines layer (Figure 23), which has a contour interval of 40

meter, was used to generate the DEM using the function Topo to Raster in ArcToolBox.

ArcToolBoxl Program:

- Spatial Analyst Tools

- Interpolation

- Topo to Raster

Input: ContourLine.shp (Field: Curva, Type:Contour)

39

Output: OriginalDEM

Cell size: 30 meter

Steps Followed for the Flow Accumulation

Important hydrologic features, such as flow accumulation and CTI are generated

using raster calculator or ArcToolbox in ArcView. Flow accumulation is generated using

the flow direction. The following steps were followed:

1) ArcToolbox in ArcView

Hydrology

Flow direction

Input: DEM

2) ArcToolbox in ArcView

Hydrology

Flow accumulation

Input: Flow direction

Steps Followed to Generate the Compound Topographic Index (CTI)

The following steps were generated using raster calculator and ArcToolBox in

ArcView. Each calculation generates a layer as shown in the diagram of Figure 7.

Figure 7. CTI Calculation

1. Get the flow accumulation layer obtained previously. This layer shows the

number of cells that contribute run-on to the cell in the DEM layer.

2. Use the flow accumulation layer to calculate the catchment area:

ArcView

Raster Calculator

CTI = ln (As / tan (β))

Flow accumulation

As = Catchments area tan(β) =Slope Layer

40

_ ( 1) 900Catch area FlowAcc= + × (4)

3. Generate the Slope layer from the DEM:

ArcToolbox in ArcView

Surface

Slope

Input: DEM

4. Eliminate zeros from the Slope layer from the DEM:

Mod_Slope = Slope + 0.001

Note: To eliminate zeros “0”.

Spatial Analysis / Raster-Calculator

Mod_Slope [Slope – dem_utm]+ 0.001

5. Finally, calculate the compound topographic index CTI:

ArcView - Spatial Analysis

Raster-Calculator

ln( _ / mod_ )CTI Catch area slope= (5)

Where:

Catch_area = As

Mod_Slope = Tanß

Two terrain types, “Valley” and “Hill”, were defined according to the CTI. The

areas with low CTI will represent higher elevation or hill and areas with high CTI will

represent lower elevation or valley. Then, values of flow accumulation and slope were

taken from these two types of terrains to run FACET (Figure 8).

41

Use of FACET


CTI

Low CTI High CTI

Take values ofFlow accumulation & Slope

Hill Valley

Use of FACET


CTI


CTI

Low CTI High CTI

Take values ofFlow accumulation & Slope

Hill Valley

Figure 8. Using CTI to Define Terrain Types for FACET Simulations

Steps Followed in FACET and Excel to Analyze Basal Area

The values of G, flow accumulation and slope calculated above were used to run

the FACET model. Using a Linux based computer the growth rate coefficients (G) were

changed in the sppima-cti.txt file (Appendix D). Also in this file, the new values of

Hmax, Dmax, b2, and b3 were actualized for some species using information from

Table 6.

Before running FACET, the annual precipitation, elevation, flow accumulation

and other important variables were changed in a file called ent.txt. Several ent.txt files

were written to control FACET runs. These are entloma120, entloma180, entvalle120,

entvalle180, and entsppvalle180 (Appendix D). Using the hypothetical species groups

(Table 8), FACET was executed for each scenario obtained by combining two different

values of precipitation, 120 cm and 180 cm precipitation and two terrain types (valley

42

and hill). Using the 39 species in Table 3., FACET was run for 180 cm precipitation, in

the valley. The following graphs summarize these FACET scenarios.

Figure 9. Scenario Definitions for FACET Simulations

FACET generated output files, such as z-tracer, z-print, and z-density. These

output files were copied to a Windows based computer using WinSCP2 and then

imported in Excel. The z-tracer file contains the basal area during the 500 simulation

years. The 39 species and the hypothetical groups were used to graph the following:

- Basal Area (BA) by species at the end of the run (Figure 22 and Figure 23),

- Basal Area as a function of years (Figure 24 and Figure 25).

Only the z.tracer values obtained from the 39 species were used to calculate the

percent of relative basal area per functional group, performing the following calculation:

Basal area per species in each year was summed to obtain the total.

39 species sppima.cti.txt

16 hypothetical groups (grpima)

Hill

RUN facet with entspploma180

RUN facet with

entvalle120

RUN facet with

entvalle180

RUN facet with

entloma120

RUN facet with

entloma180

180 cm Prec.

Valley Hill

180 cm Prec.120 cm Prec. 180 cm Prec.120 cm Prec.

Note: loma = hill

43

Then basal area for each species was converted to relative basal area (RBA, in %)

using the following equation for each species i:

100ii

i

BABARBA

= ×∑

(6)

Since in FACET the 39 species are grouped by functional group the total basal

area by functional group was calculated. Then, graphs of percent relative basal area

per functional group with respect to simulation year were constructed (Figure 26 and

Figure 27).

Steps Followed in FACET and Excel to Analyze Tree Density

As mentioned above, FACET also generates a file called z-density. This file was used

to graph and analyze tree density (TD). The following steps are the same used in the

previous steps to analyze basal area. The z.density file was imported into Excel. This

information was used to graph the following:

- Tree density by species (Figure 41)

- Tree density with respect to years (Figure 42)

- Tree density with respect to hypothetical species groups (Figure 45 and Figure

48)

- Tree hypothetical species group density with respect to years (Figure 46 and

Figure 47).

In order to calculate the percent of tree density the following calculation was

performed;

The sum of all tree density per species in each year was calculated

44

Then tree density was converted to relative tree density (TDR, in %) using the

following equation:

100ii

i

TDTDRTD

= ×∑

(7)

Since in the file sppima-cti.txt in FACET the species are grouped according to

functional group, the total tree density per functional group was calculated. Then, a

graph was constructed to illustrate relative tree density per functional group with respect

to years (Figure 43).

45

RESULTS AND DISCUSSION

Diameter Increment Analysis

Diameter Increment per Diameter and Diameter Increment as a Function of Years

Diameter increment increases rapidly for small values of diameter, reaches a

maximum and then decreases for large values of diameter. This is clearly shown for the

Aspidosperma marcgravianum (Canjilon Negro) species in the Dorado 4 plot (Figure

10). This behavior is shown for four trees identified as 5, 9, 92 and 113. Also, in the

graph of diameter as a function of year, tree number 92 increases at a faster rate for

years 81-90 compared to years 92-81 (Figure 11). Also, the diameter increment with

respect to years for each species is shown in Figure 12.

46

Aspidosperma marcgravianum (Canjilon Negro)

-1.00

-0.50

0.00

0.50

1.00

1.50

2.00

0 5 10 15 20 25 30 35 40 45 50

Diameter (cm)

Diam

eter

Incr

emen

t (cm

/yr)

5 9 92 113

Figure 10. Diameter Increment with Respect to Diameter for Aspidosperma marcgravianum

(Canjilon Negro) Species in Dorado 4 Plot

47


0

5

10

15

20

25

30

35

40

45

70 72 73 75 77 79 81 83 85 87 89 91 93 95 97 99

Years

Dia

met

er (c

m)

5 9 92 113

Figure 11. Diameter as a Function of Time (in years) for Aspidosperma marcgravianum (Canjilon

Negro) Species in Dorado 4 Plot

48


-1.00

-0.50

0.00

0.50

1.00

1.50

2.00

70 72 73 75 77 79 81 83 85 87 89 91 93 95 97 99

Years

Dia

met

er In

crem

ent (

cm/y

r)

5 9 92 113

Figure 12. Diameter Increment as a Function of Time (in Years) for Aspidosperma marcgravianum

in Dorado 4 Plot

Results of R to Analyze Diameter Increment per Diameter Category

Results obtained from the execution of the R programs were analyzed. In the

programs, the diameter increment per year of each species was calculated, but the

number of trees is low for some species. It was not possible to calculate the growth rate

by category in some cases, such as Eschweilera decolorans, Clathrotropis

brachypetala, and Chimarrihis microcarpa in Dorado1 plot, Eschweilera decolorans and

Trichilia schomburgkii in Dorado2 plot, Chimarrihis microcarpa and Inga splendens. in

Dorado3 plot, Eschweilera decolorans, Licania densiflora, Pouteria egregia, and

49

Trichilia schomburgkii in Dorado4 plot, Eschweilera subglandulosa, Alexa imperatricis,

and Eschweilera subglandulosa in Dorado5 plot, and Licania alba, Trichilia

schomburgkii and Sterculia pruriens in Dorado6 plot because of the poor representation

per category.

Nevertheless, other species were better represented, resulting in a better

estimation of the growth rate. For example, Mora excelsa (Mora) is present in Dorado 1

and Dorado 2 and it had trees belonging to the category measuring between 15 cm and

95 cm. For Mora excelsa (Mora) it was possible to observe that the growth rate is larger

for categories 55 cm to 70 cm. Therefore, for Mora excelsa (Mora) the diameter

increment increases rapidly for small diameter reaches a maximum and decreases for

large diameter (Figure 17 and Appendices H - L).

Other species have also a good representation in number of trees in Dorado1

and Dorado2 plots (Table 10). Also, these species, which had many trees per category,

showed that the diameter increment increases rapidly for small diameter reaches a

maximum and decreases for large diameter (Figure 13).

Table 10. Species with Better Representation in the Data Set

# Plot Name Species Well Represented in Each Plot

Scientific Name Species Well Represented in Each

Plot Common Name

1 Dorado 1 Mora excelsa, Aspidosperma marcgravianum

Mora and Canjilon Negro

2 Dorado 2 Mora excelsa Mora

3 Dorado 3 Eschweilera parviflora, Sterculia pruriens,

Licania densiflora and Cecropia sciadophylla

Cacao, Hierrito, Majagua and Yagrumo

4 Dorado 4 Aspidosperma marcgravianum Canjilon Negro

5 RioGrande5 Castostemma commune, Eschweilera parviflora, Inga sp., and Licania densiflora

Baraman, Cacao, Guamo and Hierrito

6 RioGrande6 Eschweilera parviflora, Carapa

Guianensis, Toulicia guianensis, Licania densiflora, Alexa imperatricis.

Cacao, Carapa, Carapo blanco, Hierrito, Leche de cochino

50

The maximum diameter increments observed in all plots are in Table 11.

Table 11. Maximum Diameter Increment for Species in Each Plot

# Plot Name Species Maximum Diameter Increment (cm)

1 Dorado 1 Mora excelsa (Mora ) 0.6 2 Dorado 2 Mora excelsa (Mora) 0.6 3 Dorado 3 Sterculia pruriens (Majagua ) 0.6 4 Dorado 4 Aspidosperma marcgravianum (Canjilon Negro) 0.8 5 RioGrande5 Licania densiflora (Hierrito) 1.5 Eschweilera grata (Cacao) 1.0 Guamo 1.0 Alexa imperatricis (Leche cochino) 1.0

6 RioGrande6 Alexa imperatricis (Leche cochino) 1.8 Licania densiflora (Hierrito) 1.2

Figure 13. Diameter Increment with Respect to Diameter for Each Species in Dorado 1 Plot

51

Tree Density Analysis

In the early years of the data set, there were few large diameter trees; most trees

are small. In later years there are less small diameter trees and larger diameter trees.

Density for small diameters is decreasing over time because small trees are changing to

the larger diameter categories. This is clear for the 20 cm category (Figure 14 and

Figure 15).

= 1971= 1981= 2000

= 1971= 1981= 2000

= 1971= 1981= 2000

Figure 14. Total Tree Density of All Species in Dorado 1 Plot

52

Figure 15. Total Tree Density for Each Species in Dorado 1 Plot

53

Relation between Diameter Increment and Rainfall and Temperature

Relation among Years, Diameter increment, and Annual Temperature and Precipitation

Possible relationships between diameter increment and climate (air temperature

and rainfall) were explored by means of charts. In these graphs (Figure 16 and Figure

17) some relations can be appreciated; for example for 1974 the rainfall decrease and

the diameter increment also decreases. In the same way, for 1984 the temperature

decreases and the diameter increment also decreases.

