TAXONOMIC IDENTIFICATION OF AMAZONIAN TREE
CROWNS FROM AERIAL PHOTOGRAPHY AND
IMPLICATIONS FOR UNDERSTANDING LANDSCAPE
SCALE DISTRIBUTIONS OF KEY TAXA
Submitted by CARLOS EDUARDO GONZALEZ OROZCO
A THESIS SUBMITTED TO THE UNIVERSITY OF LONDON
FOR THE DEGREE OF DOCTOR OF PHILOSOPHY
KING’S COLLEGE LONDON
DEPARTMENT OF GEOGRAPHY
2008
1
ABSTRACT
The aim of the thesis is to use high-resolution aerial photographic imagery for the identification of tree crowns. The study site is Tiputini Biodiversity Station
(TBS) located in the Amazon of eastern Ecuador. Visual/manual aerial identification keys have been produced, tested with 100 volunteers and validated.
The keys are useful to produce distribution maps across the landscape.
A total of 2333 crowns corresponding to ten taxa were mapped. The mapped families and genus are Arecaceae (Iriartea and Astrocaryum), Fabaceae (Inga and Parkia), Moraceae (Cecropia and Pourouma), Bombacaceae (Ceiba),
Meliaceae (Guarea), Myristicaceae (Otoba) and Sapotaceae (Pouteria). Eight terrain variables were used as environmental controls in a distribution analysis.
These variables are elevation, mean curvature, slope, slope position, eastness, northness, solar radiation and TopModel wetness index. The distribution maps for taxa at TBS are used as indicators for better understanding patterns of tree distribution and diversity as well as the environmental controls upon them.
The validation of the key with many and varied users presents identification accuracy over 50% for five of ten taxa. Crown with an intermediate rough texture are less reliable in term of identification accuracy (ID) than crown properties with regular or well-defined surfaces. When spatial patterns are examined, clumped spatial distribution patterns are reported for the majority of the mapped taxa. Two distribution forms are suggested: linear and non-linear. The predominant forms are lines, pits (clusters) and S-shaped distribution patterns.
2 There are interesting correlations between terrain characteristics and tree composition. Analysis of terrain and taxa distribution shows that the more statistically significant terrain variables controlling taxa distributions are elevation, slope, slope position and curvature. When diversity patterns are examined, the northeast part of TBS appears to have the highest diversity of taxa and a local hotspot map is proposed. The aerial identification technique provides encouraging results as a useful methodological tool for understanding biotic/abiotic environmental processes in Amazonian forests.
3 RESUMEN
El propósito de la tésis es usar fotografía aérea de alta resolución para la identificación de copas de árboles. El sitio de estudio es la Estación de
Biodiversidad Tiputini (TBS) localizada en la parte este del Amazonas
Ecuatoriano. Se produjeron claves de identificación aérea de tipo visual y manual. Estas claves fueron probadas y validadas por 100 voluntarios. Las claves resultaron útiles para producir mapas de distribución de árboles através del paisaje.
Un total de 2333 copas que correponden a 10 taxa fueron mapeadas. Las familias y generos mapeados fueron: Arecaceae (Iriartea and Astrocaryum), Fabaceae
(Inga and Parkia), Moraceae (Cecropia and Pourouma), Bombacaceae (Ceiba),
Meliaceae (Guarea), Myristicaceae (Otoba) and Sapotaceae (Pouteria). Ocho variables de terreno fueron usadas como reguladores medioambientales en un análisis de distribución. Las variables de terreno son altitud, curvatura media, inlcinación, posición de la inclinación, aspecto del terreno, radiación solar y humedad del suelo. Los mapas de distribución en TBS son usados como base para un mejor entendimiento de los patrones de distribución y diversidad de los
árboles y asi como también entender cuales son las variables de terreno que más influyen en su distribución.
Numerosos y variados usuarios de la clave obtuvieron una exactitud de identificación promedio mayor de 50 % en cinco de los diez taxa estudiados. Las copas de los árboles que tienen una textura de tipo rugosa intermedia son menos confiables a la hora de medir la precisión de identificación pero las copas con
4 una superficie regular o bien definida son de mayor confiabilidad de identificación.
Con respecto a los patrones de distribución espacial, la mayoria de los taxa mapeados presentaron una distribución espacial agrupada. Dos formas de distribución son propuestas: lineares y no lineares. Dentro de estas, los tipos predominantes de distribución son líneas cortas, grupos de líneas y curvas en forma de S.
Se encontraron correlaciones interesantes entre la composición de árboles y las características del terreno. Los análisis estadísticos muestran que las variables de terreno que más influeyen la distribución de los taxa en el pasisaje son altitud, inclinación, posición de la inclinación y curvatura. Examinando los patrones de diversidad se reporta que la parte nor-este de TBS presentó la mayor diversidad y al mismo tiempo se presenta un mapa de zonas claves de diversidad de árboles.
La técnica de identificación aérea ha producido resultados alentadores ya que es una herramienta metodológica de gran utilidad para entender los procesos medioambientales (bióticos o abióticos) que se presentan en el bosque
Amazónico.
5 ACKNOWLEDGEMENTS
The author thankfully acknowledges funding provided by Programme Alban
(European Union Programme of High level Scholarships for Latin America,
Award nº E03D26669CO), WWF (Russell E. Train fellowship, Education for
Nature Program), Royal Geographical Society fieldwork grant, Rufford Small
Grant and Herb project fieldwork support (King’s College London).
I am profoundly grateful to Dr. Mark Mulligan, my supervisor, for his unconditional advice and wise academic support. He stood up to me during the past 4 years, thank you... it was a great experience for me also! Mark’s cleverness and unique personal and technical capabilities helped me to mature. I nearly understood many of his tastes, but I never quite fancied his brave beer preferences, particularly with the Nut Brown Ale Bottles at the Lyceum pub on the Strand.
Before following the tradition of expressing gratitude to my parents, I want to thank to Caroline Anne Hay “la chinita” for all her help (English Editor), support and patience during the first two years of my thesis.
To my parents, with all my love, this thesis is dedicated to Maria Cristina Orozco and of course to my dad as well. Thank you to my UK parents, I say, Mr and Mrs
Hay in Eildon St. in Edinburgh, they welcomed me into their lovely home and fed me very well by the way. Unfortunately I was not able to beat Richard in golf, but perhaps I performed better in the kitchen.
6
I would like to thank to Camilla Smith for her English spe!!ing correction at
Borough market in London. I would like to express my sincere gratitude to Dr.
Andrew James Jarvis for his constant personal support and also to his wife Ingrid for her patience during my visits. I always considered him a great scientist and also as “mi segundo supervisor.”
I am extremely grateful to Mr. Robert Giles, my London housemate. I will never forget his immense generosity. I must also thank my housemates in Cr. 10 in
Cali, Colombia, San Antonio (Felipe, Norbert and the others).
I am also deeply grateful to Fabian Nenquimo for his fieldwork support during the long and humid days looking for trees in the Amazonian rain forest in
Ecuador.
In Cali, I would also like to thank to Dr. Jorge Rubiano and Dr. Mauricio Rincon for their support in providing me access to the GIS laboratories for conducting the online identification exercise in Universidad Nacional de Colombia in
Palmira and Universidad del Valle in Cali. Thanks to Edith Hesse for providing logistical support at the beginning of my thesis. It was a pleasure to regularly share experiences with all the wonderful Land Use project team at Ciat.
Special thanks to the Universidad San Francisco de Quito and particularly to the
TBS staff that logistically helped a great deal, including Constanza, Jaime,
David, Kelly, and all the fieldworkers, “the tigers” working there deep in the
7 jungle. I thank the following institutions: Tiputini Biodiversity Station TBS,
Universidad Catolica-Quito-, Universidad San Francisco de Quito, Centro
Internacional de Agricultura Tropical-CIAT, Museo de Historia Natural
(Santiago Ayerbe), Universidad del Cauca.