0.00

0.10

0.20

0.30

0.40

0.50

0.60

F-72 F-73 M-74 A-75 M-76 M-77 A-78 F-79 M-80 M-81 J-83 J-84 J-85 N-86 A-88 J-90 S-91 N-92 J-94 J-00

Years

Dia

met

er In

crem

ent (

cm)

0

500

1000

1500

2000

2500

Ann

ual R

ainf

all (

mm

Average of increment Annual Rainfall

Dorado 1 plot. Eschweilera grata (Cacao)

Figure 16. Diameter Increment and Annual Rainfall in Dorado 1 Plot

54

Dorado 1 plot. Eschweilera grata (Cacao)

0.00

0.20

0.40

0.60

F-72 F-73 M-74 A-75 M-76 M-77 A-78 F-79 M-80 M-81 J-83 J-84 J-85 N-86 A-88 J-90 S-91 N-92 J-94

Years

Dia

met

er In

crem

ent (

cm)

22.00

23.00

24.00

25.00

26.00

27.00

28.00

Ann

ual T

empe

ratu

re(C

)

Average of increment Annual Temperature

Figure 17. Diameter Increment and Annual Temperature in Dorado 1 Plot

Relations between temperature, rainfall and diameter increment were explored

by statistical analysis based on regression. Using Excel, regressions of annual rainfall

between diameter increments (Figure 18) and the annual mean temperature versus

diameter increments were generated (Figure 18).

55

Figure 18. Regression Analysis between Rainfall and Diameter Increment

56

Figure 19. Regression Analysis between Temperature and Diameter Increment

It was found that the p value for the rainfall vs diameter increment regression is

0.82 while the p value for the temperature vs diameter increment is 0.06. Therefore,

temperature seems to have more effect in the diameter increment than rainfall. This

also could be due to the fact that climatic series were not measured at the plots but

taken from a nearby weather station. Because temperature in this area is relatively

57

constant, the relationship emerges, but because there are more spatial variations in

rainfall, that relationship may not appear to exist. Therefore, the lack of meteorological

stations closer to or in the study area makes this part of the study inconclusive.

Calculation of G Values

In order to find the G values, a program called tasa.R was executed in R. It

generates a growth rate calibration graph for each plot (Figure 20 and Appendix C).

The values of G were changed manually in the tasa.R program until the regression line

went through the maximum value (Figure 20).

58

Figure 20. Growth Rate Calibration of Dorado 1 Plot (Tasa1.R) with Maximum Point (Line Goes

through Maximum Value)

Finally, the values of G used in the tasa.R for each plot using the maximum point

fit are shown in Table 12.

59

Table 12. Values of G Used for Each Species Present in Each Plot

Plot G value used in tasa.R

Dorado1 4900, 700, 4200, 400, 800, 950, 1600, 4000 Dorado2 210, 1150, 4250, 430 Dorado3 1700, 3700, 1700, 2000, 4300, 16000, 4000, 2800, 2400, 2100 Dorado4 1800, 4000, 1900, 1100, 1800, 1100, 1700, 18000, 500 RioGrande5 4500, 4600, 7500, 7500, 10200, 3000, 400, 4200, 3300, 1400, 2500 RioGrande6 1800, 2200, 5700, 1400, 4000, 1500, 2500, 450, 350, 1400, 1000

Table 13 shows the values of G values for each species in each Dorado plot,

whereas Table 14 shows the values of G values for each species in each plot.

60

Table 13. Values of G Used for Each Species Present in Each Dorado Plot

DORADO1

Scientific Name Common

Name G Protium neglectum Azucarito 4900 Eschweilera decolorans Cacao 700 Mora excelsa Mora 4200 Clathrotropis brachypetala Caicareño 400 Chimarrhis microcarpa Carutillo 800 Aspidosperma marcgravianum Canjilon Negro 950 Simarouba amara Cedro Blanco 1600 Protium heptaphyllum Tacamajaca 4000 DORADO2


Name G Protium neglectum Azucarito 210 Eschweilera decolorans Cacao 1150 Mora excelsa Mora 4250 Trichilia schomburgkii Suipo 430 DORADO3


Name G Eschweilera decolorans Cacao 1700 Chimarrhis microcarpa Carutillo 3700 Inga splendens Guamo 1700 Licania densiflora Hierrito 2000 Sterculia pruriens Majagua 4300 Cecropia sciadophylla Yagrumo 16000 Protium neglectum Azucarito 4000 Eschweilera grata Cacaito 2800 Trichilia schomburgkii Suipo 2400 Protium heptaphyllum Tacamajaca 2100 DORADO4


Name G Eschweilera decolorans Cacao 1800 Aspidosperma marcgravianum Canjilon Negro 4000 Licania densiflora Hierrito 1900 Licania alba Hierro 1100 Pouteria egregia Purgillo 1800 Trichilia schomburgkii Suipo 1100 Protium neglectum Azucarito 1700 Protium heptaphyllum Tacamajaca 18000 Sterculia pruriens Majagua 500

61

Table 14. Values of G Used for Each Species Present in Each Rio Grande Plot

RIOGRANDE5


Name G Castostemma commune Baraman 4500 Eschweilera decolorans Cacao 4600 Inga splendens Guamo 7500 Licania densiflora Hierrito 7500 Alexa imperatricis LecheCochino 10200 Sterculia pruriens Majagua 3000 Eschweilera subglandulosa Majaguillo 400 Protium neglectum Azucarito 4200 Eschweilera grata Cacaito 3300 Toulicia guianensis CarapoBlanco 1400 Pentaclethra macroloba Clavellino 2500 RIOGRANDE6


Name G Eschweilera grata Cacaito 1800 Eschweilera decolorans Cacao 2200 Carapa Guianensis Carapa 5700 Toulicia guianensis CarapoBlanco 1400 Licania densiflora Hierrito 4000 Licania alba Hierro 1500 Alexa imperatricis LecheCochino 2500 Sterculia pruriens Majagua 450 Trichilia schomburgkii Suipo 350 Protium heptaphyllum Tacamajaca 1400 Hirtella triandra CenizaNegra 1000

The program tasa.R reads the variable in the file called paramsp-combined.txt,

which contains Hmax (cm), Dmax (cm), b3, and b2. These values were taken from

Delgado et al. (2005) as shown on Table 6. However, as already mentioned above, that

table has the information for only 32 of the 39 species used in this study. Therefore, the

rest of the information was taken from Delgado (2000). Also, these values are shown in

Table 17. The values of G, and the values used to calculate G are summarized in Table

15. This table is the excel file saved as parameter.xls.

62

Table 15. Values Used to Generate G Values for the Plot’s Species

Dmax (cm)

Hmax (cm) b3 b2 Scientific Species Name

Common Species Name

Previous G

G Plot1

G Plot2

G Plot3

G Plot4

G Plot5

G Plot6 Final G

75 35 0.8 -0.016 Alexa imperatricis Leche de cochino 1500 3000 700 150095 40 0.6 -0.0132 Aspidosperma marcgravianum Canjilon Negro 600 1000 1700 150090 30 1.2 -0.0404 Carapa Guianensis Carapa 800 1200 120085 40 1.4 -0.0318 Castostemma commune Baraman 600 1700 160065 25 0.8 -0.0445 Chimarrihis microcarpa Carutillo 1500 700 2500 170080 30 1.2 -0.0392 Clathrotropis brachypetala Caicareño 250 350 30090 35 1 -0.0285 Couratari guianensis Capa de tabaco 500 500130 55 0.6 -0.0067 Erisma uncinatum Mureillo 700 70090 40 1 -0.021 Eschweilera decolorans Cacao 450 550 800 1300 1200 1700 1200 120075 35 0.8 -0.0219 Eschweilera parviflora Cacaito 700 1900 2000 400 180085 35 0.6 -0.0159 Licania alba Hierro 900 900 2500 140080 40 0.6 -0.0152 Licania densiflora Hierrito 900 1600 1000 3500 1100 1300120 40 1.2 -0.029 Manilkara bidentata Purguo 500 500120 40 0.8 -0.0173 Mora gonghripii Mora 900 1900 1900 1900100 40 0.8 -0.0207 Poteria sp. Purguillo 1200 1200 270085 40 1 -0.0217 Protium neglectum Azucarito 1000 2700 160 3000 1300 2700 2700100 35 1 -0.0256 Sterculia pruriens Majagua 1000 1900 350 2000 300 180070 35 1.2 -0.043 Toulicia guianensis Carapo blanco 600 1300 2900 150035 25 1.2 -0.0544 Anaxagorea dolichocarpa Yara yara negra 400 40045 30 1.4 -0.0391 Apeiba aspera Cabeza de negro 600 60025 20 1 -0.0498 Brownea coccinea Rosa de montaña 400 40050 20 0.8 -0.0419 Cecropia sciadophylla Yagrumo 1000 13000 1300040 25 1 -0.0353 Coccolaba sp. Arahueque 300 30035 25 1.4 -0.0815 Cordia sericicalix Alatrique negro 1200 120040 30 1.2 -0.0568 Himathantus articulata Mapolo 1500 150025 15 1 -0.0515 Hirtella sp. Ceniza negra 700 700 70065 25 1 -0.0361 Inga sp. Guamo 1200 800 3500 220060 25 1.6 -0.114 Protium guianensis Tacamajaca 1800 2500 2300 6000 900 230050 25 0.8 -0.02 Rollinia exsucca Anoncillo 400 40060 30 1.4 -0.0573 Scheflera morototoni Sun-Sun 1000 100065 35 1.4 -0.0362 Simarouba amara Cedro blanco 1000 1400 140035 25 1 -0.0919 Sloanea guianensis Suipo 1500 150035 25 1.6 -0.0808 Tailicia sp. Catoperi 1000 100025 35 1.6 -0.0693 Trichilia schomburgkii Suipo 1200 500 2500 1300 1700 300 180040 25 1 -0.0316 Eschweilera subglandulosa Majaguillo 700 600 60065 35 0.6 -0.01 Pentaclethra macroloba Clavellino 300 300110 35 0.6 -0.0158 Protium sp. Caraño 1000 1000110 35 0.6 -0.0084 Especie1 Jobo 800 80070 30 0.6 -0.0147 Especie2. Canelo 800 800

The final growth rate coefficient (G) for each species was the average of the G

values in all the plots. In the 5 plots there are a total of 23 species (Table 16). The

other G was taking from previos studies (Delgado, 2000). As mentioned before, G is a

very important parameter, which is required by the FACET model to simulate forest

dynamics.

63

Table 16. Final Values of G (Growth Rate Coefficient) for the Plot’s Species

# Scientific Name Common Name G

1 Alexa imperatricis Leche de cochino 6350 2 Aspidosperma marcgravianum Canjilon Negro 2475 3 Carapa Guianensis Carapa 5700 4 Castostemma commune Baraman 4500 5 Chimarrhis microcarpa Carutillo 2250 6 Clathrotropis brachypetala Caicareño 400 7 Eschweilera decolorans Cacao 2025 8 Eschweilera grata Cacaito 2633 9 Licania alba Hierro 1300 10 Licania densiflora Hierrito 3850 11 Mora excelsa Mora 4225 12 Pouteria egregia Purguillo 1800 13 Protium neglectum Azucarito 3002 14 Sterculia pruriens Majagua 2063 15 Toulicia guianensis Carapo blanco 1400 16 Cecropia sciadophylla Yagrumo 16000 17 Hirtella triandra Ceniza negra 1000 18 Inga splendens Guamo 4600 19 Protium heptaphyllum Tacamajaca 6375 20 Simarouba amara Cedro blanco 1600 21 Trichilia schomburgkii Suipo 1070 22 Eschweilera subglandulosa Majaguillo 400 23 Pentaclethra macroloba Clavellino 2500

Table 17. G values Used for the Species that Are Not Present in the Six Study Plots

# Scientific name Common Name G

1 Couratari pulchra Capa de tabaco 500 2 Erisma uncinatum Mureillo 700 3 Manilkara bidentata Purguo 500 4 Anaxagorea dolichocarpa Yara yara negra 400 5 Apeiba aspera Cabeza de negro 600 6 Brownea coccinea Rosa de Montaña 400 7 Coccoloba caurana Arahueque 300 8 Cordia fallax Alatrique negro 1200 9 Himatanthus articulata Mapolo 1500 10 Rollinia exsucca Anoncillo 400 11 Schefflera morototoni Sun-Sun 1000 12 Sloanea guianensis Aleton 1500 13 Talisia hexaphylla Cotoperi 1000 14 Protium decandrum Caraño 1000 15 Especie1. Jobo 800 16 Especie2. Canelo 800

64

Finally, all the G values used for the 39 studied species are in given in Table 18.