8 TABLE OF CONTENTS
ABSTRACT ...... 2
RESUMEN...... 4
ACKNOWLEDGEMENTS ...... 6
CHAPTER 1: INTRODUCTION AND RESEARCH GOALS...... 22
1.1 Introduction...... 22
1.2 The research problem...... 24
1.3 Hypotheses ...... 26
1.4 Aim ...... 26
1.5 Objectives...... 27
1.6 Rationale ...... 29 1.6.1 The challenge of landscape inventory...... 29 1.6.2 The limitations of traditional taxonomy...... 30 1.6.3 Forest conservation ...... 32
1.7 The outline of the thesis...... 32 1.7.1 Chapter 1. Introduction and research goals...... 33 1.7.2 Chapter 2. Literature review ...... 33 1.7.3 Chapter 3. Research strategy and methods ...... 33 1.7.4 Chapter 4. Developing and testing crown properties and signatures for key taxa ...... 34 1.7.5 Chapter 5. The spatial patterns in the distribution of key taxa...... 34 1.7.6 Chapter 6. Tree distribution of key taxa in relation to terrain...... 35 1.6.7 Chapter 7. Conclusions and future work...... 35
CHAPTER 2: LITERATURE REVIEW...... 36
9 2.1 The challenge of arboreal inventory in high diversity environments...... 36 2.1.1 Lowland forest tree composition...... 37 2.1.2 Floristic composition analysis at the landscape scale ...... 39
2.2 Approaches to diversity assessment ...... 40 2.2.1 Higher-taxa...... 42 2.2.2 Structural diversity...... 43 2.2.3 Plant Functional types...... 45 2.2.4 Conservation value...... 46
2.3 Conventional taxonomic inventory...... 48 2.3.1 Ground plot assessment ...... 48 2.3.2 Plot and transect-based approaches...... 53
2.4 The Aerial approach ...... 55 2.4.1 Crown separation ...... 55 2.4.2 Key case studies ...... 58 2.4.3 Identification using binoculars...... 64 2.4.4 Use of botanical keys ...... 65 2.7.1 Basic concepts and methods of aerial photography ...... 66 2.7.1.1 Extent ...... 66 2.7.1.2 Resolution ...... 66 2.7.2 Automatic crown segmentation ...... 67
2.8 Main aspects affecting aerial crown identification ...... 69 2.8.1 Light interaction with canopy ...... 70 2.8.1.1 Shadow effects ...... 71 2.8.1.2 Light penetration ...... 71 2.8.1.3 Tree phenology...... 73 2.8.1.4 Tree structure ...... 74
2.9 Spatial distribution of trees in Lowland rainforest...... 75
2.10 Landscape variables controlling the spatial distribution of taxa ...... 77
2.11 Seed dispersal associated to biota ...... 82
10 CHAPTER 3: RESEARCH STRATEGY AND METHODS ...... 86
3.1 Introduction...... 86
3.2 Overall research strategy ...... 87
3.3 Description of the field site...... 89
3.4 Collection of Aerial photography ...... 92 3.4.1 Data collection ...... 93 3.4.2 Instruments and technical specifications...... 94
3.5 Description of the technique...... 94 3.5.1 Airborne Aerial Photography (AAP) ...... 96 3.5.1.1 Data collection ...... 96 3.5.1.2 Instruments and technical specifications...... 98 3.5.1.3 Georeferencing...... 98 3.5.1.4 TBS canopy forests dataset ...... 100
3.6 Field data collection ...... 100 3.6.1 Sampling strategy...... 101 3.6.2 Plots stratified by landscape...... 102 3.6.2.1 Imagery centred selection ...... 106 3.6.2.2 Field centred data collection ...... 107 3.6.3 Data collection ...... 108 3.6.3.1 Finding the trees...... 109 3.6.3.2 Identifying the taxa ...... 112 3.6.3.3 Labelling the trees...... 113
3.7 Analytical methodology ...... 113 3.7.1 Developing a crown identification technique ...... 113 3.7.2 Methodological strategy...... 115 3.7.2.1 Storing data ...... 115 3.7.3 Development of a visual interpretation key ...... 115 3.7.3.1 Leaf presence ...... 116 3.7.3.2 Woody elements...... 117
11 3.7.3.3 Crown type...... 117 3.7.3.4 Foliage texture...... 118 3.7.3.5 Foliage continuity ...... 120 3.7.3.6 Crown shape...... 121
3.8 Analysing crown imagery...... 122 3.8.1 Development of a crown separation system...... 123 3.8.2 Developing an online key ...... 125 3.8.2.1 The concept of the key ...... 125 3.8.2.2 Instructions given to the key respondents ...... 126 3.8.2.3 Key respondents...... 127 3.8.2.4 Crowns to be identified ...... 128 3.8.2.5 Web-interface...... 129 3.8.2.6 Key validation: identification accuracy ...... 132 3.8.3 Mapping higher taxa based on the aerial identification key ...... 134 3.8.4 Spatial patterns...... 135 3.8.4.1 Moran-I spatial autocorrelation index...... 136 3.8.4.2 The Ripley-K measure of aggregation ...... 136 3.8.4.3 Distance analysis...... 137 3.8.4.4 Clustering analysis ...... 138 3.8.5 Taxa distribution in relation to terrain ...... 139 3.8.5.1 Terrain maps...... 141 3.8.5.2 Landscape analysis...... 141 3.8.5.3 Statistical analysis of terrain variables...... 142 3.8.5.4 Diversity analysis...... 143
CHAPTER 4: DEVELOPING AND TESTING CROWN PROPERTIES AND SIGNATURES FOR KEY TAXA ...... 144
4.1 INTRODUCTION...... 144
4.2 RESULTS AND DISCUSSION ...... 145 4.2.1 Development of the key ...... 145 4.2.1.1 Crown properties (CP) ...... 146 4.2.1.1.1 CP at family level...... 147
12 4.2.1.1.2 CP at genera level ...... 148 4.2.1.2 Crown Property Signatures (CPS) ...... 151 4.2.1.2.1 CPS at family and genera level ...... 151 4.2.2 The online key and their validation...... 154 4.2.2.1 Identification accuracy...... 155 4.2.2.2 Crown properties mis-identified ...... 157 4.2.2.3 Key limitations...... 159
4.3 CONCLUSIONS ...... 161
CHAPTER 5: THE SPATIAL PATTERNS IN THE DISTRIBUTION OF KEY TAXA...... 163
5.1 INTRODUCTION...... 163
5.2 RESULTS AND DISCUSSION ...... 164 5.2.1 Spatial patterns...... 164 5.2.1.1 Point distribution maps ...... 165 5.2.1.2 Distance distribution analysis ...... 172 5.2.1.2.1 Non-linear ...... 172 5.2.1.2.2 Linear distributions ...... 176 5.2.1.3 Moran-I spatial autocorrelation index...... 185 5.2.1.4 Clustering analysis ...... 187 5.2.1.5 The Ripley-K measure of aggregation ...... 194
5.4 CONCLUSIONS ...... 196
CHAPTER 6: THE DISTRIBUTION OF KEY TAXA IN RELATION TO TERRAIN AT TBS ...... 198
6.1 INTRODUCTION...... 198
6.2 RESULTS AND DISCUSSION ...... 198 6.2.1 TERRAIN VARIABLES...... 198 6.2.1.1 Elevation ...... 200 6.2.1.3 Mean curvature...... 201
13 6.2.1.4 Slope...... 202 6.2.1.5 Slope position...... 203 6.2.1.6 Eastness...... 204 6.2.1.7 Northness ...... 204 6.2.1.8 Solar radiation ...... 205 6.2.1.9 TopModel wetness ...... 206 6.2.2 LANDSCAPE PROPERTIES...... 207 6.2.2.1 Elevation ...... 208 6.2.2.2 Mean curvature...... 209 6.2.2.3 Slope...... 210 6.2.2.4 Slope position...... 211 6.2.2.5 Eastness...... 212 6.2.2.6 Relative northness ...... 213 6.2.2.7 Solar radiation ...... 214 6.2.2.8 TopModel wetness index ...... 215 6.2.3 STATISTICAL ANALYSIS OF TERRAIN VARIABLES...... 216 6.2.3.1 Arecaceae ...... 220 6.2.3.1.1 Astrocaryum chambira...... 221 6.2.3.1.2 Iriartea deltoidea ...... 225 6.2.3.2 Fabaceae...... 230 6.2.3.2.1 Inga...... 232 6.2.3.2.2 Parkia ...... 235 6.2.3.3 Meliaceae ...... 236 6.2.3.3.1 Guarea ...... 237 6.2.3.4 Moraceae...... 239 6.2.3.4.1 Cecropia ...... 241 6.2.3.4.2 Pourouma ...... 243 6.2.3.5 Myristicaceae ...... 245 6.2.3.5.1 Otoba...... 246 6.2.3.6 Sapotaceae...... 248 6.2.3.6.1 Pouteria ...... 249 6.2.4 DIVERSITY ANALYSIS...... 251 6.2.4.1 Tree richness ...... 251 6.2.4.2 Tree diversity in relation to terrain...... 253
14 6.3 CONCLUSIONS ...... 254
CHAPTER 7: FINAL CONCLUSIONS ...... 258 7.1.1 General limitations...... 262 7.1.2 Future Research Avenues...... 262
REFERENCES ...... 265
APPENDIX 1 ...... 283
APPENDIX 2 ...... 297
APPENDIX 3 ...... 317
APPENDIX 4 ...... 329
APPENDIX 5 ...... 344
APPENDIX 6 ...... 351
APPENDIX 7 ...... 367
15 LIST OF FIGURES
Figure 1 Interpretation key for the mangroves of Galle, Sri Lanka (From Verheyden et al, (2002)...... 62 Figure 2 Profile diagram of three forest vertical strata in Australia (Lowman, 1986) ...... 72 Figure 3 Geographical distribution of the 10 25 x 25 m plots established in Tiputini Biodiversity Station (Jarvis 2005)...... 78 Figure 4 50 hectare plot located in Yasuni National Park, 1 km x 0.5 km (Valencia et al, 2004) ...... 80 Figure 5 Geographical location TBS (From: Keizer, 2007) ...... 90 Figure 6 River Tiputini around Tiputini Biodiversity Station (TBS), Ecuador ...91 Figure 7 Helium balloon with digital camera device...... 93 Figure 8 GTAP method for helium balloon image acquisition from forest gaps (From Jarvis, 2005) ...... 95 Figure 9 Aerial photography equipment...... 96 Figure 10 Frontal view of the camera used for aerial photography ...... 97 Figure 11 Flight route for aircraft surveys over study region at TBS, Ecuador (From Jarvis, 2005) ...... 97 Figure 12 Spatial distribution of the crowns mapped and collected during fieldwork in TBS. The red lines correspond to the network of paths and the dots represent each of the crowns located and identified during the fieldtrips (red for the first, green for the second and yellow for the final field visit). Red and green were imagery centred crown selection and yellow were field centred data collection...... 102 Figure 13 Missing sampling units (red areas) around TBS...... 106 Figure 14 Collection sectors (blue circles or rectangles) at TBS. The base camp is located within the rectangle number 21. Red and green were imagery centred crown selection...... 109 Figure 15 Image centred data collection technique for mapping trees in Amazonian rain forest, TBS, Ecuador ...... 111 Figure 16 Using Photoshop to make a visual dataset for each crown collected 115 Figure 17 Multiple and single crown properties ...... 118
16 Figure 18 Smooth and mottled crown properties...... 118 Figure 19 Granular and smoky crown properties...... 119 Figure 20 Grainy and dotted crown properties ...... 120 Figure 21 Continuous and discontinuous crown properties...... 120 Figure 22 Flat and rounded crown properties ...... 121 Figure 23 Interpretation key (find an A3 size version of this figure in appendix 6) ...... 122 Figure 24 Conceptual basis used for the identification: A. Key 1 on the top is the approach using a diagram, and B. Key 2 uses a real image apart from the drawing and a diagram...... 125 Figure 25 Images of the 10 crowns to be identified and their Latin names. From left to right: upper row from left to right Arecaceae- Iriartea deltoidea; Arecaceae- Astrocayum chambira; Fabaceae- Inga; Fabaceae- Parkia; Moraceae- Cecropia. Bottom row from left to right: Moraceae- Pourouma; Meliaceae- Guarea; Myristicaceae- Otoba; Bombacaceae- Ceiba; Sapotaceae- Pouteria ...... 128 Figure 26 Access to key using GoogleEarth...... 130 Figure 27 Starting the key...... 130 Figure 28 First step for choosing if the crown looks like a palm or not ...... 131 Figure 29 Final step for the identification of the palm tree...... 132 Figure 30 Example of a distance distribution map at TBS (meters)...... 138 Figure 31 Example of the clustering patterns for Astrocaryum chambira at TBS (stem density relative to the mean stem density for the taxon for the study area)...... 139 Figure 32 This is a schematic example (profiles and original crown images) for each of the main CP and its sub-classes...... 146 Figure 33 Crown property exclusivity for foliage texture (top), crown type (middle) and crown shape (bottom) at family level. Values close to 1 signify high exclusivity...... 148 Figure 34 Structural variation of the three main crown properties at genera level. Values close to 1 signify high dominance...... 149 Figure 35 Comparison of crown type between two palm species against non- palms. Values close to 1 signify high exclusivity...... 150
17 Figure 36 An example of four taxonomic groups (families) obtained using three hierarchical levels...... 152 Figure 37 CPS for families (top) and genera (bottom) level. Values close to 1 signify high dominance...... 153 Figure 38 Identification accuracy percentage for ten taxa using a sample size of around 100 key respondents...... 156 Figure 39 Crown properties with the least mis-identification (A) and properties with most mis-identifications (B)...... 158 Figure 40 Point patterns for the spatial distribution of ten taxa at TBS. From top to the bottom the taxon maps are Arecaceae (Iriartea deltoidea, Astrocaryum chambira), Bombacaceae (Ceiba), Fabaceae (Inga, Parkia), Moraceae (Cecropia, Pourouma), Meliaceae (Guarea), Myristicaceae (Otoba) and Sapotaceae (Pouteria)...... 171 Figure 41 Non linear distribution for Irartea deltoidea, Cecropia and Inga....174 Figure 42 Linear distribution for Astrocaryum chambira, Pourouma, Guarea and Otoba...... 179 Figure 43 Generally non linear (i.e. isolated individuals or groups forming semi- circular shapes) distribution for Ceiba, Parkia and Pouteria ...... 183 Figure 44 Clusters with regular distribution pattern for Iriartea deltoidea, Astrocaryum chambira, Cecropia and Inga...... 189 Figure 45 Clustered with aggregation patterns for Parkia, Pourouma, Guarea and Otoba...... 192 Figure 46 Dispersed pattern for Ceiba and Pouteria ...... 193 Figure 47 Multi-scale Ripley-K spatial aggregation analysis for ten key taxa distributed across TBS. Clustering increases K, while regularity decreases K...... 195 Figure 48 DEM for the area of 600 m imagery analysed (meters above sea level) ...... 200 Figure 49 Mean curvature for the mapped area (index)...... 201 Figure 50 Slope for the mapped area in TBS (degrees)...... 202 Figure 51 Slope position for the mapped area in TBS (index) ...... 203 Figure 52 Eastness for the mapped area in TBS (index)...... 204 Figure 53 Northness for the mapped area in TBS (index) ...... 205 Figure 54 Solar radiation for the mapped area in TBS (W/m²/yr)...... 206
18 Figure 55 TopModel wetness for the mapped area in TBS (index)...... 207 Figure 56 Relative elevation for the mapped area at TBS ...... 209 Figure 57 Relative Mean curvature for the mapped area in TBS ...... 210 Figure 58 Relative slope for the mapped area in TBS ...... 211 Figure 59 Relative slope position for the mapped area in TBS ...... 212 Figure 60 Relative eastness for the mapped area in TBS...... 213 Figure 61 Relative northness for the mapped area in TBS ...... 214 Figure 62 Relative solar radiation for the mapped area at TBS...... 215 Figure 63 Relative TopModel wetness index for the mapped area in TBS ...... 216 Figure 64 Frequency distribution for the difference in mean of the terrain variables between the mapped and random points for Astrocaryum chambira ...... 224 Figure 65 Profile diagram for A chambira (the palm with the green crown) showing slope position...... 225 Figure 66 Frequency distribution for the difference in mean of the terrain variables between the mapped and random points for Iriartea deltoidea..229 Figure 67 Profile diagram for I deltoidea (the palm with the red crown) showing slope position...... 230 Figure 68 Profile diagram for Inga showing slope position...... 233 Figure 69 Terrain controls for Inga (Fabaceae)...... 234 Figure 70 Terrain controls for Parkia (Fabaceae)...... 235 Figure 71 Terrain controls for Guarea (Meliaceae)...... 238 Figure 72 Profile diagram for Guarea showing slope position...... 239 Figure 73 Slope position representation for Cecropia (Top) and Pourouma (Bottom)...... 242 Figure 74 Terrain controls for Cecropia (Moraceae)...... 243 Figure 75 Terrain controls for Pourouma (Moraceae)...... 245 Figure 76 Profile diagram for Otoba showing slope position...... 247 Figure 77 Terrain controls for Otoba (Myristicaceae)...... 248 Figure 78 Terrain controls for Pouteria (Sapotaceae)...... 250 Figure 79 Diversity maps (richness related) for 10 tree taxa at TBS. 250 m diameter map (top) and 500 m diameter map (bottom) ...... 252 Figure 80 Diversity of taxa in relation to elevation at TBS ...... 253 Figure 81 Local hotspots map for TBS...... 254
19 LIST OF TABLES
Table 1 A chronological summary of the development of plant functional types. From Duckworth et al., (2000)...... 46 Table 2 Descriptive example of a traditional dichotomous key (From: http://www.saskschools.ca/curr_content/biology20/unit3/unit3_mod1_les2.h tm) ...... 66 Table 3 NIKON 990 digital camera parameters...... 94 Table 4 Image acquisition and ground coverage...... 98 Table 5 Number of individual in sampling strategy ...... 103 Table 6 Number of individuals represented within the sampling units for the image centred collection case (top) and sampling units factors (bottom)..104 Table 7 Representation of the sampling units per taxa ...... 105 Table 8 Total number of individuals per taxa collected in TBS ...... 108 Table 9 Properties used for describing the TBS tree crowns...... 116 Table 10 Instruction emailed to the key respondents about how use the online key...... 127 Table 11 Number of mapped taxa using the aerial identification key ...... 134 Table 12 The most frequent CPS at family level ...... 151 Table 13 Error matrix for Key 1 and Key 2 based on 100 key respondents...... 159 Table 14 Some of the respondents comments about the online key ...... 160 Table 15 Spatial autocorrelation measure using Moran’s I C index at TBS. For Moran’s I: (1) if I > E (I) then clustered patterns (2) if I ~= E (I) then random and (3) if I < E (I) then dispersed. Z-scores < 0.05 is statistically significant. Values in grey indicate positive spatial autocorrelation and clustered distribution...... 186 Table 16 Summary of the terrain variables for TBS (From Jarvis 2005)...... 199 Table 17 Summary of the statistical significance (K-S test) between terrain characteristics and ten key taxa trees in TBS, the statistically significant cases are highlighted in grey...... 218 Table 18 Statistic and p-value using Kolmogorov-Smirnov test for Astrocaryum chambira (left) and Iriartea deltoidea (right) related to eight terrain characteristics in TBS. Statistical significance (p<0.05) are highlighted in grey...... 220
20 Table 19 Summary of the mean values for Astrocaryum chambira ...... 222 Table 20 Summary of the mean values for Iriartea deltoidea...... 226 Table 21 Statistic, p-value and Kolmogorov-Smirnov test for Inga (left) and Parkia (right) related to nine terrain characteristics in TBS. Statistical significance (p<0.05) is highlighted in grey...... 231 Table 22 Summary of the mean values for Inga...... 232 Table 23 Summary of the mean values for Parkia ...... 235 Table 24 Statistic, p-value and Kolmogorov-Smirnov test for Guarea (Meliaceae) related to nine terrain characteristics in TBS. Statistical significance (p<0.05) is highlighted in grey...... 236 Table 25 Summary of the mean values for Guarea ...... 237 Table 26 Statistic, p-value and Kolmogorov-Smirnov test for Cecropia (left) and Pourouma (right) related to eight terrain characteristics in TBS. Statistical significance (p<0.05) is highlighted in grey...... 240 Table 27 Summary of the mean values for Cecropia ...... 241 Table 28 Summary of the mean values for Pourouma ...... 244 Table 29 Statistic and p-value using F, t and Kolmogorov-Smirnov test for Otoba (Myristicaceae) related to nine terrain characteristics in TBS. Statistical significance (p<0.05) is highlighted in grey...... 246 Table 30 Summary of the mean values for Otoba ...... 246 Table 31 Statistic and p-value using F, t and Kolmogorov-Smirnov test for Pouteria (Sapotaceae) related to nine terrain characteristics in TBS. Statistical significance (p<0.05) is highlighted in grey...... 249 Table 32 Summary of the mean values for Pouteria ...... 249
21 CHAPTER 1: INTRODUCTION AND RESEARCH GOALS
This thesis falls within the context of an ongoing research effort to better understand the spatial distribution of biological diversity for the better targeting of inventory and conservation efforts in tropical lowland and tropical montane forest environments: The HERB project 1997-2007, http://www.ambiotek.com/herb.
Implementing aerial taxonomy techniques using crown imagery alone is a considerable challenge. For this reason, the main focus of this thesis is to develop a better understanding of the requisites for successful tree identification from the air and the image specifications and processing that will need to be carried out in order to make this possible.
1.1 Introduction
According to the 1993 Convention of Biological Diversity, Article 2, biodiversity is “the variability among living organisms from all sources, including, inter alia, terrestrial, marine, and other aquatic ecosystems and the ecological complexes of which they are a part; this includes diversity within species, between species and of ecosystems” (Heywood., 1995). Biodiversity is therefore extremely complex and deals with the composition, structure, and process of its component parts
(Noos and Cooperrider, 1994).
22 As demonstrated by Lovejoy (1997), tropical lowland forests harbour most of the world’s biodiversity, and are constantly disappearing to give way to agriculture.
In South America, for example, tropical forest cover declined by 23,277,000 hectares; a loss of 2.7% from 1990 to 1995 (FAO, 1997). This decline in forest cover inevitably leads to loss of wildlife habitat and, ultimately, loss of biodiversity (Myers, 1986).
The forest in tropical South America remains largely unknown from a compositional standpoint (Terborgh and Andresen, 1998). In the past few decades, researchers have become increasingly interested in documenting and understanding the tropical rain forest (Tuomisto et al., 2003). Considering that
“as many as 44 % of all species of vascular plants and 35 % of all species in four vertebrate groups are confined to 25 hotspots comprising only 1.4 % of the land surface of the Earth” (Myers et al., 2000), this research to identify and classify the biological diversity of tropical forests is fast becoming critically important to the effective management and protection of these systems in the face of land use and climate change.
It is clear that traditional identification techniques have not fulfilled the need for rapid surveying of endangered neotropical flora. Edwards & Morse (1995) state that much of the necessary biodiversity research simply cannot be conducted, not because the species are unknown to science, but because the taxonomic and human resources required to identify them are not available. Although this thesis does not aim to solve the issue of plant species identification in its entirety, it will go some way towards providing techniques for more rapid inventory, by
23 breaking out of the slow ground-based, traditional taxonomic methodologies and developing new techniques for identification and inventory over larger areas using increasingly readily available aerial photography.