Table 18. Values Used for Generate G Values for the 39 Species

# Scientific Name Common Name G

1 Alexa imperatricis Leche de cochino 6350 2 Aspidosperma marcgravianum Canjilon Negro 2475 3 Carapa Guianensis Carapa 5700 4 Castostemma commune Baraman 4500 5 Chimarrhis microcarpa Carutillo 2250 6 Clathrotropis brachypetala Caicareño 400 7 Couratari pulchra Capa de tabaco 500 8 Erisma uncinatum Mureillo 700 9 Eschweilera decolorans Cacao 2025 10 Eschweilera grata Cacaito 2633 11 Licania alba Hierro 1300 12 Licania densiflora Hierrito 3850 13 Manilkara bidentata Purguo 500 14 Mora excelsa Mora 4225 15 Pouteria egregia Purguillo 1800 16 Protium neglectum Azucarito 3002 17 Sterculia pruriens Majagua 2063 18 Toulicia guianensis Carapo blanco 1400 19 Anaxagorea dolichocarpa Yara yara negra 400 20 Apeiba aspera Cabeza de negro 600 21 Brownea coccinea Rosa de Montaña 400 22 Cecropia sciadophylla Yagrumo 16000 23 Coccoloba caurana Arahueque 300 24 Cordia fallax Alatrique negro 1200 25 Himatanthus articulata Mapolo 1500 26 Hirtella triandra Ceniza negra 1000 27 Inga splendens Guamo 4600 28 Protium heptaphyllum Tacamajaca 6375 29 Rollinia exsucca Anoncillo 400 30 Schefflera morototoni Sun-Sun 1000 31 Simarouba amara Cedro blanco 1600 32 Sloanea guianensis Aleton 1500 33 Talisia hexaphylla Cotoperi 1000 34 Trichilia schomburgkii Suipo 1070 35 Eschweilera subglandulosa Majaguillo 400 36 Pentaclethra macroloba Clavellino 2500 37 Protium decandrum Caraño 1000 38 Especie1. Jobo 800 39 Especie2. Canelo 800

65

GIS Analysis

Generating Digital Map for the Study Area

Paper maps were scanned, rectified, and georeferenced, to generate the map of

the area with the rivers and roads layers (Figure 21). Using various points of the rivers

and roads the papers maps were referenced. This process makes the paper maps

match with the river, road, and contour lines layers.

66

Figure 21. Map of the Study Area Using Scanned Paper Maps

The following map was created using a satellite image. The roads and rives

layers were overlaid (Figure 22). The above map and the following map are a good

67

reference of the study area. Also, looking at these two maps it is possible to have a

better idea of the terrain.

Figure 22. Map of the Study Area Using Satellite Image

68

Generating the DEM for the Study Area

A contour line map at 40m intervals was provided by the UNEG’s GEOECOLAB.

Using interpolation in ArcToolbox with the function Raster to Topo was possible to

create a DEM of the study area from a contour lines layer (Figure 23). Cell size of the

generated DEM is 30m meters by 30m meters (Figure 24). This DEM was used for the

hydrology study and the generation of the CTI.

69

Figure 23. Contour Lines Layer

70

Figure 24. 30m by 30m Meter DEM Generated from the Contour Lines Layer

71

Hydrologic Study

The hydrology study was performed to generate important layers, such as flow

direction (Figure 25), flow accumulation (Figure 26), Slope (Figure 28), and CTI (Figure

29). The information from some of these layers will be used to run FACET.

Generating the Flow Accumulation and Flow Direction

In order to generate the flow accumulations it is necessary to generate flow

direction (Figure 26). Using the DEM (Figure 24) the flow direction layer was performed

using Hydrology functions in ArcToolbox.

72

Figure 25. Flow Direction Layer Generated with ArcToolbox

73

The above flow direction layer (Figure 25) was used to calculate the flow accumulation

layer (Figure 26).

Figure 26. Flow Accumulation Layer Generated Using Hydrology Function in ArcToolbox

The highest flow accumulation value in the study area is 392172 cells.

74

Calculation of the CTI

The CTI is calculated using the catchments area and the slope. Therefore, the

catchments area (Figure 27) was generated raster calculator in ArcView.

Figure 27. Catchments Area

75

Then, the slope (Figure 28) was generated from the DEM using surface function in

ArcToolBox

Figure 28. Slope Layer

Finally, using the Catchments Area (Figure 27) and the Slope (Figure 28) the CTI was

calculated following equation (5) and is shown in (Figure 29)

76

Figure 29. Compound Topographic Index (CTI)

77

Areas with low CTI and high CTI were taken from the above generated CTI to

map to define the two terrain types; valley and hill. Values of flow accumulation, slope,

and elevation were taken from one area with low CTI (in the hill) and from one area with

high CTI (in the valley). The values used to run FACET in the hill and in the valley are

shown in Table 19. The values for the hill are changed and saved in a file called ent-

loma.txt, and the values of valley in a file called entvalle (Appendix D).

Table 19. Definition of Terrain Types

Valley (entvalle)

Hill (entloma)

Elevation(m) 120 340 Soil Type 9 9 Slope 0.01 3 Flow Accumulation 900 1 Specific Area 27000 30 Average of run off coefficient 0.02 0.01 CTI (cell size 30m) 19.4 3.5

As mentioned before, in the valley terrain type’s soil water is more abundant

whereas water is less abundant in the hill.

Basal Area Analysis from FACET runs

The previous analyses help to calculate some of the needed parameters and

input values to run FACET such as the growth G values (Table 18), CTI (Figure 29 and

Table 19), Flow accumulation (Figure 26, Table 19), and Slope (Figure 28, Table 19).

This information helped to update the data in some of the FACET files. For

example, the information of Table 6, Table 14 and Table 17 was used to update G,

hmax, dmax, b2 and b3 in the FACET files called sppima-cti.txt and grpima.cti.txt

(Appendix D). Sppima-cti.txt contains information for the 39 tree species studied, and

78

grpima.cti.txt contains information for the 16 hypothetical species groups. The layer

generated by the hydrologic analysis in GIS, such as CTI, was used to generate Table

19 and update the FACET entvalle and entloma file (Appendix D). These files contain

information for the two types of terrains used to run FACET.

Basal area and density analyses were perfomed for each FACET scenario

defined in Figure 9. The results will be presented in two sections, one for the basal area

and another for the density analysis. Each section will have results from the scenarios

and will contain results from FACET using 39 species (sppima-cti.txt) in the valley and

16 hypothetical groups (grpima.cti.txt) in the valley and the hill.

Basal Area Analysis Using Sppima-cti.txt in the Valley with 180 cm Precipitation

The basal area in the year 500 of the simulation using sppima-cti.txt with 180 cm

precipitation is saved in the z.tracer file. This file is exported into Excel to graph the

basal area in the year 500 per species (Figure 30).

79

Basal Area in the Valley for the 39 Studied Species at Year 500 with 180 cm Precipitation

0

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1 2 3 4 5 6 7 8 9 101112131415161718192021222324252627282930313233343536373839

Species

Bas

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(m2/

ha)

Figure 30. Basal Area (m2/ha) with Respect to 39 Studied Species Using 180 cm Precipitation

The species with the highest and the lowest basal area at this last year of

simulation are shown in Table 20 and Table 21.

Table 20. Species with Highest Basal Area by Year 500 Using 180 cm Precipitation


Name

Basal Area (m2/ha) at Year 500

21 L4 Carapa guianensis Carapa 12.929 L4 Mora excelsa Mora 11.422 L4 Catostemma commune Baraman 420 L4 Alexa imperatricis Leche de cochino 327 L4 Licania densiflora Hierrito 1.3

80

Table 21. Species with Lowest Basal Area by Year 500 using 180 cm Precipitation


NameBasal Area (m2/ha)

at Year 500

1 L1 Apeiba aspera Cabeza de negro 02 L1 Cecropia sciadophylla Yagrumo 03 L1 Cordia fallax Alatrique negro 04 L1 Himathantus articulata Mapolo 05 L1 Inga splendens Guamo 0

Table 22 shows basal area for all the species in the year 500 using 180 cm

precipitation.

81

Table 22. Basal Area in the Year 500 Using 180 cm Precipitation

# Functional groups Scientific species name Common species

name

Basal area (m2/ha) at year 500

1 L1 Apeiba aspera Cabeza de negro 02 L1 Cecropia sciadophylla Yagrumo 03 L1 Cordia fallax Alatrique negro 04 L1 Himathantus articulata Mapolo 05 L1 Inga splendens Guamo 06 L1 Scheflera morototoni Sun-Sun 07 L1 Simarouba amara Cedro blanco 08 L2 Chimarrihis microcarpa Carutillo 09 L2 Clathrotropis brachypetala Caicareno 010 L2 Sterculia pruriens Majagua 011 L2 Toulicia guianensis Carapo blanco 012 L2 Protium heptaphyllum Tacamajaca 0.113 L2 Sloanea guianensis Aleton 014 L2 Talicia hexaphylla Cotoperi 015 L2 Trichilia schomburgkii Suipo 016 L4 Aspidosperma marcgravia Canjilon amarillo 0.317 L4 Eschweilera grata Cacaito 0.318 L4 Pouteria egregia Purguillo 0.119 L4 Protium decandrum Caraño 020 L4 Alexa imperatricis Leche de cochino 321 L4 Carapa guianensis Carapa 12.922 L4 Catostemma commune Baraman 423 L4 Couratari pulchra Capa de tabaco 024 L4 Erisma uncinatum Mureillo 025 L4 Eschweilera decolorans Cacao 0.126 L4 Licania alba Hierro 0.127 L4 Licania densiflora Hierrito 1.328 L4 Manilkara bidentata Purguo 029 L4 Mora excelsa Mora 11.430 L4 Protium neglectum Azucarito blanco 0.631 L4 Eschweilera subglandulos Majaguillo 032 L4 Pentaclethra macroloba Clavellino 0.133 L4 Especie1. Jobo 0.134 L4 Especie2. Canelo 035 L5 Anaxagorea dolichocarpa Yara yara negra 0.136 L5 Brownea coccinea Rosa de montaña 0.237 L5 Coccoloba caurana Arahueque 0.138 L5 Hirtella triandra Ceniza negra 0.339 L5 Rollinia exsucca Anoncillo 0.1

Using 180 cm precipitation, the majority of species with high basal areas belong

to group four (L4). These species grow 30-meters tall and are shade tolerant. The

82

species with the highest basal area is Carapa guianesis (Carapa) and Mora excelsa

(Mora). Almost all the species from functional groups L1 and L2 disappear or have very

low basal areas.

The previous graphs show results at year 500 in the simulation. Therefore, in

order to see what happens during these 500 years the basal area of each species was

plotted during the 500-year simulation (Figure 31). The basal area during the entire

500-year simulation was generated. However, it is hard to clearly distinguish all the

species in the graph because of so many traces. Therefore, only the species with

largest basal area during the simulation were graphed (Figure 31).

83

Basal Area during 500 Years using 180cm Precipitation

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2 Cecropia sciadophylla 20 Alexa imperatricis 21 Carapa guianensis22 Catostemma commune 27 Licania densiflora 29 Mora excelsa30 Protium neglectum

Figure 31. Basal Area (m2/ha) during 500 Years of Simulation with 180 cm Precipitation

Cecropia sciadophylla (Yagrumo) has the most dominant basal area at the

beginning of the simulation and when a canopy gap is generated during the run.

Carapa guianensis (Carapa), a shade tolerant species, is prominent during the 500

years of simulation. Shade tolerant species achieve larger values of basal area only

after 100 years. The following table (Table 23) shows the highest and lowest basal area

at the beginning and the end of the simulation.

84

Table 23. Basal Area at the Beginning and at the End of the Simulation Using 180 cm Precipitation


Name

Basal Area (m2/ha) at the

Beginning of the Simulation


End of the Simulation

1 L1 Apeiba aspera Cabeza de negro 0.1 02 L1 Cecropia sciadophylla Yagrumo 0.8 03 L1 Cordia fallax Alatrique negro 0.1 04 L1 Himathantus articulata Mapolo 0.1 05 L1 Inga splendens Guamo 0.2 06 L1 Scheflera morototoni Sun-Sun 0.1 07 L1 Simarouba amara Cedro blanco 0.1 08 L2 Chimarrihis microcarpa Carutillo 0.1 09 L2 Clathrotropis brachypetala Caicareno 0 010 L2 Sterculia pruriens Majagua 0 011 L2 Toulicia guianensis Carapo blanco 0 012 L2 Protium heptaphyllum Tacamajaca 0.1 0.113 L2 Sloanea guianensis Aleton 0 014 L2 Talicia hexaphylla Cotoperi 0 015 L2 Trichilia schomburgkii Suipo 0 016 L4 Aspidosperma marcgravia Canjilon amarillo 0 0.317 L4 Eschweilera grata Cacaito 0 0.318 L4 Pouteria egregia Purguillo 0 0.119 L4 Protium decandrum Caraño 0 020 L4 Alexa imperatricis Leche de cochino 0 321 L4 Carapa guianensis Carapa 0 12.922 L4 Catostemma commune Baraman 0 423 L4 Couratari pulchra Capa de tabaco 0 024 L4 Erisma uncinatum Mureillo 0 025 L4 Eschweilera decolorans Cacao 0 0.126 L4 Licania alba Hierro 0 0.127 L4 Licania densiflora Hierrito 0 1.328 L4 Manilkara bidentata Purguo 0 029 L4 Mora excelsa Mora 0 11.430 L4 Protium neglectum Azucarito blanco 0 0.631 L4 Eschweilera subglandulos Majaguillo 0 032 L4 Pentaclethra macroloba Clavellino 0 0.133 L4 Especie1. Jobo 0 0.134 L4 Especie2. Canelo 0 035 L5 Anaxagorea dolichocarpa Yara yara negra 0 0.136 L5 Brownea coccinea Rosa de montaña 0 0.237 L5 Coccoloba caurana Arahueque 0 0.138 L5 Hirtella triandra Ceniza negra 0 0.339 L5 Rollinia exsucca Anoncillo 0 0.1

At the beginning of the simulation, some species such as Cecropia sciadophylla

(Yagrumo) have a large basal area (0.8 m2/ha). These species belong to functional

group one (L1), which are shade intolerant species with a low to medium stature

85

(between 10 and 30 meters). At the beginning of the simulation, there is less vegetation

land cover, and consequently more sun and less shade. Species from group L1 and L2

grow well in these conditions and have the highest basal area at this time. Around year

33, these species decrease in basal area and other species, such as Carapa guianensis

(Carapa) from functional group four (L4), dominate the area.

This is corroborative of the idea that shade intolerant species grow faster and

dominate the area when there is less vegetation land cover. With time and greater

vegetation land cover, shade tolerant species from group L4 and L5 become more

predominant.

Percentage of relative basal area was calculated per functional group to see the

behavior of the groups rather than individual species (Figure 32). Again, significant

basal area is only achieved after 100 years.

86

Percent of Relative Basal Area in the Valley per Funcional Grup during 500 years with 180 cm Precipitation

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Total Basal Area per Functional Group (FG) L1 Total Basal Area per Functional Group (FG) L2

Total Basal Area per Functional Group (FG) L4 Total Basal Area per Functional Group (FG) L5

Figure 32. Percent of Relative Basal Area with Respect to Years with 180 cm Precipitation.

In both graphs, at the beginning of the simulation, functional group one (L1) has

the highest percent of relative basal area. During the rest of the simulation functional

group four (L4) is dominant.

Basal Area Analysis Using Grpima.cti.txt in the Valley with 120 cm and 180 cm

Precipitation

The previous analyses use real data for 39 species located in six different plots in

the study area. However, it was not possible to find data on soil water response for

each species. In order to study the soil water response of the model in different

locations, instead of using species the model was simulated for 16 hypothetical species

groups. These groups are based on soil-moisture response (hill or drought-

87

tolerant/saturation-intolerant, and valley or drought-intolerant/saturation-tolerant), tree

size (small, medium, large), and shade tolerance (pioneer, intermediate, and tolerant)

(Table 8). The 16 hypothetical species group and their characteristics are saved in the

file called grpima.cti.txt.

FACET was used to generate the basal area using grpima.cti.txt at 120 cm

precipitation over 500 years. The basal area during those years is saved in the z-tracer

file which was imported into Excel. These data were used to graph the basal area in the

year 500 per hypothetical functional group (Figure 33).

88

Hypothetical Species Group Basal Area in the Valley at Year 500 with 120 cm Precipitation

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1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16Hypothetical Species Groups

Bas

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Figure 33. Basal Area (m2/ha) with Respect to Hypothetical Species Groups with 120 cm

Precipitation

All the species groups with highest basal area values are sorted in ascending

order by basal area in Table 24.

Table 24. Hypothetical Species Groups in the Valley with Highest Basal Area at Year 500 with 120

cm Precipitation

# Hypothetical species groups Nomenclature Basal area (m2/ha) in the year 500

8 Tolerant small valley TOPeva 10.46 Tolerant large valley TOgrva 7.37 Tolerant medium valley TOmeva 5.71 Intermediate large valley INgrva 2.32 Intermediate medium valley INmeva 1.23 Intermediate small valley INpeva 0.5

89

All the species groups with lowest basal area or zero values are sorted in

ascending order by basal area in Table 25.

Table 25. Lowest Basal Area or Basal Area Zero at Year 500 using 120 cm Precipitation

# Hypothetical species groups Nomenclature Basal area (m2/ha) in

the year 5009 Intermediate large hill INgrlo 010 Intermediate medium hill INmelo 011 Intermediate small hill INpelo 012 Pioneer medium hill PImelo 013 Pioneer small hill PIpelo 014 Tolerant large hill TOgrlo 015 Tolerant medium hill TOmelo 016 Tolerant small hill TOPelo 05 Pioneer small valley PIpeva 0.2

It can be seen that the hill species groups (those that are drought-

tolerant/saturation-intolerant) have a basal area of zero. All the valley species groups

(those that are drought-intolerant/saturation-tolerant) are present in the year 500. The

species group with highest basal area is the tolerant large valley group. Results using

FACET with grpima.cti.txt at 180 cm precipitation are shown in Figure 34.

90

Hypothetical Species Groups Basal Area in the Valley at Year 500 with 180 cm Precipitation

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1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16

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Figure 34. Basal Area (m2/ha) with respect to Hypothetical Species Groups with 180 cm

Precipitation

The species groups with the highest and the lowest basal area at year 500 of the

simulation are shown in Table 26. The values are sorted in ascending order by basal

area.

91

Table 26. Basal Area at Year 500 with 180 cm Precipitation

# Hypothetical species groups Nomenclature Basal area (m2/ha) in the year 500

6 Tolerant large valley TOgrva 15.18 Tolerant small valley TOPeva 12.47 Tolerant medium valley TOmeva 7.81 Intermediate large valley INgrva 2.42 Intermediate medium valley INmeva 1.14 Pioneer medium valley PImeva 0.13 Intermediate small valley INpeva 05 Pioneer small valley PIpeva 09 Intermediate large hill INgrlo 010 Intermediate medium hill INmelo 011 Intermediate small hill INpelo 012 Pioneer medium hill PImelo 013 Pioneer small hill PIpelo 014 Tolerant large hill TOgrlo 015 Tolerant medium hill TOmelo 016 Tolerant small hill TOPelo 0

Again, the hill groups have a basal area of zero. The group with the highest

basal area is the large tolerant valley group. Table 27 compares basal area at year 500

with 120 cm and 180 cm precipitation. It can be seen that pioneer and intermediate

species have zero value at the end of the simulation run because they are intolerant to

shade.

92

Table 27. Hypothetical Species Groups Basal Area in the Valley at Year 500 with 120 cm and 180

cm Precipitation

# Hypothetical Species Groups Nomenclature Basal Area (m2/ha) with

120 cm Precipitation

Basal Area (m2/ha) with 180 cm Precipitation

1 Intermediate large valley INgrva 2.3 2.42 Intermediate medium valley INmeva 1.2 1.13 Intermediate small valley INpeva 0.5 04 Pioneer medium valley PImeva 0.5 0.15 Pioneer small valley PIpeva 0.2 06 Tolerant large valley TOgrva 7.3 15.17 Tolerant medium valley TOmeva 5.7 7.88 Tolerant small valley TOPeva 10.4 12.49 Intermediate large hill INgrlo 0 010 Intermediate medium hill INmelo 0 011 Intermediate small hill INpelo 0 012 Pioneer medium hill PImelo 0 013 Pioneer small hill PIpelo 0 014 Tolerant large hill TOgrlo 0 015 Tolerant medium hill TOmelo 0 016 Tolerant small hill TOPelo 0 0

Basal area results are similar for 120 cm and 180 cm of precipitation. In some

cases basal area is higher at 180 cm precipitation. It can be seen that pioneer and

intermediate have zero value at the end of the simulation run because they are

intolerant to shade. However, it is important to study the behavior of basal area during

the entire simulation run and not only the last year.

In order to see the basal area per hypothetical group during the entire 500 years

simulation with 120 cm precipitation the following graphs were generated (Figure 35).

93

Hypothetical Species Groups Basal Area in the Valley during 500 Years with 120 cm Precipitation

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20 40 60 80 100 120 140 160 180 200 220 240 260 280 300 320 340 360 380 400 420 440 460 480Years

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1 Intermediate large valley 2 Intermediate medium valley 3 Intermediate small valley 4 Pioneer medium valley

5 Pioneer small valley 6 Tolerant large valley 7 Tolerant medium valley 8 Tolerant small valley

9 Intermediate large hill 10 Intermediate medium hill 11 Intermediate small hill 12 Pioneer medium hill

13 Pioneer small hill 14 Tolerant large hill 15 Tolerant medium hill 16 Tolerant small hill

Figure 35. Basal Area of Hypothetical Species Group during a 500-Year Simulation with 120 cm

Precipitation

Basal area at the beginning (year 3) and the end (year 500) of the simulation is

shown in the following table.

94


# Hypothetical Species Groups Nomenclature

Basal Area (m2/ha) at the Beginning of the

Simulation

Basal Area (m2/ha) at the End of the

Simulation1 Intermediate large valley INgrva 0 2.32 Intermediate medium valley INmeva 0 1.23 Intermediate small valley INpeva 0.1 0.54 Pioneer medium valley PImeva 0.1 0.55 Pioneers small valley PIpeva 0.1 0.26 Tolerant large valley TOgrva 0 7.37 Tolerant medium valley TOmeva 0 5.78 Tolerant small valley TOPeva 0 10.49 Intermediate large hill INgrlo 0 010 Intermediate medium hill INmelo 0 011 Intermediate small hill INpelo 0 012 Pioneer medium hill PImelo 0 013 Pioneer small hill PIpelo 0 014 Tolerant large hill TOgrlo 0 015 Tolerant medium hill TOmelo 0 016 Tolerant small hill TOPelo 0 0

At the beginning of the simulation the first species groups that grow are the valley

pioneer groups. At the end of the simulation, the species group with highest basal area

is the tolerant small valley.

In order to see the basal area per hypothetical group during the 500 years of

simulation with 180 cm precipitation the following graphs were generated (Figure 36).

95

Hypothetical Species Groups Basal Area in the Valley during 500 Years with 180 cm Precipitation

0

5

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15

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25

30

20 40 60 80 100 120 140 160 180 200 220 240 260 280 300 320 340 360 380 400 420 440 460 480

Years

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1 Intermediate large valley 2 Intermediate medium valley 3 Intermediate small valley

4 Pioneer medium valley 5 Pioneer small valley 6 Tolerant large valley7 Tolerant medium valley 8 Tolerant small valley 9 Intermediate large hill

10 Intermediate medium hill 11 Intermediate small hill 12 Pioneer medium hill13 Pioneer small hill 14 Tolerant large hill 15 Tolerant medium hill

16 Tolerant small hill

Figure 36. Basal Area (m2/ha) of Hypothetical Species Group during 500 Years of Simulation with


The following Table 29 shows the basal area at the beginning (year 4) and the

end (year 500) of the simulation.