Aerial taxonomy has just begun to be explored as a potential tool for future forest assessments. Johnson (1995) notes that “within the context of forests, trees are the main structural organisms, and there is strong evidence that tree diversity is an indicator of total species diversity across all taxa”. Tree species are not easy to identify in the hyper-diverse tropical lowlands, even from ground-based observation using traditional taxonomic methods. Indeed, as Gentry (1993) observes, plant identification is a challenge faced every day in the tropics.
1.2 The research problem
Traditional taxonomic techniques are time-consuming, as teams of botanists on the ground need to identify each tree individually using floral characteristics, which are not always present. If a floristic survey needs to be carried out over a large region, ground assessment would not be the most suitable method in terms of time and effort. As a result much inventory is carried out intensively at the 1-
50 Ha. plot scale and very little is known in between these widely separated plots. In contrast, with aerial photography, a large area can be viewed in a single image, thus providing much greater coverage in less time. However, while traditional inventory can identify almost every tree, aerial inventory only
24 captures the tree crowns present at the top of the canopy and does not necessary enable the complete identification of even those trees.
Individual crowns can be recognised with high-resolution aerial imagery but these images still do not have a high enough resolution to view some of the morphological structures necessary for traditional taxonomic identification such as stipules or stem characteristics. Such morphological characteristics must be observed to identify species when using traditional taxonomic methods. If individual species of tree cannot be identified using traditional taxonomic methods (dichotomous keys) with high-resolution aerial photography, an alternative solution is needed which provides features of the crown that can be used for identification from the air.
It may be possible to recognise crown characteristics using aerial photography, and derive reliable means of identification for particular taxa at the family, genus or even species level, though in many cases identification to species level may not be possible. This limits the aerial photographic approach to higher taxonomic levels, more specifically, at the family or genera level. Mapping at these higher taxonomic levels reduces the biodiversity information but does enable the observation of distributions over much larger areas than is possible by plot based studies. A key question is therefore which element of diversity is it most important to provide information for: taxonomic diversity, species diversity, structural diversity, genetic diversity, plant functional types or some other measure. The answer clearly depends on the objective of the mapping. In relation to inventory and protection purposes one first has to decide which of
25 these needs to be protected i.e. which best represents the ‘value’ of biodiversity
(see more information in section 2.2.4 in Chapter 2).
1.3 Hypotheses
1. The aerial approach can be used to identify aspects of tree diversity,
which are useful for landscape-scale analysis of tree diversity patterns,
something that is very difficult using traditional plot-based techniques.
2. The distribution of key taxa at the landscape scale [up to 2 sq. km],
surveyed using high-resolution aerial photography has a non-random
distribution, which can be at least partially explained by specific terrain
characteristics and/or ecological processes.
1.4 Aim
The basic aim of the thesis is to use high-resolution aerial photographic imagery for the identification of tree crowns to family, genus and species level, as applicable. Another relevant aspect is to produce spatial distribution maps. The resulting maps for taxa at Tiputini Biodiversity Station (TBS) are used as indicators for better understanding patterns of tree distribution and diversity as well as the environmental controls upon them, building on previous research
(Jarvis, 2005). This research aims to bridge the gap between the traditional taxonomic techniques for tree identification used in botany with high-resolution aerial photographic data, which is becoming more accessible in recent years and holds promise for larger area studies of species distributions. These new data sources will be used alongside traditional approaches to tree identification,
26 mapping and composition inventory at the landscape scale. The term
“composition inventory” refers to the assessment of the floristic composition of a plant community.
Little research has been published on the identification of trees from a distance, and consequently, our understanding of distance identification and of crown characteristics that might be used for identification is poor.
1.5 Objectives
The general objectives of this thesis are to:
1. Explore which canopy characteristics might be used for identifying
particular trees from a distance.
2. Develop techniques for the robust description, classification, and
quantification of these characteristics.
3. Integrate these techniques within a semi-objective system for
identification, based on the traditional taxonomic approach of
dichotomous taxonomic keys.
4. Apply the resulting key using multiple human analysts in order to test the
identification accuracy.
5. Map all occurrences of particular taxa at TBS and examine the spatial
patterns in the distribution of these at the landscape scale
6. Correlate the spatial distribution of key taxa in relation to terrain at TBS.
27 The specific objectives are to:
(a) Collect and process high-resolution airborne imagery over an area of lowland tropical forest in areas where ground survey data are available for comparison and validation.
(b) Develop methods and standardised procedures to:
• Describe crown properties of use in crown identification.
• Classify the properties of individual crowns for taxonomical
identification.
• Identify (higher taxa) families and/or genera from these characteristics.
(c) Integrate this information to:
• Produce visual, manual or semi-objective paper and web-based
identification keys.
(d) Apply the techniques for the identification of crowns around Tiputini
Biodiversity Station – Ecuador, where ground-collected validation data are available. Validate the aerial identifications against ground truths and provide recommendations for improving the imagery and identification keys.
(e) Analyse the spatial distribution patterns for identified taxa across TBS by using the spatial autocorrelation index and spatial clustering analysis
28 (f) Integrate the spatial distribution patterns of key taxa with known terrain variation across the reserve in order to better understand tree diversity patterns and its relationship with:
• Elevation, slope, slope position, eastness, northness, soil moisture and
solar radiation at the landscape scale
1.6 Rationale
1.6.1 The challenge of landscape inventory
Remote sensing technologies may provide potential data for future forest assessments (Held et al, 2003). Bridging the gap between traditional taxonomy and the potential for new aerial identification techniques by developing key for aerial identification is therefore an urgent area for research.
The majority of recent neotropical lowland tree diversity research has been conducted using ground-based techniques, rather than satellite or airborne methods, largely because of historic spectral and spatial resolution limitations of the latter. Standard plant taxonomy systems provide few appropriate methodologies for taxonomic analysis over large forested areas (Chazdon, 1999).
In order to meet the new demands for tree identification and diversity assessment over large areas, an innovative plant identification technique is necessary. Slow progress in implementing agile and fast ground-based tree inventory techniques is another reason for developing new approaches, which aim to link conventional knowledge with new remote sensing technologies.
29 Traditional floristic ground surveys are conducted at the plot scale. New challenges in conservation planning and management in tropical forests mean that new assessment techniques need to be explored, and then used more efficiently to produce forest composition inventories at the landscape scale, since this is the scale at which conservation and management decisions need to be made. Understanding tropical forest tree diversity involves understanding the distribution of taxa at the landscape scale and that is barely possible from the rather rare plot based studies that exist. The combination of plot based intensive studies and inter-plot identification by aerial means may yield important data and discoveries in the understanding of landscape and environmental controls on tropical forest diversity.
Implementing aerial taxonomy techniques using crown imagery alone is a considerable challenge. This thesis will not provide a definitive method but will develop a pilot technique to better understand the image, processing and analytical requirements for tree identification from the air, and at what level of taxonomic detail - and for which taxa - it is possible.
1.6.2 The limitations of traditional taxonomy
According to Thorne (1992), more than “230,000 species of higher plants have been recorded in the world”, of which “over 180,000 are from tropical regions”
(Raven, 1988). Since the 18th century, botanists have been using traditional nomenclature to classify plants according to Carolus Linnaeus’ (1707-1778) taxonomical system (Porter, 1967). At current rates of progress, it may take several hundred more years to classify all plants using this system. This is
30 particularly true in the hyper-diverse Amazonian lowland forest where “200 -
300 species can be found with DBH greater than 10 cm over a single hectare”
(Gentry, 1992).
Ground surveys have taken either years of fieldwork for lone botanists or massive funds have been poured into intensive research efforts such as the network of SI/MAB permanent plots. In most cases, these are focused on small- area studies over plots with little potential for scaling up to the region. With a 25 hectare plot case study in Yasuni National Park, Romolerux (1997) showed that after 10 years of continuous work, the taxonomy of all species still remains uncertain, leading us to observe that even with a lot of effort put into traditional taxonomical work, the state of the art in conventional taxonomy does not seem to develop at the same speed as conservation needs and concerns. In a two-hectare sample of the plot, “sixty-two percent of the 781 species identified have a scientific name and the remaining 38% have a temporary name”. From this example, it could be concluded that the focus on identifying species is not helping to efficiently measure the diversity and conservation priority of forests.
Although plot inventory is important, more research has to be done using alternative techniques, which may help to understand how future measures of tree diversity and conservation priority should be developed in tropical rain forests.
It is clear that there is currently a lack of experts in taxonomy as well as a large number of undiscovered or unidentified plants, which are often only subtlety different to known species. More time and resources will be necessary to
31 describe uncertain taxa, which implies a crisis in terms of conservation as each day land degradation and deforestation rates are increasing. One of the aspects to be urgently tackled is the development of new approaches to measure and value diversity, especially in diverse ecosystems such as Amazonian forests.
1.6.3 Forest conservation
Using satellite imagery has not, to date, proven very accurate for species identification, while ground-sampling techniques have not led to complete taxonomical identifications either. Rapidly disappearing forest resources urgently require the development of new techniques that can be used at the regional scale, especially in diverse areas of rapid economic and infrastructural development.
The most fundamental element of new techniques for tree diversity assessment has to be their applicability to making assessments over large areas. Using the rapidly developing data sources from aerial and satellite imagery for this purpose means that botanists need to find, in the first instance, a botanical basis for tree identification from individual crowns. From this initial information, aerial keys can be developed for taxonomic identification from imagery of particular scales and characteristics.
1.7 The outline of the thesis
This thesis is split into seven chapters, as described briefly below:
1. Chapter 1: Introduction and aims
2. Chapter 2: Literature review
3. Chapter 3: Research strategy and methods
32 4. Chapter 4: Developing and testing crown properties and signatures
for key taxa
5. Chapter 5: The spatial patterns in the distribution of key taxa
6. Chapter 6: Tree distribution of key taxa in relation to terrain
7. Chapter 7: Conclusions and future work
1.7.1 Chapter 1. Introduction and research goals
This chapter contains a short introduction, the research question, research goals, a hypothesis, overall aim, objectives and finally the rationale.
1.7.2 Chapter 2. Literature review
A literature review is given in this section, guiding the reader from historical to traditional and advanced identification techniques. As part of the botanical framework, some basic principles about plant morphology are also explained, with reference made to key literature for understanding aerial approaches in relation to tree identification. Then, a review concerning what is known on the spatial distributions of trees in tropical lowland forest is carried out. Finally, the effect that terrain characteristics or environmental factors can have on the spatial distribution of taxa at the landscape scale is discussed.
1.7.3 Chapter 3. Research strategy and methods
This chapter describes and illustrates the research strategy and methodology, from field data collection to application of the aerial identification technique.