96



Basal Area (m2/ha) at the Beginning of

the Simulation



1 Intermediate large valley INgrva 0 2.42 Intermediate medium valley INmeva 0 1.13 Intermediate small valley INpeva 0.1 04 Pioneer medium valley PImeva 0.1 0.15 Pioneer small valley PIpeva 0.2 06 Tolerant large valley TOgrva 0 15.17 Tolerant medium valley TOmeva 0 7.88 Tolerant small valley TOPeva 0 12.49 Intermediate large hill INgrlo 0 010 Intermediate medium hill INmelo 0 011 Intermediate small hill INpelo 0 012 Pioneer medium hill PImelo 0 013 Pioneer small hill PIpelo 0 014 Tolerant large hill TOgrlo 0 015 Tolerant medium hill TOmelo 0 016 Tolerant small hill TOPelo 0 0

At the beginning of the simulation the first species groups that grow are the valley


is the tolerant medium valley.

For both simulations, 120 cm and 180 cm precipitation, the group pioneer small

valley grows first. Around year 70, the intermediate large valley group dominates and

then decreases around year 100. The group tolerant small valley dominates the area

after the intermediate large valley group begins to decrease. The group tolerant large

valley grows constantly until it dominates at the end of the simulation. All the hill groups

have basal area of zero.

97

Basal Area Analysis using grpima.cti.txt on the Hill with 120 cm and 180 cm

precipitation

The following analysis is similar to the one presented previously but referred to

the hill terrain. The basal area on the hill in the year 500 with 120 cm precipitation is

shown in Figure 37.

Hypothetical Species Groups Basal Area on the Hill at Year 500 using 120 cm Precipitation

0

1

2

3

4

5

6

7

8

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rmed

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ley

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rant

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rmed

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rmed

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ll

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med

ium

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Tole

rant

smal

l hill

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16


Bas

al A

rea

(m2/

ha)

Figure 37. Basal Area (m2/ha) of Hypothetical Species Groups on the Hill at Year 500 with 120 cm

Precipitation

Now, the valley hypothetical groups have a basal area of zero whereas almost all

the hill groups are present in the simulation. The groups with basal area not equal to

zero are listed in the following table (Table 30). They are sorted by descending basal

area.

Table 30. Hypothetical Species Groups in the Valley with Highest Basal Area at Year 500 with 120

cm precipitation.

98

# Hypothetical Species Groups Nomenclature Basal Area (m2/ha) at

Year 50014 Tolerant large hill TOgrlo 7.610 Intermediate medium hill INmelo 7.49 Intermediate large hill INgrlo 7.115 Tolerant medium hill TOmelo 6.816 Tolerant small hill TOPelo 6.812 Pioneer medium hill PImelo 1.3

The group with the highest basal area is the tolerant large hill followed by medium and

large intermediate hill.

Using 180 cm precipitation the basal area at year 500 on the hill is shown in the

following graph (Figure 38).

Hypothetical Species Groups Basal Area on the Hill at Year 500 with 180cm Precipitation

0

0.5

1

1.5

2

2.5

3

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rmed

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hill

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Tole

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Bas

al A

rea

(m2/

ha)

Figure 38. Basal Area (m2/ha) of Hypothetical Species Groups in the Year 500 with 180 cm

Precipitation

99

The groups with the highest basal area are sorted in the following table (Table

31.).

Table 31. Hypothetical Species Groups with Highest Basal Area at Year 500 with 180 cm

Precipitation

# Hypothetical Species Groups Nomenclature Basal Area (m2/ha) at

the Year 50016 Tolerant small hill TOPelo 2.811 Intermediate small hill INpelo 2.714 Tolerant large hill TOgrlo 2.615 Tolerant medium hill TOmelo 2.69 Intermediate large hill INgrlo 2.210 Intermediate medium hill INmelo 1.8

In this simulation the group with the highest basal area is tolerant small hill

followed by intermediate small hill and large tolerant hill.

The following table (Table 32) compares FACET results with 120 cm and 180 cm

precipitation at year 500.

100

Table 32. Hypothetical Species Groups Basal Area at Year 500 with 120 cm and 180 cm

Precipitation


Basal area (m2/ha) Using 120 cm precipitation

Basal Area (m2/ha) Using 180 cm Precipitation

1 Intermediate large valley INgrva 0 02 Intermediate medium valley INmeva 0 03 Intermediate small valley INpeva 0 04 Pioneer medium valley PImeva 0 05 Pioneer small valley PIpeva 0 06 Tolerant large valley TOgrva 0 07 Tolerant medium valley TOmeva 0 08 Tolerant small valley TOPeva 0 09 Intermediate large hill INgrlo 7.1 2.210 Intermediate medium hill INmelo 7.4 1.811 Intermediate small hill INpelo 0.2 2.712 Pioneer medium hill PImelo 1.3 1.713 Pioneer small hill PIpelo 0 1.214 Tolerant large hill TOgrlo 7.6 2.615 Tolerant medium hill TOmelo 6.8 2.616 Tolerant small hill TOPelo 6.8 2.8

More hypothetical groups are present with 180 cm precipitation than 120 cm.

However, the basal area values are higher with 120 cm precipitation. But this could be

due to an increase at the end of the run and may not reflect the entire simulation. In

order to verify this assumption, it is necessary to graph the entire simulation. The basal

area present during 500 years of simulation with 120 cm and 180 cm are shown in the

following graphs (Figure 39 and Figure 40 Table 33.). Indeed, it can be noticed that

basal area was lower at many times during the run.

101

Hypothetical Species Groups Basal Area on the Hill during 500 Years with 120 cm precipitation

0

2

4

6

8

10

12

14

20 40 60 80 100 120 140 160 180 200 220 240 260 280 300 320 340 360 380 400 420 440 460 480

Years

Bas

al A

rea

(m2/

ha)




13 Pioneer small hill 14 Tolerant large hill 15 Tolerant medium hill 16 Tolerant small hill

Figure 39. Basal Area of Hypothetical Species Groups during 500 Years with 120 cm Precipitation

The following Table 33. shows the basal area near the beginning (year 30) and

the end (year 500) of the simulation with 120 cm precipitation.

102

Table 33. Basal Area at the Beginning and at the End of the Simulation using 120 cm Precipitation



the Simulation



1 Intermediate large valley INgrva 0 02 Intermediate medium valley INmeva 0 03 Intermediate small valley INpeva 0 04 Pioneer medium valley PImeva 0 05 Pioneer small valley PIpeva 0 06 Tolerant large valley TOgrva 0 07 Tolerant medium valley TOmeva 0 08 Tolerant small valley TOPeva 0 09 Intermediate large hill INgrlo 0.1 7.110 Intermediate medium hill INmelo 0 7.411 Intermediate small hill INpelo 0.1 0.212 Pioneer medium hill PImelo 0.1 1.313 Pioneer small hill PIpelo 0.1 014 Tolerant large hill TOgrlo 0 7.615 Tolerant medium hill TOmelo 0 6.816 Tolerant small hill TOPelo 0 6.8

At the beginning of the simulation the first species groups that grow are the hill


is the tolerant large hill.

Figure 36 shows the basal area per hypothetical species group during the 500

years of simulation with 180 cm precipitation.

103

Hypothetical Species Groups Basal Area on the Hill during 500 Years with 180 cm Precipitation

0

2

4

6

8

10

12

20 40 60 80 100 120 140 160 180 200 220 240 260 280 300 320 340 360 380 400 420 440 460 480

Years

Basa

l Are

a (m

2/ha

)

1 Intermediate large valley 2 Intermediate medium valley 3 Intermediate small valley4 Pioneer medium valley 5 Pioneer small valley 6 Tolerant large valley7 Tolerant medium valley 8 Tolerant small valley 9 Intermediate large hill10 Intermediate medium hill 11 Intermediate small hill 12 Pioneer medium hill14 Pioneer small hill 15 Tolerant large hill 16 Tolerant medium hill17 Tolerant small hill

Figure 40. Basal Area of Hypothetical Species Groups during 500 Years with 180 cm Precipitation

The following Table 34 shows the basal area at the beginning (year 4) and the

end (year 500) of the simulation.

104




the Simulation

Basal Area (m2/ha) at the End of the

Simulation1 Intermediate large valley INgrva 0 02 Intermediate medium valley INmeva 0 03 Intermediate small valley INpeva 0 04 Pioneer medium valley PImeva 0 05 Pioneer small valley PIpeva 0 06 Tolerant large valley TOgrva 0 07 Tolerant medium valley TOmeva 0 08 Tolerant small valley TOPeva 0 09 Intermediate large hill INgrlo 0 2.210 Intermediate medium hill INmelo 0 1.811 Intermediate small hill INpelo 0.1 2.712 Pioneer medium hill PImelo 0.1 1.713 Pioneer small hill PIpelo 0.1 1.214 Tolerant large hill TOgrlo 0 2.615 Tolerant medium hill TOmelo 0 2.616 Tolerant small hill TOPelo 0 2.8

At the beginning of the simulation the first species groups that grow are the hill


is the tolerant small hill.

For both, 120 cm and 180 cm precipitation, the group that grows first is pioneer

small or pioneer medium hill. For 120 cm precipitation the predominant group at year

500 is the tolerant large hill, whereas for 180 cm precipitation the predominant group at

year 500 is the tolerant small hill. With 180 cm precipitation the tolerant large and small

hill group dominates during almost all the 500 years of simulation while keeping a fairly

stable behavior. However, such behavior becomes less stable with drastic falls for

precipitation of 120 cm. In both cases the basal area valley groups is zero.

As expected, the species groups grow differentially according to location (valley, hill)

due to soil water response (drought and saturation tolerances). In the valley soil water is

more abundant whereas soil water is less abundant in the hill. The drought-

105

tolerant/saturation-intolerant species have typical largest basal area in the hills while

drought-intolerant/saturation-tolerant have largest basal area in the valleys.

Tree Density Analysis Results using FACET

Tree Density Analysis using Sppima-cti.txt in the Valley 180 cm Precipitation

FACET was run using 180 cm precipitation. It generated a file called z-density

which contains the tree density during the 500 simulation years. The density analysis

was performed in Excel using z-density with 120 cm and 180 cm precipitation. The total

basal area per species at year 500 with 180 cm is in Figure 41.

Density in the Valley for the 39 Studied Species at Year 500 with 180 cm Precipitation

0

20

40

60

80

100

120

140

Apei

baC

ecro

pia

Cor

dia

falla

x H

imat

hant

usIn

gaSc

hefle

raSi

mar

ouba

Chi

mar

rihis

Cla

thro

tropi

sSt

ercu

liaTo

ulic

iaPr

otiu

mSl

oane

aTa

licia

Tric

hilia

Aspi

dosp

erm

aEs

chw

eile

raPo

uter

iaPr

otiu

mAl

exa

Car

apa

Cat

oste

mm

aC

oura

tari

Eris

ma

Esch

wei

lera

Lica

nia

alba

Lica

nia

Man

ilkar

aM

ora

exce

lsa

Prot

ium

Esch

wei

lera

Pent

acle

thra

Espe

cie1

.Es

peci

e2.

Anax

agor

eaBr

owne

aC

occo

loba

Hirt

ella

Rol

linia

1 2 3 4 5 6 7 8 9 101112131415161718192021222324252627282930313233343536373839

Species

Den

sity

(ind

iv/h

a)

Figure 41. Tree Density per Species at Year 500 with 180 cm Precipitation

Table 35 shows the highest density at year 500 with 180 cm precipitation in the

valley.

106

Table 35. Species with Highest Tree Density at Year 500 with 180cm Precipitation

# Functional Group Scientific Species Name Common Species Name

Density (m2/ha) at Year 500

31 L4 Eschweilera subglandulos Majaguillo 115.838 L5 Hirtella triandra Ceniza negra 11536 L5 Brownea coccinea Rosa de montaña 11516 L4 Aspidosperma marcgravia Canjilon amarillo 106.537 L5 Coccoloba caurana Arahueque 104.519 L4 Protium decandrum Caraño 74.5

Species with the highest density belongs to group L5 and L4. As mentioned

before, these groups are shade tolerant. They are usually present where there are not

gaps in the canopy. The following Table 36 shows the species with the lowest tree

density.

Table 36. Species with Lowest Tree Density at Year 500 with 180 cm Precipitation

# Functional Group Scientific Species Name Common Species Name

Density (m2/ha) at Year 500

4 L1 Himathantus articulata Mapolo 1.815 L2 Trichilia schomburgkii Suipo 3.811 L2 Toulicia guianensis Carapo blanco 45 L1 Inga splendens Guamo 43 L1 Cordia fallax Alatrique negro 4.28 L2 Chimarrihis microcarpa Carutillo 4.2

Species with the lowest density belongs to group L1 and L2. As mentioned

before, these groups are shade intolerant. They are usually present where there are

gaps in the canopy.

In addition, using z-density the tree density for some species during 500 years

was generated (Figure 42).

107

Density During 500 Years using 180 cm Precipitation

0

50

100

150

200

250

20 40 60 80 100

120

140

160

180

200

220

240

260

280

300

320

340

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380

400

420

440

460

480

500

Years

Tree

Den

sity

(ind

/ha)

21 Carapa guianensis 29 Mora excelsa 35 Anaxagorea dolichocarpa36 Brownea coccinea 37 Coccoloba caurana 38 Hirtella triandra39 Rollinia exsucca

Figure 42. Tree Density with Respect to Years during 500 Years of Simulation Using 180 cm

Precipitation

The species with largest density during the 500 years of simulation are Hirtella

triandra and Rollinia coccinea. Tree densities at the beginning and at the end of the

simulation using 180 cm precipitation are shown in Table 37.

108

Table 37. Tree Density at the Beginning and the End of the Simulation with 180 cm Precipitation


Name

Density (m2/ha) at the Beginning of the Simulation

Density (m2/ha) at the End of the

Simulation1 L1 Apeiba aspera Cabeza de negro 123.8 52 L1 Cecropia sciadophylla Yagrumo 109 1.83 L1 Cordia fallax Alatrique negro 121.8 44 L1 Himathantus articulata Mapolo 130.5 4.85 L1 Inga splendens Guamo 123.8 6.26 L1 Scheflera morototoni Sun-Sun 130 47 L1 Simarouba amara Cedro blanco 114.8 3.88 L2 Chimarrihis microcarpa Carutillo 138.5 4.29 L2 Clathrotropis brachypetala Caicareno 125.5 7

10 L2 Sterculia pruriens Majagua 126.5 6.211 L2 Toulicia guianensis Carapo blanco 126.5 4.212 L2 Protium heptaphyllum Tacamajaca 140 8.813 L2 Sloanea guianensis Aleton 131 5.814 L2 Talicia hexaphylla Cotoperi 125 5.815 L2 Trichilia schomburgkii Suipo 124.2 8.816 L4 Aspidosperma marcgravia Canjilon amarillo 108.5 39.817 L4 Eschweilera grata Cacaito 113.5 34.818 L4 Pouteria egregia Purguillo 112.5 4419 L4 Protium decandrum Caraño 109.8 42.220 L4 Alexa imperatricis Leche de cochino 110.2 47.521 L4 Carapa guianensis Carapa 110 74.522 L4 Catostemma commune Baraman 117.2 5623 L4 Couratari pulchra Capa de tabaco 111.8 34.824 L4 Erisma uncinatum Mureillo 112.8 37.825 L4 Eschweilera decolorans Cacao 102 36.226 L4 Licania alba Hierro 119 3427 L4 Licania densiflora Hierrito 101.8 39.828 L4 Manilkara bidentata Purguo 114.2 33.529 L4 Mora excelsa Mora 112.2 59.230 L4 Protium neglectum Azucarito blanco 116 4031 L4 Eschweilera subglandulos Majaguillo 103.2 3432 L4 Pentaclethra macroloba Clavellino 112.5 34.833 L4 Especie1. Jobo 106 38.534 L4 Especie2. Canelo 123.2 4135 L5 Anaxagorea dolichocarpa Yara yara negra 96 104.536 L5 Brownea coccinea Rosa de montaña 98 115.837 L5 Coccoloba caurana Arahueque 93 106.538 L5 Hirtella triandra Ceniza negra 83.5 11539 L5 Rollinia exsucca Anoncillo 88 115

In order to understand the simulation during the 500 years, the species were

grouped by functional groups. After converting tree density to relative tree density and

109

grouping species by functional group, a graph showing tree density by functional group

was generated Figure 36).

Percent of Relative Tree Density per Funcional Grup during 500 Years with 180 cm Precipitation

0

10

20

30

40

50

60

70

80

90

20 40 60 80 100 120 140 160 180 200 220 240 260 280 300 320 340 360 380 400 420 440 460 480

Years

% R

elat

ive

Den

sity

Total Basal Area per Functional Group (FG) L1 Total Basal Area per Functional Group (FG) L2Total Basal Area per Functional Group (FG) L4 Total Basal Area per Functional Group (FG) L5

Figure 43. Functional Group during 500 Years Using 180 cm

Again, the functional group with the highest percent of relative tree density during

500 years of simulation is the functional group four (L4). The functional group with

lowest density was L1.

Tree Density Analysis using Grpima.cti.txt in the Valley with 120 cm and 180 cm

Precipitation

FACET was run twice using 120 cm and 180 cm precipitation for each location,

valley and hill. It generated a file called z-density, which contains the tree density

during the 500 simulation years. For precipitation 120 cm the tree density per

hypothetical species groups at year 500 is shown in Figure 44.

110

Hypothetical Species Tree Density in the Valley in the Year 500 with 120 cm Precipitation

050

100150200250300350400

Inte

rmed

iate

larg

e va

lley

Inte

rmed

iate

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ium

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ley

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rmed

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sm

all v

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y

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eer m

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eer s

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rmed

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Inte

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eer m

ediu

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Tole

rant

sm

all h

ill

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16


Den

sity

(ind

iv/h

a)

Figure 44. Tree Density at Year 500 with 120 cm Precipitation

All the tree densities at year 500 with 120 cm precipitation are shown in Table 38.

111

Table 38. Species with Highest and Lowest Tree Density at Year 500 with 120 cm Precipitation

# Hypothetical Species Groups Nomenclature Density (indiv/ha) in the Year 500

1 Intermediate large valley INgrva 136.82 Intermediate medium valley INmeva 131.23 Intermediate small valley INpeva 664 Pioneer medium valley PImeva 64.25 Pioneer small valley PIpeva 39.26 Tolerant large valley TOgrva 235.87 Tolerant medium valley TOmeva 232.58 Tolerant small valley TOPeva 351.89 Intermediate large hill INgrlo 010 Intermediate medium hill INmelo 011 Intermediate small hill INpelo 012 Pioneer medium hill PImelo 013 Pioneer small hill PIpelo 014 Tolerant large hill TOgrlo 015 Tolerant medium hill TOmelo 016 Tolerant small hill TOPelo 0

As the above graph (Figure 44) and table (Table 38) show the valley small

tolerant group hase the highest tree density. All the tolerant groups in the valley have

the highest tree density followed by the valley intermediate groups. All the hill groups

have a basal area of zero.

Using 180 cm precipitation the tree density per species at year 500 was

generated (Figure 45).

112

Hypothetical Species Group Density in the Valley at Year 500 with 180 cm Precipitation

020406080

100120140160180200

Inte

rmed

iate

larg

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lley

Inte

rmed

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ium

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rmed

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Pio

neer

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neer

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rant

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e va

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rant

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illPi

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Tole

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ium

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Tole

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Den

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(ind

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Figure 45. Tree Density per Species in Year 500 with 180 cm Precipitation

Table 39. Tree Densities at Year 500 with 180 cm Precipitation

# Hypothetical Species Group Nomenclature Density (indiv/ha) in the

Year 5001 Intermediate large valley INgrva 162 Intermediate medium valley INmeva 18.83 Intermediate small valley INpeva 8.54 Pioneer medium valley PImeva 75 Pioneer small valley PIpeva 76 Tolerant large valley TOgrva 64.27 Tolerant medium valley TOmeva 58.58 Tolerant small valley TOPeva 1839 Intermediate large hill INgrlo 0

10 Intermediate medium hill INmelo 011 Intermediate small hill INpelo 012 Pioneer medium hill PImelo 013 Pioneer small hill PIpelo 014 Tolerant large hill TOgrlo 015 Tolerant medium hill TOmelo 016 Tolerant small hill TOPelo 0

113

Table 40. Tree Densities at Year 500 with 120 cm and 180 cm Precipitation

# Hypothetical Species Groups Nomenclature Density (indic/ha) Using


Density (indiv/ha) Using 180 cm Precipitation

1 Intermediate large valley INgrva 136.8 162 Intermediate medium valley INmeva 131.2 18.83 Intermediate small valley INpeva 66 8.54 Pioneer medium valley PImeva 64.2 75 Pioneer small valley PIpeva 39.2 76 Tolerant large valley TOgrva 235.8 64.27 Tolerant medium valley TOmeva 232.5 58.58 Tolerant small valley TOPeva 351.8 1839 Intermediate large hill INgrlo 0 010 Intermediate medium hill INmelo 0 011 Intermediate small hill INpelo 0 012 Pioneer medium hill PImelo 0 013 Pioneer small hill PIpelo 0 014 Tolerant large hill TOgrlo 0 015 Tolerant medium hill TOmelo 0 016 Tolerant small hill TOPelo 0 0

Using the z-density.txt file the tree density for all groups during 500 years with 120 cm

and 180 cm precipitation was generated (Figure 46 and Figure 47).

114

Hypothetical Species Group Density in the Valley during 500 Years with 120 cm Precipitation

0

100

200

300

400

500

600

20 40 60 80 100

120

140

160

180

200

220

240

260

280

300

320

340

360

380

400

420

440

460

480

Years

Den

sity

(ind

iv/h

a)

1 Intermediate large valley INgrva 2 Intermediate medium valley INmeva 3 Intermediate small valley INpeva4 Pioneer medium valley PImeva 5 Pioneer small valley PIpeva 6 Tolerant large valley TOgrva7 Tolerant medium valley TOmeva 8 Tolerant small valley TOPeva 9 Intermediate large hill INgrlo10 Intermediate medium hill INmelo 11 Intermediate small hill INpelo 12 Pioneer medium hill PImelo13 Pioneer small hill PIpelo 14 Tolerant large hill TOgrlo 15 Tolerant medium hill TOmelo16 Tolerant small hill TOPelo

Figure 46. Tree Density per Species during 500 Years with 120 cm Precipitation

115

Hypothetical Species Group Density in the Valley during 500 years with 180 cm Precipitation

0

100

200

300

400

500

600

20 40 60 80 100

120

140

160

180

200

220

240

260

280

300

320

340

360

380

400

420

440

460

480

Years

Den

sity

(ind

iv/h

a)


4 Pioneer medium valley 5 Pioneer small valley 6 Tolerant large valley

7 Tolerant medium valley 8 Tolerant small valley 9 Intermediate large hill

10 Intermediate medium hill 11 Intermediate small hill 12 Pioneer medium hill

13 Pioneer small hill 14 Tolerant large hill 15 Tolerant medium hill


Figure 47. Tree Density with Respect to Years during 500 Years with 180 cm Precipitation

Again the hypothetical group with the highest tree density is the tolerant small

valley group followed by the large and the medium tolerant valley groups. All hill groups

have very low or no tree density.

Tree Density Analysis usin Grpima.cti.txt on the Hill with 120 cm and 180 cm

Precipitation

Tree density on the hill with 120 cm precipitation is shown in Figure 48.

116

Hypothetical Species Group Density on the Hill at Year 500 with 120 cm Precipitation

0

20

40

60

80

100

120In

term

edia

tela

rge

valle

yIn

term

edia

tem

ediu

mva

lley

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rmed

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yP

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yTo

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ntsm

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rmed

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Inte

rmed

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smal

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Pio

neer

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Pio

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Tole

rant

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Tole

rant

med

ium

hill

Tole

rant

smal

l hill

1 2 3 4 5 6 7 8 9 10 11 12 14 15 16 17


Den

sity

(ind

iv/h

a)

Figure 48. Hypothetical Species Group Density at Year 500 with 120 cm Precipitation

The species group with the highest density in the year 500 with 120 cm

precipitation is sorted by density in Table 41.