Defining a reliable meaning of crown identification is explored through the
33 statistical analysis of the data collected. The process of developing subjective manual identification keys and objective signatures for crown identification is also explained. Finally, consideration is given to whether crown identification can be applied to mapping trees, with specific emphasis on correlations between spatial distribution of key taxa and environmental variables like slope, elevation and aspect.
1.7.4 Chapter 4. Developing and testing crown properties and signatures for key taxa
Chapter 4 presents the first part of the research results, including subjective crown measurements and quantification for individual trees. The analyses are used as a basis for understanding the development of the key and how properties change within and between taxa. In short, this chapter establishes basic and testable identification rules for separating the taxa in a reliable way, using data collected during fieldwork. The final step is to develop manual, visual tree identification keys at the family and the genera level, starting with the definition of hierarchical categories. The identification key validation test results are also analysed as part of this chapter.
1.7.5 Chapter 5. The spatial patterns in the distribution of key taxa
Chapter 5 covers mapping the botanical families, genus or species that help to understand the extent to which the taxa are spread out or clustered spatially across TBS.
34 1.7.6 Chapter 6. Tree distribution of key taxa in relation to terrain
Tree taxa distribution was mapped for TBS in relation to terrain characteristics, to identify the controls on tree distribution. Statistical tests were made in order to quantify the significance between spatial distribution patterns and landscape properties, if applied.
1.6.7 Chapter 7. Conclusions and future work
Final conclusions, limitations met and proposals for further research.
35 CHAPTER 2: LITERATURE REVIEW
The aim of this chapter is to explore the basis and implications of translating traditional taxonomy into aerial taxonomic identification on the basis of observable crown properties using high-resolution aerial images. The topics explored throughout the review are divided into eight main sections: we start with a discussion of floristic composition in a lowland tropical rain forest and the need for and difficulty of landscape scale assessment of tree diversity distributions. This is followed by a review of conventional taxonomic inventory, a discussion of alternative approaches to diversity assessment. And then we begin by reiterating the importance of tree diversity as a surrogate for biodiversity in general and the importance of obtaining landscape scale information on biodiversity for better understanding of how it is generated and maintained but also for the practical purposes of prioritisation of inventory and conservation. Finally we examine what is known concerning the spatial distribution of trees in lowland rainforest and the role of terrain characteristics on it.
2.1 The challenge of arboreal inventory in high diversity environments
Since this thesis is focused on the Ecuadorian Amazon, we will start with a review of the floristic composition of that region. Floristic variation in
Amazonian lowland forest is still poorly understood (Tuomisto et al., 2003) partly because of the very large number of species encountered which are taxonomically difficult to classify, making inventories time consuming and
36 demanding. South American rainforest floristic surveys have been carried out by several botanists (Condit et al., 1996; Condit et al., 2002; Fosberg, 1950; Gentry,
1990; Gentry, 1995; Grubb, 1963; Grubb and Withmore, 1966; Valencia et al.,
1994) but it has usually not been possible to define neither a precise number of species nor the spatial distribution patterns of particular taxa or the floristic composition, at the landscape scale.
2.1.1 Lowland forest tree composition
According to Thorne’s estimate (1992), there are 199,500 plant species found across the world, among which 10,900 genera were reported in the neotropics, distributed across 376 dicotyledonous families. Of these genera, approximately
225 are native taxa unique to the neotropics. Within the neotropics, there are approximately 3500 dicotiledon genera and 64,300 species. The five most representative plant families in the neotropics in terms of composition are
Asteraceae, Orchidaceae, Poaceae, Fabaceae and Rubiaceae.
It is well known that there are current and historical climatic, edaphic and geological differences between the eastern and western Amazonian regions which affect diversity patterns not only in plants but also in other biological groups such as birds (Haffer, 1969; Prance, 1982). In addition, Gentry’s study demonstrated that the flora of western Amazonia is of more recent origin than that of eastern Amazonia (Gentry, 1990). Studies conducted by Terborgh &
Andresen (1998) confirmed that there is a trend of increasing compositional dissimilarity with distance between plots in the Amazonian tree communities on both terra firme and in inundated sites. The apparently uniform Amazonian forest
37 is characterised by its diverse and complex mixture of soil mosaics, which have been discussed as a parameter which drives variation in plant diversity (Gentry,
1988).
It is said that the floristic composition of different plant communities is consistent, at least at the family level (Gentry, 1988) in the Neotropics, for example, Fabaceae is the dominant family in virtually all neotropical and lowland African tropical humid forest. Gentry’s research represents the largest published database in the neotropical literature, summarising a total of 87 sites located in 25 countries including 10 locations in the Amazonian forest. Gentry also shows that in the neotropics, the same 11 families - Fabaceae, Lauraceae,
Annonaceae, Rubiaceae, Moraceae, Myristicaceae, Sapotaceae, Meliaceae,
Arecaceae, Euphorbiaceae and Bignoniaceae, contribute about half of the species richness of 0.1 ha samples of any lowland forest.
Fabaceae, Moraceae, Sapotaceae, Rubiaceae, Lauraceae and Chrysobalanaceae are the largest families within the Amazonian flora (Gentry, 1990). Fabaceae dominates the floristic composition in the Amazon region. The majority of the tree species in the neotropics are part of just six or seven families; these families vary spatially according to geographical gradients all over the neotropical rain forest (Gentry, 1990).
More specific to the eastern Amazon, Leguminosae appears as one of the most abundant families of the eastern Napo-Yasuni region (Valencia, 2004). This pattern of dominance is repeated in the samples taken at the study site for this
38 research in the Ecuadorian Amazon (Jarvis, 2005; Pitman, 2001), thus echoing
Gentry’s general observation on floristic patterns. Despite all the effort that has been made over the past few centuries, “large parts of the tropical flora remain chronically understudied” (Prance, 2000). After reviewing the published literature it is clear that there is a research gap concerning the spatial distribution of taxa at landscape scale since almost all of the existing work has been conducted within plots of various sizes.
2.1.2 Floristic composition analysis at the landscape scale
In addition to the floristic composition and the number of species in an area, there are two significant aspects to be mentioned: (1) it is important to quantify and discover how composition changes between different locations within a region (β-diversity), a notion central to the theories of species diversity maintenance advocated by Condit et al., (2000). (2), lack of efficient assessment techniques and quantifiable methodologies is a factor influencing the slow advance of the rain forest floristic exploration as mentioned by Phillips et al,
(2003b).
An attempt to conduct research at broader spatial scales is an initiative called
RAINFOR. It is an Amazonian Forest Inventory Network project implemented in
2002, which is trying to develop a trans-national network of permanent sample plots around the Amazon (Phillips and Baker, 2002). They are attempting to interpolate databases between plots located in different regions within
Amazonian ecosystems. After four years of working on diverse issues such as forest biomass, forest structure, forest productivity and tree diversity across
39 Amazonia, this project has produced 18 scientific publications, with only one article with regard to flora inventories itself. This article (Phillips et al., 2003b) emphasises floristic assessments using plots. One of the relevant findings is that narrow rectangular transects -0.1 ha “are more efficient than plots in terms of floristic data gained per effort invested”.
Although positive findings were reported about floristic composition at the local and regional scale, no conclusive results were reported about changes of composition at landscape scale or variation between sites. Local scale refers to studies conducted in a single location, for example, sampling ten sub-plots of 0.1 ha each (Gentry, 1986). Sometimes the same studies are repeated in similar regions but in different locations, which can be understood as the regional scale.
Less common is to find studies that are conducted within a location but covering larger ranges of forest, which can be considered as the landscape scale. Large plots (between 1 and 50 ha) can be considered borderline between the local and landscape scale. In our case, the coverage of the aerial photography sampling area is about 200 ha, if contextualised within the Amazon forests, it is still small but nevertheless covers larger extensions than most of the reported literature. As a conclusion, this kind of comparative studies are useful to rethink what sort of techniques should be implemented for Amazonian assessments.
2.2 Approaches to diversity assessment
How can aerial techniques contribute to routine taxonomic identification in areas of high diversity? Although taxonomy research is contributing significantly to
40 the discovery of species, there is still an information gap to be explored at higher taxonomical levels (families, genera) with new approaches like aerial identification. However, there are some key issues to consider in this move towards aerial identification. A move away from taxonomic species-based identification might mean a move towards:
1) A higher taxa approach using genera or families or a focus on taxonomic
diversity.
2) A focus on structural diversity rather than species diversity.
3) A focus on plant functional types.
4) A focus on conservation value rather than diversity per se. This could
mean a move towards assessment of endemism and endangerment.
The key question, which must guide the measurement of biodiversity used in conservation-focused studies, is which measure is closest to the value of biodiversity (to humans and their environment). Debate still rages about the true value of biodiversity (more information in section 2.2.4) so this thesis focuses on traditional measures (taxonomic diversity) as opposed to less developed ones such as functional groups. Other measures may be closer to the true value of biodiversity to humans but taxonomic diversity is the best accepted and the one for which most data are currently available.
It is advisable to adopt a combination between taxonomic diversity from the higher taxa approach and finding plant functional types. Higher taxa are the elements that add value for biodiversity. The advantage of this strategy lies in understanding local relationships between taxonomic diversity and
41 environmental factors in order to discover functional groups that show correlations between terrain controls and ecological groups which must be key for biodiversity assessments (beta diversity) and therefore local conservation mapping (of e.g. individual trees).
This approach might be applied to a variety of studies such as assemblage and stability of communities, successional changes, detection and prediction of response to environmental changes at several scales, etc.
2.2.1 Higher-taxa
The main advantage of this higher taxa approach is that it saves time and money in terms of the identification effort, as the data collected reflect similar biodiversity elements as do traditional methods. It has been argued that investing effort in researching compositional variation of flora between regions rather than species richness in particular areas is potentially the most efficient approach
(Gaston and Williams, 1993), however it has not yet been fully explored. In the environmental sciences, recognising higher taxa patterns could be useful or even crucial for studies of conservation, diversity, community ecology, ecosystem functioning, biogeography, etc. Gaston and Williams (1993) have claimed that efforts would not have to be directed toward differentiating species.
According to Williams and Gaston (1994), one possible approach to higher taxon richness is a ‘top-down’ taxonomic approach, in which the diversity of different areas may be compared using measures based on the number of higher taxa present in each”. Williams et al., (1997), in the Biodiversity and WorldMap
42 project hosted at the London Natural History Museum, found that several studies support the notion of a relationship between the numbers of higher taxa, such as families, and the number of species. Higher genus or family diversity may be a good surrogate for higher species diversity.