117

Table 41. Hypothetical Species Group Density at Year 500 with 120 cm Precipitation

# Hypothetical Species Groups Nomenclature Density (indiv/ha) at the Year 500

14 Tolerant large hill TOgrlo 114.216 Tolerant small hill TOPelo 100.815 Tolerant medium hill TOmelo 8910 Intermediate medium hill INmelo 79.59 Intermediate large hill INgrlo 76.812 Pioneer medium hill PImelo 13.511 Intermediate small hill INpelo 4.513 Pioneer small hill PIpelo 2.21 Intermediate large valley INgrva 02 Intermediate medium valley INmeva 03 Intermediate small valley INpeva 04 Pioneer medium valley PImeva 05 Pioneer small valley PIpeva 06 Tolerant large valley TOgrva 07 Tolerant medium valley TOmeva 08 Tolerant small valley TOPeva 0

Using 180 cm precipitation the density per hypothetical species group on the hill

is shown in Figure 49.

118

Hypothetical Species Group Density in the Valley at Year 500 with 180 cm Precipitation

020406080

100120140160180200

Inte

rmed

iate

larg

e va

lley

Inte

rmed

iate

med

ium

Inte

rmed

iate

smal

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ley

Pio

neer

med

ium

Pio

neer

smal

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ley

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rant

larg

e va

lley

Tole

rant

med

ium

Tole

rant

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ley

Inte

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iate

larg

e hi

llIn

term

edia

tem

ediu

m h

illIn

term

edia

tesm

all h

illP

ione

erm

ediu

m h

illPi

onee

rsm

all h

illTo

lera

ntla

rge

hill

Tole

rant

med

ium

hill

Tole

rant

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Den

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a)

Figure 49. Hypothetical Species Group Density at Year 500 with 180 cm Precipitation

Table 42 shows the tree density per hypothetical species group density with 180 cm

precipitation. They are sorted by tree density.

119

Table 42. Species Group Tree Density at Year 500 with 180 cm Precipitation

# Hypothetical Species Groups Nomenclature Density (indiv/ha) at the Year 500

11 Intermediate small hill INpelo 138.89 Intermediate large hill INgrlo 123.810 Intermediate medium hill INmelo 115.814 Tolerant large hill TOgrlo 11312 Pioneer medium hill PImelo 109.815 Tolerant medium hill TOmelo 109.216 Tolerant small hill TOPelo 107.513 Pioneer small hill PIpelo 87.21 Intermediate large valley INgrva 02 Intermediate medium valley INmeva 03 Intermediate small valley INpeva 04 Pioneer medium valley PImeva 05 Pioneer small valley PIpeva 06 Tolerant large valley TOgrva 07 Tolerant medium valley TOmeva 08 Tolerant small valley TOPeva 0

Table 43. compares the density between 120 cm and 180 cm precipitation

120

Table 43. Species Group Tree Density at Year 500 with 120 cm and 180 cm Precipitation




1 Intermediate large valley INgrva 0 02 valley INmeva 0 03 Intermediate small valley INpeva 0 04 Pioneer medium valley PImeva 0 05 Pioneer small valley PIpeva 0 06 Tolerant large valley TOgrva 0 07 Tolerant medium valley TOmeva 0 08 Tolerant small valley TOPeva 0 09 Intermediate large hill INgrlo 76.8 123.810 Intermediate medium hill INmelo 79.5 115.811 Intermediate small hill INpelo 4.5 138.812 Pioneer medium hill PImelo 13.5 109.813 Pioneer small hill PIpelo 2.2 87.214 Tolerant large hill TOgrlo 114.2 11315 Tolerant medium hill TOmelo 89 109.216 Tolerant small hill TOPelo 100.8 107.5

Using 120 cm precipitation the group with highest tree density at year 500 is the

tolerant medium hill group. Using 180 cm precipitation the group with highest tree

density at year 500 is the intermediate small hill group. In both simulations, the tree

density for the valley species group is zero.

In order to see the density for the hypothetical species groups during 500 years

of simulation with 120 cm and 180 cm precipitation the following graphs were generated

(Figure 50 and Figure 51).

121

Hypothetical Species Group Density on the Hill during 500 Years with 120 cm Precipitation

0

50

100

150

200

250

300

350

400

450

20 40 60 80 100

120

140

160

180

200

220

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440

460

480

Years

Den

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(ind

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14 Pioneer small hill 15 Tolerant large hill TOgrlo 16 Tolerant medium hill 17 Tolerant small hill

Figure 50. Tree Density with Respect to Years during 500 Years of Simulation with 120 cm

Precipitation

Figure 50 shows the densities for all species groups at the beginning and the end

of the simulation with 120 cm precipitation.

122

Table 44. Hypothetical Species Group Density at the Beginning and End of the Simulation with


# Hypothetical Species Group Nomenclature

Density (indiv/ha) at the beginning of the

simulation

Density (indiv/ha) at the end of the

simulation1 Intermediate large valley INgrva 0 02 valley INmeva 0 03 Intermediate small valley INpeva 0 04 Pioneer medium valley PImeva 0 05 Pioneer small valley PIpeva 0 06 Tolerant large valley TOgrva 0 07 Tolerant medium valley TOmeva 0 08 Tolerant small valley TOPeva 0 09 Intermediate large hill INgrlo 22.8 76.810 Intermediate medium hill INmelo 28 79.511 Intermediate small hill INpelo 20 4.512 Pioneer medium hill PImelo 16 13.513 Pioneer small hill PIpelo 15.5 2.214 Tolerant large hill TOgrlo 37.2 114.215 Tolerant medium hill TOmelo 36 8916 Tolerant small hill TOPelo 31.5 100.8

Tolerant small hill and Tolerant medium hill groups dominate during almost all the

simulation. At the beginning and at the end of the simulation tolerant large hill and

tolerant small hill have the highest density.

123

Hypothetical Species Group Density on the Hill during 500 Years with 180 cm Precipitation

0

50

100

150

200

250

300

20 40 60 80 100

120

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180

200

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480

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Den

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(ind

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4 Pioneer medium valley 5 Pioneer small valley 6 Tolerant large valley

7 Tolerant medium valley 8 Tolerant small valley 9 Intermediate large hill

10 Intermediate medium hill 11 Intermediate small hill 12 Pioneer medium hill

13 Pioneer small hill 14 Tolerantes large hill 15 Tolerant medium hill


Figure 51. Tree Density with Respect to Years During 500 Years of Simulation with 180 cm

Precipitation

Table 45 shows tree density for all species groups at the beginning and the end

of the simulation using 180 cm precipitation.

124

Table 45. Hypothetical Species Group Density at the Beginning and the End of the Simulation

Using 180 cm Precipitation

# Hypothetical species groups NomenclatureDensity (indiv/ha) at the beginning of the

simulation

Density (indiv/ha) at the end of the

simulation

1 Intermediate large valley Ingrva 0 02 Intermediate medium valley INmeva 0 03 Intermediate small valley INpeva 0 04 Pioneer medium valley PImeva 0 05 Pioneer small valley PIpeva 0 06 Tolerant large valley TOgrva 0 07 Tolerant medium valley TOmeva 0 08 Tolerant small valley TOPeva 0 09 Intermediate large hill INgrlo 181.2 123.810 Intermediate medium hill INmelo 199.2 115.811 Intermediate small hill INpelo 203 138.812 Pioneer medium hill PImelo 184.8 109.813 Pioneer small hill PIpelo 154.8 87.214 Tolerant large hill TOgrlo 207.8 11315 Tolerant medium hill TOmelo 218.2 109.216 Tolerant small hill TOPelo 191.2 107.5

Different groups, such as tolerant small medium and large hill groups dominate in

different periods in the simulation. At the beginning of the simulation, tolerant large hill

and tolerant small hill have the highest tree density. At the end of the simulation,

Intermediate small hill and intermediate small hill have the highest tree density. All the

hill groups have tree density of zero.

The species groups grow differentially according to location (valley, hill). In the

valley soil water is more abundant whereas water is less abundant in the hill. The

drought-tolerant/saturation-intolerant species are typical present in the hills while

drought-intolerant/saturation-tolerant are typical present in the valleys.

125

CONCLUSION

The dynamics of diameter increment and the dynamics of diameter during the 29

years of observation for each species were determined. These results were used to

determine and analyze the relationship between diameter increment and diameter. It

was found that for most species growth rate is dependent on tree diameter. Diameter

increment of a tree increases rapidly when the diameter is small, reaches its maximum,

and decreases when the diameter is large. The tree density study, which describes the

forest structure over time, also supports this idea. In the early years of the study, trees

with small diameter were abundant, but at the end of the study trees with large diameter

were more abundant. Small trees change over time to large diameter categories.

These patterns are evident for those species that are represented by a

substantial number of trees. However, a limitation to establish these patterns more for

all species is the low number of trees representing most species. This limitation is

typical of mixed diverse tropical forests where there are many species represented by a

few individuals.

Potential relationships between diameter increment and temperature and

diameter increment and rainfall were explored by regression analysis. Diameter

increment was related to temperature but not to rainfall. This may be due to the fact

that weather data was obtained from a weather station that was not located at the same

site as the study plots. Whereas temperature is more constant over large areas, rainfall

varies greatly, thus the values of rainfall employed in the regression may not correspond

to the precipitation at the plots. Further work on this issue is needed and installing

weather stations at the plots is recommended.

126

Growth rate coefficients (G) for each species were found taking the maximum

diameter increment values. This important variable was used to run the FACET model

for a period of 500 years. Some of the FACET results were tree density and basal area

during these years of simulation. The results were used to study the forest dynamic of

representative locations of the Imataca forest reserve. These locations were

characterized by annual rainfall and topographic position (indicated by CTI). For

example, using precipitation 180 cm, it was noted that at the beginning of the simulation

the functional group one (L1) has larger percent of relative basal area and tree density,

but during the rest of the simulation the functional group four (L4) is more dominant.

Also, the functional group with larger percent of relative tree density and relative basal

area during the 500 years is the functional group number four (L4) followed by

functional group number five (L5). This corroborates the idea that shade intolerant

species (L1, L2) grow faster and dominate the area when there are canopy gaps in the

forest. However, with time and greater vegetation land cover, shade tolerant species

(L4 and L5) become more dominant.

GIS hydrologic studies can be combined with dynamic models of forest to

simulate complex landscape environments. Together, these tools helped improve forest

behavior predictions, and will contribute to better models of forest dynamics. This study

generated digital maps, including a digital elevation model (DEM), layers for a

hydrologic GIS study to calculate the compound topographic index (CTI) for the

Botanamo watershed in the Imataca forest reserve. Digital maps helped to create new

maps, overlay different layers, and have a better understanding of the terrain in the

study area. The DEM helped to perform the hydrologic study, which generated some

127

important layers, such as flow accumulation, flow direction, slope, catchments area, and

CTI. The slope and catchments area allowed calculation of the CTI values for the

Botanamo watershed. The DEM generated for Botanamo area will be very useful for

other several studies and modeling in this area.

The results from the hydrologic study and the CTI allowed simulations of the soil

water response of species and groups in different locations, such as valley and hill. The

growth rate parameter G, flow accumulation and slope were used in FACET to simulate

the forest dynamics in Imataca. The FACET results were used to analyze basal area

and density dynamics. It was confirmed that FACET adequately simulates response to

soil moisture. Drought-tolerant/saturation-intolerant species group are typical of

simulations for sites with low CTI such as hills whereas drought-intolerant/saturation-

tolerant groups occurred in simulations of sites with high CTI such as valleys. In other

words, the species groups grow differentially according to location. The groups of

“drought-tolerant/saturation-intolerant”, which require less water, grow in the hill, where

soil water is less abundant. Whereas, the groups of “drought-intolerant/saturation-

tolerant”, which require more water, grow in the valley, where the soil water is more

abundant.

An important lesson learned from this study is the importance of empirical long-

term data for model calibration and evaluation. Great strides can be made in the quality

of the model predictions by using data from permanent plots. Continuation of the efforts

to collect data is strongly recommended. Another important lesson learned is that the

time required to achieve substantial amount of basal area is in most cases greater than

100 years. This period of time is longer than the rotation cycle allowed for logging in the

128

area. Increasing the duration of logging cycle is recommended from the point of view of

sustainability of forest.

Overall, the results of this thesis provide valuable information about the dynamics

of Imataca forest, and establish basis for future studies in this area.

129

APPENDIX A

TREE SPECIES DATA

130

Table 46. Example of the Original Data File for Dorado 1 Plot

Note: The file shown above illustrates the information only for one plot in 29 years of

data. The file was cut in year 1976 to fit it in this appendix. Also it was cut in row 56 in

excel, but the original file have 256 rows.