From the conservation point of view, questions have been asked about hotspots in terms of higher taxa assessments. Hotspot analyses are focused on species, however they do not disregard higher taxa assessments. Terborgh & Andresen
(1998) have proven the existence of floristic pattern variability across
Amazonian regions, arguing that different floras were found on the eastern or western geographical axes. This kind of higher taxa study is urgent and should be a priority to explore issues surrounding the production of large area vegetation maps, as mentioned by Pitman et al., (2001).
2.2.2 Structural diversity
Structural diversity is different from functional types because it only considers the shape or form of a single object under analysis (i.e ovate and rounded leaves).
On the other hand, functional types include environmental factors as a relevant criterion. For example Schimper (1903) recognised convergence between plant form and function between vegetation types from geographically different, but climatically similar areas.
It is only with recent advances in the data grain and spectral detail of remotely sensed imagery that analysis has become feasible at the level of individual crowns rather than aggregate canopies or forest structure. Verheyden et al.,
43 (2002) has stated that understanding “fine-scale heterogeneity over large geographical areas is critical for improving analysis in all areas of earth science” though most studies to date have concentrated on scales above that of the individual crown.
Birnbaum (2001) has recently commented on the canopy level, collecting structural data taken from ground surveys using an optical telemeter. It was found that the canopy structure appeared to be an important attribute for identifying vegetation assemblages at the community scale. At the plot scale, he found a variation in convexities and concavities within a group of trees. In addition, Bongers (2001) has observed that individual tree crowns can be detected in remotely sensed images when they are large compared to the spatial resolution of the image, and when they are part of the upper canopy, preferably of emergent trees.
Although some studies have been carried out at the individual crown level, these are few and often fail to realise the potential. Verheyden et al., (2002) implemented an aerial photography visual interpretation method for monitoring and mapping mangrove vegetation dynamics. It was found that tree crown features are especially effective for recognising mangrove species (Avicennia marina, Rhizophora) and mixed forest composition (Excoecaria agallocha,
Bruguiera and Lumnitzera); the texture, form and size of the individual crowns were the main characteristics used to split trees to the generic level.
44 There is a lack of baseline research about crown structural properties. However,
Trichon’s approach (2001), using a manual and visual description of tropical forest tree crowns in French Guiana, is especially relevant to this research. She used aerial photography taken from a hot air balloon, at approximately 100 m above the canopy. A typology was compiled, which included specific descriptions of individual crowns. A similar approach appears to be the most appropriate in order to develop a crown typology for Amazonian trees. The structural diversity approach makes it possible to understand crown properties, which could be used for finding groups with similar characteristics.
2.2.3 Plant Functional types
Plant functional types are “sets of species showing similar responses to the environment and similar effects on ecosystems functioning” (Gitay and Noble,
1997). These groupings tend to be based on common attributes (growth form, life form system etc) rather than phylogenetic relationships (Diaz and Cabido,
1997; Lavorel, 1997). The groups of common attributes that correlate to environmental factors are considered ‘functional types’ because they may able to explain changes in ecosystem function (e.g., architecture of trees, plant morphology).
There are many examples of functional types, as shown in Table 1.
Author Comments Von Humboldt (1806) First recognised relationship between plant form and function. Developed classification based on growth form Grisebach (1872) Classification of 60 vegetative forms
45 correlated with climate Warming (1884; 1909) Classification based on simple life history features (e.g vegetation expansion power) Raunkiaer (1907; 1934) Life-forms system Keamey and Shantz (1912) Proposed four basic strategies of plants in arid regions in response to drought Braun-Blanquet (1928) Added further detail to the life-forms system Gimingham (1951) Growth forms system which also considered branching of stems Dansereau (1951) Classification system based on life-form, morphology, deciduousness and cover Kuchler (1967) Hierarchical classification, with initial division based on whether plant is woody or herbaceous. Lower order groups are based on life-forms, leaf characteristics and cover Mooney and Dunn (1970), Investigation of form-environment Mooney (1974) relationships in the context of convergent evolution Halle et al,. (1978) Models of tree architecture based on the underlying ‘blueprint’ for development rather than morphology at any given moment Box (1981) Developed global classification based on structural and phenological attributes Grime (1974; 1979a; 1979b) Plant strategy theory Noble and Slatyer (1980) ‘Vital attributes’ classification of plants on basis of life-history factors in relation to response to disturbance
Table 1 A chronological summary of the development of plant functional types.
From Duckworth et al., (2000).
2.2.4 Conservation value
The idea of approaching forest assessments using aerial images is based on understanding crown taxonomical diversity, and then translating it into taxonomical keys. The technique could be applied to compare ground species richness surveys with aerial higher- taxon richness assessments, using crown structural assessments of higher taxa (family or genera) as surrogates for ground measured species richness.
46
Some of the practical approaches illustrated above are considered appropriate for adding value to biodiversity conservation (Humphries et al., 1995). Specifically,
“higher taxon” case studies, such as those made by Williams (1995), indicate that
“the idea behind such an approach is that mapping 1000 genera or families represents more diversity than mapping 1000 species and may incur little or no extra data-gathering cost”.
If a relationship can be demonstrated between higher taxon-richness and species richness, there will be a higher possibility of assessing overall biodiversity distribution across large regions at a lower cost. In addition, the process will be less time consuming and less sampling effort will be required (Williams and
Gaston, 1994). As evidence, they report that “family richness is a good predictor of species richness for a variety of groups and regions, including both British ferns and British butterflies among 10,000 km² grid squares, and North and
Central American bats among grid squares of 611,000 km ², as well as other groups”.
In conclusion, species as a diversity measure must be reconsidered before implementing biodiversity assessments. The principal merit of higher taxa approaches is that higher taxa may be a good representation of taxonomic diversity, and it is more easily measured from both ground based and aerial approaches and may be closer to the value of biodiversity (more information in section 2.2.1).
47 2.3 Conventional taxonomic inventory
This part of the literature review (from traditional taxonomy to new aerial identification approaches) is important because it makes a contrast between the ground-based plot assessment of tree diversity and the aerial approach. In general, it is essential to have an idea about the main challenges that a person involved in plant identification may experience. The other valuable reasons to mention these topics here is to provide basic information illustrating differences between the traditional approach and the aerial approach
2.3.1 Ground plot assessment
In addition to identification, field-sampling methods are the fundamental basis of diversity assessments. In general terms, most floristic studies have been carried out at the plot scale, using quadrants or transects as the unit of sampling. A recent article about sampling methods for forest diversity and dynamics plots in the tropics published by Phillips et al., (2003b), explained the advantages of small plots, namely that the sampling effort per day is minimised and the capture of species numbers is more efficient.
Within the context of tropical forest, experience has shown that the most appropriate method is also strongly affected by the purpose of the research
(Phillips and Raven, 1997). For example, if researchers wish to monitor or repeatedly census trees, large square plots may be considered the most accurate technique because each tree can be located and labelled in order to monitor the plant dynamics. This technique has been employed in the permanent plot in
48 Yasuni National Park, Ecuador, where individual trunk measurements have been taken each month over the past 10 years.
To describe and discover forest composition or the number of different species in a short time, rapid assessment approaches would be the preferred technique, using rectangle shaped plots or ‘transects’. Since the 1980s, the trend in fieldwork has moved toward rapid surveys of plant diversity (Phillips and Raven,
1997). Small sample plots are frequently used for rapid assessment of biodiversity in tropical rainforests. A particularly good example of a rapid technique is the floristic inventory method described by Gentry (1982), where the sampling technique includes only plants > 2.5 cm in diameter in 0.1 ha, over 2 x
50 m belt transects with parallel spatial configuration.
Gentry’s (1982) ground-breaking data on 25 tropical forest countries applied the same rapid assessment method with rectangular plots for measuring species richness of the local flora. His rainforest characterisation carried out in the Chocó region of western Colombia found one of the highest concentrations of species
(258) in 0.1 ha across the South American tropics (Gentry, 1986).
Methods that are both capable of measuring tree species richness and which consume less time, effort and resources are still to be defined, as plot studies are only a partial solution to this (Phillips et al., 2003b). Another standard method, which has been implemented by many botanists in neotropical locations to gain quantitative floristic data, involves a single census of all stems > 10 cm diameter in an area of 1 hectare, (Campbell, 1994; Vasquez and Phillips, 2000).
49 According to Phillips et al., (2003b), 1 ha samples on average record more species than 0.1 ha studies, and can therefore be seen as a more accurate representation of local species richness.
The current trend in species measurement is to replace small-scale sampling of
Forest Dynamics Plots (FDP) with large-scale projects, such as that undertaken in Plot 16, Barro Colorado Island on Gatun Lake, Panama (Burslem et al., 2001).
This 50 ha permanent tree plot was established in 1980 and a number of censuses have been carried out there over the past 20 years. The method used was to identify, tag and map every tree with stems greater than 0.1 cm diameter at breast height. In addition to Plot 16, the project and collaborators have established FDP sites in more than 13 countries.
Although the forest dynamics project mentioned by Condit et al., (1996) could be considered the largest forest dynamics initiative ever carried out, it has not been able to measure more than a small portion of the forest tree species richness within the tropics. On the other hand, it gives very valuable measurements for identifying tree spatial distribution patterns within the plots and for comparing between plots located at different locations but with similar environmental conditions, as demonstrated by Condit et al., (2000). Such data are useful to better understand the properties and processes that control the generation and maintenance of species richness in tropical tree communities rather than for measuring diversity itself. Many other similar studies are now underway, such as the Yasuni National Park 50 ha plot Valencia et al., (2004) located in the Napo region in Amazonian Ecuador, where temporal dynamics are also being analysed.
50 According to Tuomisto (1998), “the areas surrounding a few cities and biological stations are relatively well inventoried, while most of Amazonia still remains unknown in floristic terms”. This implies that the resources available are inadequate to meet the demands for the discrimination, description and routine identification of specimens of most taxa (Gaston and May, 1992). These large plot studies help provide the detail on alpha diversity in a few areas but provide little on beta diversity and spatial patterns at the landscape scale.
Most of the recent literature concentrates on forest structural measurements
(Lewis et al., 2004; Phillips et al., 2002; Phillips et al., 2005), tree demography
(Condit et al., 2006) , tree phenology (Carate, 2006) and climate change (Haley et al., 2004; Malhi and Phillips, 2004). Some studies have been done about tree diversity, forest composition and distribution using plots at the local or regional scale (Condit et al., 2002; Normand et al., 2006; Phillips et al., 2003a; Phillips et al., 2003b; Pitman, 2000; Pitman et al, 2001; Terborgh and Andresen, 1998;
Valencia et al., 2004). All those approaches have been used more often for answering questions like:
What is the minimum sampling area?
How many species are there in 1 ha?
What floristic composition is present in a plot?
What is the taxonomic dominance over a set of plots?
The opportunity to look at the forest from the air makes it possible to approach new frontiers and questions, which traditional plot sampling methods, cannot
51 fully answer yet. Some of the interesting scientific questions to be addressed when aerial identification techniques can be used are:
To what extent can aerial photography be used as a reliable tool for floristic composition assessments?
What are the controlling landscape properties affecting tree distribution?
Which is the minimum landscape scale for Amazonian forest tree composition diversity predictors?
How can tree aerial assessments be used as surrogates for predicting forest diversity?
Being optimistic, perhaps it is possible to answer some of the questions mentioned above. Apart from thinking about those questions, it might be even more relevant to find either suitable techniques or methods for up-scaling or bridging the gap between existing plot databases all over the tropics and new arising commercial technologies such as satellites or high-resolution aerial photography. Testable and quantifiable methods are required to measure those diversity elements at the landscape scale from aerial transects. Perhaps, the aerial imagery approach shall be more accessible and cheaper in the future than going back to remote forest places for tree re-counting or first-time monitoring.
It is important to take into account the limitations that aerial identification techniques involve. For example, one of the main disadvantages is related to what kind of trees can be observable from above. Usually, small and sub-canopy trees are the ones covered by the upper layer of the forest, which are not visible
52 using aerial photography, as a consequence any aerial survey underestimates the component of diversity which is found in the subcanopy flora (Bongers, 2001).
Another constraint is the fact that adapting new remote sensing technologies to aerial means of identification requires a serious validation process, which is normally very data demanding because all the difficulties related to geolocation and topographic aspects which makes the ground validation a very difficult stage, but not impossible so far. Overall, the combination of a reliable and quantifiable aerial identification method with commercially available imagery plus efficient objective crown measurement might provide a potential solution to scale up from plot to landscape scale. Of course, this considers that aerial identification would not be expected to replace the traditional identification methods, just to provide a complementary tool for inventories.
2.3.2 Plot and transect-based approaches
Aerial photography interpretation techniques have been significantly developed over the past 60 years and now form the basis of most forest management inventories (FAO, 1997).
Most botanists working in the Amazon region use the traditional ground-based identification system, as species identification keys are widely available in the botanical literature and practice, and are also considered more reliable compared with other techniques. New aerial and satellite technologies have been too expensive and technically complicated for taxonomic specialists located in neotropical regions. We know of no published keys for identification of tropical trees from the air. It could be projected that plant experts will adopt aerial
53 identification keys as soon as their identification accuracy can be proven and the technology for collection and analysis of the necessary imagery is within their reach. Such keys may not only be of use in identification and inventory but also in monitoring flowering, measuring the growth process, and assessing succession and other aspects of dynamics that are measurable from the crown.
After developing an appropriate tool, identifying an individual tree from a digital image is potentially much simpler than using traditional keys based on sample collections that botanists have used historically. This implies that it would be more efficient to train non-experts to use the digital keys, though the level of taxonomic detail achievable with such approaches is likely to be much less than that currently possible from traditional techniques. In this way the aerial approach sacrifices taxonomic detail for spatial detail, extent and rapidity of inventory. In many cases the extent, spatial detail and speed of inventory can be just as or more important than the taxonomic detail of inventory, especially in cases of conservation prioritisation and if we recognise taxonomic, structural or functional diversity (which can be more readily measured from the aerial approach) to be as useful as a surrogate for conservation value as species richness is.
In real terms, the implementation of aerial imagery as a tool for characterising tree diversity will only become widespread when museums and herbariums use aerial images to store, sort and identify specimens as well as accumulating dry samples in cupboards.
54 2.4 The Aerial approach
Aerial surveys are alternative techniques used to complement plot studies, which have been developed since aerial or satellite-based imaging systems have been able to observe upper canopy tree crowns. Many studies have used ground methods for assessing species richness within plots, some of which are examined above.
Estimating tree diversity from aerial surveys has not been the main objective in the last few decades. The development of techniques for classification and crown delineation has been the starting point for aerial surveys. It appears that there is more research on crown segmentation and automated crown extraction, but the focus has been lost on analysing structural crown complexity.
2.4.1 Crown separation
The first set of case studies are concerned with crown separation using mathematical techniques. Analyses of multispectral imagery were made for characterising forest mapping in a pine plantation in Canada (Leckie et al.,
2003a). 27 square plots 28 × 28 m (0.081 ha each) were established but only three used; these data were useful for counting trees per hectare and describing the type of forest canopy.
Brandtberg & Walter (1998) evaluated an algorithm for automated crown delineation on 43 sample plots (10 × 10 m) randomly selected from 86 plots on
24 different images. Infrared images were used for the analysis, and the scale
55 was approximately 1:2000 corresponding to 0.60 m in resolution. The technique used was based on detection of edges and spectral signatures in multi-scale imagery. They concluded that the proposed technique, which used a multiple scale algorithm to delineate individual tree crowns, could be implemented as a basic tool for forest surveys. The basis of the model was to calculate how crown contour changes from crown to crown according to the mean circle of curvature for each segment of the crown edges.
Leckie et al., (2003b) applied a new routine on a larger geographical scale, by implementing a tree crown delineation technique to characterise species composition and stems per hectare over rectangular transects (30 × 50 m long).
The resolution used was 0.60 m. Their analysis demonstrated that the average species identification error over 16 sites was 7.25% ranging from 3% to 13% for each stand. The imagery was acquired with a CASI “Compact Airborne
Spectrographic Imager” sensor in a twin-engined aircraft, during one flight at
535 m above ground level. Although some positive results were obtained using
0.6 m high-resolution imagery for a conifer stand, the overall outcome indicated that individual trees were isolated well, but at the individual level, species classification did not produce accurate results. Leckie’s method is called the valley technique, which “treats the spectral value of the imagery as topography with the shaded and darker areas representing valleys and bright pixels of the tree crown” (Leckie et al., 2003).
In another study, Leckie et al., (2003a) combined high-density lidar sensor data and multispectral imagery for individual tree crown analysis. They placed the
56 sensors in a pod beneath a helicopter, and achieved images with a resolution of
0.085 m from flights at 265 m above ground level. The imagery was acquired with a Kodak digital camera with 2008 x 3040 pixel array, and a Nikon 28-mm lens. This valley technique was very accurate for isolating and classifying crown shapes for five coastal coniferous species (Pseudotsuga menziesii, Abies grandis,
Abies amabilis, Thuja plicata and Tsuga heterophylla) and had 80-90% correspondence with the ground reference.
Gougeon (1999) has written that high spatial resolution imagery has enabled the identification and mapping of individual trees, or groups of trees. Analyses based on CASI imagery (high resolution images) were used to extract forest parameters on Vancouver Island, British Columbia (Leckie et al., 1999). Data were acquired from 20 × 20 m plots with a resolution of 0.7 m in eight spectral bands. The method was based on tree isolation determining crown closure, gap distribution and species identification. A tree delineation algorithm, the tailored thresholds method and band ratio filter techniques were used for the analysis. They found that landscape type (old growth or young hemlock) tree counts were sensitive to the methodology and that only mixed success was achieved with species classification of an old growth site. An average of 500-1600 stems per hectare were found on the immature site; the best correspondence occurred between 600 and 900 stems per hectare.
Erikson (2004) provides an excellent example of this approach. His research attempts the identification of common tree species in Sweden using radiometric and morphologic image measures (Erikson, 2004). The four species analysed
57 were: Norway spruce Piceas abies, Scots pine Pinus sylvestris, Birch Betula pubescens and Aspen Populus tremula. The image capture method used was an infrared aerial camera, installed in an aircraft at 600m above ground level.
Erickson took information on the colour as well as the shape of the segmented tree crown, and developed algorithms to measure the parameters. The technique is based on “segment examination for the measures one by one and if one measure becomes true, the segment is interpreted as that species”. The overall species classification result for these images was 77 %. This approach is an excellent example of current state of the art research on crown identification; however, more studies based on empirical data related to crowns must be carried out.
Within this framework, this research will be characterising crown composition, which is an essential element for determining future efforts in conservation for tree diversity between regions, as well as trying to cover landscape geographical scales. This means that we aim mainly to understand and characterise crown taxonomic differences at the higher taxa level (families or genera) and perhaps species but only if applicable, so providing basic information, which in the future could be extended over a regional scale. For instance, it could be used to measure the difference between the flora in two different sites or habitats.
2.4.2 Key case studies
The literature has shown that spatial and spectral resolution, altitude and camera parameters are the main conditions for studying trees from aerial images.
However, less effort has been invested in understanding the tree crown as a
58 structure composed by internal elements to be translated into taxonomical keys.
There is a lack of information about visual interpretation of crowns from the taxonomical point of view instead of as a whole object to be isolated.
There are relatively few references on tree taxonomical identification using aerial photography. Most of the following are descriptions of key case studies about plot scale analyses using aerial photography, except for the study made by
Verheyden et al., (2002), which is on a larger geographical scale: “transects”
(100 x 100 km). An early example of interpretation from aerial photography is provided by Myers (1982) and Sayn-Wittgenstein (1978) whose explored means of describing upper canopy tree crowns in tropical forests, developing terminologies based on structural characteristics of internal crown parts using stereoscopy of stereo images. Another author implemented aerial keys using landscape properties for land use and vegetation identification from aerial photographs in north-east Nigeria (Alford et al., 1974).
A detailed photographic aerial key was subsequently developed to identify the northern conifers of New England and the Maritime provinces in Canada
(Hilborn, 1978). This key was devised for use with panchromatic photography at scales from about 1:6000 to 1:16000 - which is equivalent to a resolution of 3 m.
As regards analysis at the canopy level, it shows that direct remote sensing of certain aspects of biodiversity are becoming increasingly feasible; and perhaps, for distinguishing species assemblages or even identifying species of individual trees (Turner, 2003). The finest grain found within the high-resolution multispectral airborne imagery studies are between 0.35 and 0.60 m on average.
59 Visual detection and interpretation on an individual tree basis has been conducted in the Petawawa Research Forest, Ontario, Canada, where a few common species were analysed (Leckie and Gougeon., 1998). The imagery was acquired with the MEIS II (Electro Optical Imaging Scanner), with flights by
Innotech Aviation Ltd at 360 m above the ground level. They achieved a resolution of 0.36 m, and species interpretation accuracy was in the order of 79-
90% for softwood species and down to 50-65% or less for hardwoods. The properties used were crown shape and size. The sampling was done on 24 ground plots (20 × 20 m) incorporating 600 trees and 17 species. Boreal and temperate species occur in a pure and mixed forest (Pinus resinosa, Pinus strobus, Pinus banksiana, Picea mariana, Thuya occidentalis, Larix larcina and
Betula papyrifera).