131

APPENDIX B

DIAMETER INCREMENT PER SPECIES

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133


134


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Figure 55. Rio Grande 5 Plot Diameter Increment with Respect to Diameter for Each Species

136

Figure 56. Diameter Increment with Respect to Diameter in Rio Grande 6 Plot

137

APPENDIX C

CALCULATION OF G USING R PROGRAM

138

Figure 57. Growth Rate Calibration of Dorado 2 Plot (tasa.R)

139


140


141

Figure 60. Growth Rate Calibration of Rio Grande 5 Plot (tasa.R)

142

Figure 61. Growth RFate Calibration of Rio Grande 6 Plot (tasa.R)

143

APPENDIX D

FACET INPUT FILES

144

entloma120 file:

Control control_filename siteima.cti site_filename grpima.cti spp_filename mosima.cti mosaic_filename loma terrain_name 340 elevation(m) 3 slope(%) 90 aspect(deg) 9 soil_type 30 specific_area 0.01 avg_runoff_coeff(%) 120 annual_prec(cm) 1 hurricane_damage 0 ranf_weather_seed 0 switch_calib

entloma180 file:

Control control_filename siteima.cti site_filename grpima.cti spp_filename mosima.cti mosaic_filename loma terrain_name 340 elevation(m) 3 slope(%) 90 aspect(deg) 9 soil_type 30 specific_area 0.01 avg_runoff_coeff(%) 180 annual_prec(cm) 1 hurricane_damage 0 ranf_weather_seed 0 switch_calib

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entvalle120 file:

Control control_filename siteima.cti site_filename grpima.cti spp_filename mosima.cti mosaic_filename valle terrain_name 120 elevation(m) 0.01 slope(%) 90 aspect(deg) 9 soil_type 27000 specific_area 0.01 avg_runoff_coeff(%) 120 annual_prec(cm) 1 hurricane_damage 0 ranf_weather_seed 0 switch_calib

entvalle180

Control control_filename siteima.cti site_filename grpima.cti spp_filename mosima.cti mosaic_filename valle terrain_name 120 elevation(m) 0.01 slope(%) 90 aspect(deg) 9 soil_type 27000 specific_area 0.01 avg_runoff_coeff(%) 180 annual_prec(cm) 1 hurricane_damage 0 ranf_weather_seed 0 switch_calib

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entsppvalle180 Control control_filename siteima.cti site_filename sppima-cti.txt spp_filename mosima.cti mosaic_filename valle terrain_name 120 elevation(m) 0.01 slope(%) 90 aspect(deg) 9 soil_type 27000 specific_area 0.01 avg_runoff_coeff(%) 180 annual_prec(cm) 1 hurricane_damage 0 ranf_weather_seed 0 switch_calib

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sppima-cti.txt

39 species in imataca 400 55 Apei Apeiba aspera Cabeza de negro 100 45 30 -0.0391 1.400 600 9 5000 10000 1 0.40 0.10 1.10 3-3 0 0 5 1 Cecr Cecropia sciadophylla Yagrumo 50 50 20 -0.0419 0.80016000 9 5000 10000 1 0.40 0.10 1.10 3 3 0 0 5 1 Cord Cordia fallax Alatrique negro 100 35 25 -0.0638 1.400 1200 9 5000 10000 1 0.40 0.10 1.10 3 3 0 0 5 1 Hima Himathantus articulata Mapolo 100 55 30 -0.0610 1.200 1500 9 5000 10000 1 0.40 0.10 1.10 3 3 0 0 5 1 Inga Inga splendens Guamo 100 65 35 -0.0617 1.600 4600 9 5000 10000 1 0.40 0.10 1.10 3 3 0 0 5 1 Sche Scheflera morototoni Sun-Sun 100 85 30 -0.0387 0.800 1000 9 5000 10000 1 0.40 0.10 1.10 3 3 0 0 5 1 Sima Simarouba amara Cedro blanco 100 65 35 -0.0362 1.400 1600 9 5000 10000 1 0.40 0.10 1.10 3 3 0 0 5 1 Chim Chimarrihis microcarpa Carutillo 150 65 25 -0.0504 1.200 2250 9 5000 10000 2 0.40 0.10 1.10 3 3 0 0 5 1 Clat Clathrotropis brachypetala Caicareno 150 80 35 -0.0202 0.800 400 9 5000 10000 2 0.40 0.10 1.10 3 3 0 0 5 1 Ster Sterculia pruriens Majagua 250 100 40 -0.0295 1.000 2060 9 5000 10000 2 0.40 0.10 1.10 3 3 0 0 5 1 Toul Toulicia guianensis Carapo blanco 100 70 35 -0.0477 1.400 1400 9 5000 10000 2 0.40 0.10 1.10 3 3 0 0 5 1 ProG Protium heptaphyllum Tacamajaca 150 60 40 -0.0343 1.200 6370 9 5000 10000 2 0.40 0.10 1.10 3 3 0 0 5 1 Sloa Sloanea guianensis Aleton 150 50 35 -0.0288 0.800 1500 9 5000 10000 2 0.40 0.10 1.10 3 3 0 0 5 1 Tali Talicia hexaphylla Cotoperi 150 35 25 -0.0766 1.600 1000 9 5000 10000 2 0.40 0.10 1.10 3 3 0 0 5 1 Tric Trichilia schomburgkii Suipo 150 45 35 -0.0606 1.600 1070 9 5000 10000 2 0.40 0.10 1.10 3 3 0 0 5 1 Aspi Aspidosperma marcgravia Canjilon amarillo 500 95 40 -0.0073 0.600 2475 9 5000 10000 4 0.40 0.10 1.10 3 3 0 0 5 1 ESpa Eschweilera grata Cacaito 500 75 35 -0.0274 1.000 2630 9 5000 10000 4 0.40 0.10 1.10 3 3 0 0 5 1 Pout Pouteria egregia Purguillo 500 100 40 -0.0160 0.600 1800 9 5000 10000 4 0.40 0.10 1.10 3 3 0 0 5 1 ProS Protium decandrum Caraño 350 65 40 -0.0175 0.600 1000 9 5000 10000 4 0.40 0.10 1.10 3 3 0 0 5 1 Alex Alexa imperatricis Leche de cochino 250 80 35 -0.0352 1.000 6350 9 5000 10000 4 0.40 0.10 1.10 3 3 0 0 5 1 Cara Carapa guianensis Carapa 500 105 40 -0.0129 0.600 5700 9 5000 10000 4 0.40 0.10 1.10 3 3 0 0 5 1 Cato Catostemma commune Baraman 500 85 40 -0.0238 1.000 4500 9 5000 10000 4 0.40 0.10 1.10 3 3 0 0 5 1 Cour Couratari pulchra Capa de tabaco 500 90 40 -0.0188 0.800 500 9 5000 10000 4 0.40 0.10 1.10 3 3 0 0 5 1 Eris Erisma uncinatum Mureillo 400 180 55 -0.0090 0.800 700 9 5000 10000 4 0.40 0.10 1.10 3 3 0 0 5 1 ESde Eschweilera decolorans Cacao 250 90 40 -0.0167 0.800 2025 9 5000 10000 4 0.40 0.10 1.10 3 3 0 0 5 1 Lial Licania alba Hierro 300 85 35 -0.0178 0.600 1300 9 5000 10000 4 0.40 0.10 1.10 3 3 0 0 5 1

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Lide Licania densiflora Hierrito 300 95 40 -0.0202 0.800 3850 9 5000 10000 4 0.40 0.10 1.10 3 3 0 0 5 1 Mani Manilkara bidentata Purguo 350 120 45 -0.0160 0.800 500 9 5000 10000 4 0.40 0.10 1.10 3 3 0 0 5 1 Mora Mora excelsa Mora 500 120 45 -0.0250 1.000 4225 9 5000 10000 4 0.40 0.10 1.10 3 3 0 0 5 1 ProN Protium neglectum Azucarito blanco 400 85 40 -0.0217 1.000 3002 9 5000 10000 4 0.40 0.10 1.10 3 3 0 0 5 1 Esch Eschweilera subglandulosa Majaguillo 350 95 35 -0.0194 0.600 400 9 5000 10000 4 0.40 0.10 1.10 3 3 0 0 5 1 Pent Pentaclethra macroloba Clavellino 350 65 30 -0.0278 0.800 2500 9 5000 10000 4 0.40 0.10 1.10 3 3 0 0 5 1 Spp1 Especie1. Jobo 400 110 35 -0.0084 0.600 800 9 5000 10000 4 0.40 0.10 1.10 3 3 0 0 5 1 Spp2 Especie2. Canelo 350 70 30 -0.0147 0.600 800 9 5000 10000 4 0.40 0.10 1.10 3 3 0 0 5 1 Anax Anaxagorea dolichocarpa Yara yara negra 250 35 25 -0.0544 1.200 400 9 5000 10000 5 0.40 0.10 1.10 3 3 0 0 5 1 Brow Brownea coccinea Rosa de montaña 250 25 20 -0.0366 0.800 400 9 5000 10000 5 0.40 0.10 1.10 3 3 0 0 5 1 Cocc Coccoloba caurana Arahueque 250 45 25 -0.0242 0.800 300 9 5000 10000 5 0.40 0.10 1.10 3 3 0 0 5 1 Hirt Hirtella triandra Ceniza negra 250 40 25 -0.0135 0.400 1000 9 5000 10000 5 0.40 0.10 1.10 3 3 0 0 5 1 Roll Rollinia exsucca Anoncillo 250 50 25 -0.0302 0.600 400 9 5000 10000 5 0.40 0.10 1.10 3 3 0 0 5 1

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grpima.cti

16 parameters from the groups. Only for loma (hill) and valley 400 55 INgrva Intermedias grandes valle 300 150 45 -0.0300 1.000 5000 9 5000 10000 3 0.10 0.60 1.10 3 2 0 0 5 2 INmeva Intermedias medianas valle 300 100 30 -0.0500 1.000 5000 9 5000 10000 3 0.10 0.60 1.10 3 2 0 0 5 2 INpeva Intermedias pequenas valle 300 50 15 -0.0700 1.000 5000 9 5000 10000 2 0.10 0.60 1.10 3 2 0 0 5 1 PImeva Pioneras medianas valle 80 80 30 -0.0700 1.200 8000 9 5000 10000 2 0.10 0.60 1.10 3 2 0 0 5 1 PIpeva Pioneras pequenas valle 50 40 15 -0.1000 1.200 8000 9 5000 10000 2 0.10 0.60 1.10 3 2 0 0 5 1 TOgrva Tolerantes grandes valle 400 150 45 -0.0200 0.800 4000 9 5000 10000 4 0.10 0.60 1.10 3 2 0 0 5 3 TOmeva Tolerantes medianas valle 400 100 30 -0.0300 0.800 4000 9 5000 10000 4 0.10 0.60 1.10 3 2 0 0 5 3 TOPeva Tolerantes pequenas valle 400 50 15 -0.0500 0.800 4000 9 5000 10000 5 0.10 0.60 1.10 3 2 0 0 5 4 INgrlo Intermedias grandes loma 300 150 45 -0.0300 1.000 5000 9 5000 10000 3 0.60 0.12 0.70 3 2 0 0 5 2 INmelo Intermedias medianas loma 300 100 30 -0.0500 1.000 5000 9 5000 10000 3 0.60 0.12 0.70 3 2 0 0 5 2 INpelo Intermedias pequenas loma 300 50 15 -0.0700 1.000 5000 9 5000 10000 2 0.60 0.12 0.70 3 2 0 0 5 1 PImelo Pioneras medianas loma 80 80 30 -0.0700 1.200 8000 9 5000 10000 2 0.60 0.12 0.70 3 2 0 0 5 1 PIpelo Pioneras pequenas loma 50 40 15 -0.1000 1.200 8000 9 5000 10000 2 0.60 0.12 0.70 3 2 0 0 5 1 TOgrlo Tolerantes grandes loma 400 150 45 -0.0200 0.800 4000 9 5000 10000 4 0.60 0.12 0.70 3 2 0 0 5 3 TOmelo Tolerantes medianas loma 400 100 30 -0.0300 0.800 4000 9 5000 10000 4 0.60 0.12 0.70 3 2 0 0 5 3 TOPelo Tolerantes pequenas loma 400 50 15 -0.0500 0.800 4000 9 5000 10000 5 0.60 0.12 0.70 3 2 0 0 5 4

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