Most aerial analyses do not deal with many taxa but rather with low diversity as they are generally made for commercial plantations, probably because of the demanding and difficult tasks involved in the identification process. A different approach was explored in mapping mangrove species in the Daintree River estuary in North Queensland, Australia. The research examined the possibility of combining a high-spatial resolution scanner, (CASI), with the Airborne
Polarimetric Radar (2.5m resolution, AIRSAR) for mapping and monitoring mangrove estuaries (Held et al., 2003).
They found the AIRSAR useful for analysing structural mangrove classes and diversity across the estuary, and were able to identify the broad mangrove zones.
They found areas dominated by large and dense canopies, by Rhizophora at the
60 water’s edge, and further inland, by open-canopy Ceriops. Both CASI and
AIRSAR were able to distinguish different mangrove types with accuracies of between 60% and 71%, depending on the classification methodology used.
To date, little work on organizing the remotely sensed morphological characteristics of crowns into taxonomical keys has been carried out. Detailed classification criteria for individual upper crown layers in French Guiana were developed by Trichon (2001). Her research analysed the upper canopy crown using aerial photographic prints at a scale of 1:600, equivalent to an average resolution of 0.3 m, using stereoscopic techniques. The visual interpretation of the photographs was very clear for recognising structures within the crown.
Studies at the crown scale shows that aerial photography can be used for estimating and recognizing three mangrove forest trees – Excoecaria agallohca,
Lumnitzera recemosa and Rhizophora – (Verheyden et al., 2002). In order to assess species assemblages and composition, attributes such as grey values, texture form and crown size were used (Verheyden et al., 2002). The photographs originally had scales of 1:50,000 and 1:10,000 but were photographically enlarged to 1:10,000 and 1:5,000. The attributes for species identification used were grey values, texture (smooth or grainy), shape and size of the crown, density and shape of the canopy, which were useful for developing manual identification keys. Verheyden and colleagues found that none of the attributes were useful for the identification of individual species, only for genera so far (Figure 1).
61
Figure 1 Interpretation key for the mangroves of Galle, Sri Lanka (From
Verheyden et al, (2002).
In some cases, there was image overlap, which led to a loss of attributes. The edges of the aerial photographs made the visual identification unclear on those particular areas because of the border effect distortion. Crown structure was also used to split species according to crown size, height and colour. These parameters proved useful to separate assemblages within more homogeneous canopies. The results obtained proved the suitability of aerial photography for the identification of assemblages or supra crown categories; however, the information was not detailed enough for analysing very heterogeneous canopies.
More recent research conducted by Trichon and Julien (2006) showed that tree species identification on a large scale using aerial photography is becoming possible. They used 5 ha area as a training set for species description and the
62 validation sets (10 and 6,25 ha) to identify 12 taxa in French Guyana based on crown typology (structural criteria). Two photo interpreters identified 309 tree crowns in overall, with a good agreement in respect to identification. The overall identification average was high (87%).
The lack of research on translating crown characteristics into taxonomic classification and identification will be addressed in this thesis. The focus will therefore be on crown shape, size and texture, and image characteristics such as spatial resolution and extent. This study will not only discuss colour airborne imagery, but also images obtained from ground tethered airborne platforms (e.g. helium balloons) which are capable of taking data at much lower altitudes and thus with a much higher spatial resolution.
For images taken with a helium balloon, the average resolution is 0.1 m whereas airborne imagery have an average of 0.5 m resolution in this research. The balloon resolution depends on the elevation above the ground; for example, from
50 to 100 m, the resolution is between 0.1 and 0.12 m. These images are at a higher resolution than the current commercial satellites, which is advantageous for identifying trees.
Though much work has been carried out using multispectral techniques, the objective of this research is to produce techniques that can be used with fairly standard colour aerial photographic imagery which is much more readily available and cheap to acquire than the largely experimental multispectral airborne instruments. Moreover, there is a trade off between spatial and spectral
63 resolution so that instruments with high spectral resolution tend to be of low spatial resolution. Since higher spatial resolution is critical for taxonomic identification the focus is on simple imaging (photographic techniques) with data in a maximum of four bands (red, green, blue, infrared) but at a high spatial resolution.
2.4.3 Identification using binoculars
Distance identification is used in traditional botanical surveying as a complementary technique, and it takes place from the ground, viewing the leaves and reproductive organs using high power binoculars. Some of the difficulties in using binoculars are the great heights of most trees, occlusion by lower forest layers, background brightness from the tropical sun in the upper forest layer and the associated loss of colour and tone. The main advantage of the field binocular technique is that it can eliminate the need to climb trees to collect samples, which is hazardous, time-consuming and uncomfortable.
Binoculars will not provide all of the information used for identification in traditional approaches but it does provide information on (a) patterns of shapes and angles of the branches that can be observed (b) leaf sizes and shapes, which can be differentiated with the more conspicuous species (c) leaf types, when the parts are big enough to be recognised with the lens, for example, palmate leaves.
64 2.4.4 Use of botanical keys
A traditional identification key is based on following a path, which always has two options. If the person identifying uses a key with the right parameters, all they have to choose are the options that are most appropriate for the sample they are aiming to identify.
According to Leckie et al., (1998) any interpretation key for assisting taxonomic identification does not replace the user capabilities, but just has “a function of facilitating and maximising the interpreter aim”. Each detail taken in the field will help to produce a strong description and more opportunities for exploring the potential identification. After deciding the taxonomical features, botanists follow dichotomous botanical keys to confirm the identification.
A key is a simple tool to find the route to the name, or could be seen as an artificial device for finding the scientific name rapidly. A botanical key gives a set of choices to be made when identifying plant characteristics, and is a route that organizes the general characteristics or diagnostic characters of plants aimed at building sequential paths to arrive at a single identification objectively (Table
2).
1a. Bean round Garbanzo bean
1b. Bean elliptical or oblong Go to 2
2a. Bean white White northern
2b. Bean has dark pigments Go to 3
65
3a. Bean evenly pigmented Go to 4
3b. Bean pigmentation mottled Pinto bean
4a. Bean black Black bean
4b. Bean reddish-brown Kidney bean
Table 2 Descriptive example of a traditional dichotomous key (From: http://www.saskschools.ca/curr_content/biology20/unit3/unit3_mod1_les2.htm)
2.7.1 Basic concepts and methods of aerial photography
Conventional aerial photography is probably the best-known remote sensing platform, although satellite platforms have a better land coverage “extent”. Aerial photography covers less extent, but with much higher resolution and consequently can give considerably more information, allowing more detailed study even at the level of individual crowns.
2.7.1.1 Extent
The geographical area over which measurements are made, extent is defined as
“the size, geometry and orientation of the space over which a measurement is made in remotely sensed imagery” (Atkinson, 1993).
2.7.1.2 Resolution
Resolution is the precision used in measurement. The spatial resolution of imagery is one of the characteristics that greatly influence the capability of image
66 analysis to study crown properties. Resolution can be observed from how small or large the image pixel size is. A pixel can be defined as the “smallest resolved unit of a video image that has specific luminescence and colour” (Britannica
Concise Encyclopedia, 2006).
2.7.2 Automatic crown segmentation
Automated crown segmentation is not well developed yet, especially for complex and closed tropical forest canopies. Although manual segmentation is a time consuming task, it seems to have positive results for tropical tree crowns in the
Amazonian forest (Vimos-Calle, 2007).
Little information is available about automatic crown identification. In spite of advances in many related fields, there is no published literature on fully automated crown identification in natural forest systems. Most studies have worked on semi-automated processes at broader scales, but the literature shows that a great deal of effort is currently being put into crown detection and delineation (Brandtberg and Walter, 1998; Gougeon, 1995; Leckie, 2003b;
Pouliot et al, 2002) as a first step to individual measurement of morphological features and thus to identification. Most of the techniques mentioned above have contributed to developing delineation algorithms based on measuring and extracting data from the intensity or grey level depending on individual crown pixel features.
Another study by Wulder et al., (2004) compares the results of local maximum
(LM) filtering for individual tree identification for a 1m spatial resolution
67 airborne Multi-detector Electro-optical Imaging Sensor II (MEIS II) image and a
1m IKONOS image. LM filtering is a technique that has been adapted to detect individual trees based on the maximum number of pixels. The function of LM filtering is to detect the apex of coniferous crowns according to the maximum pixel number. When pines are analysed, they normally have a constant cone shape with a relatively well-defined feature, which may reflect specific grey pixel intensities according to each layer of the upper, middle or lower crown. The analysis shows that further work in developing LM techniques for IKONOS data is required, but a comparison of the accuracy data demonstrated that the method applied IKONOS achieved 85% of individual trees identified against 67% for application to MEIS II.
The body of research on semi-automated techniques (computing algorithms) highlights their key role in identifying trees from different perspectives such as isolating, counting, delineating and extracting crowns (Brandtberg, 2002;
Franklin, 2000; Gougeon, 1999; Gougeon, 1998; Gougeon., 1992; Gougeon. and
Leckie., 2001; Haralick, 1979; Leckie and Gougeon., 1998; Lederman et al.,
1986; Ravishankar Rao, 1996; Verheyden et al., 2002).
In contrast, there is very little literature on automated textural analyses for sub- crown property measurement. Most of the published references found have been developing the techniques of delineation, isolation, extraction and separation of individual crowns (Brandtberg and Walter., 1998; Gougeon, 1999; Gougeon,
1998; Gougeon, 1992; Gougeon, 1995; Leckie and Gougeon., 1998; Pouliot D.
A., 2002).
68 There is currently a research gap in automated identification from high-resolution aerial photography based on textural properties. Filling this gap could potentially allow the characterisation of tree crowns from the air, which could then be translated into the production of crown-based taxonomic keys. Identification techniques developed from empirical fieldwork data, multi-scale high-resolution imagery methods and computer-aided and automated identification combine to form a powerful package for forest inventories, conservation and management in important, but poorly known, ecosystems like the tropical lowland forests.
2.8 Main aspects affecting aerial crown identification
The source of data used for conducting this thesis is low altitude aerial photography taken from an aircraft. Naturally this technique does not produce the perfect imagery; however, the high quality of the images in terms of their spatial resolution is an advantage. When trees are observed from the air, a view composed of a nearly constant canopy is the first visual impression. It is a very heterogeneous and complex landscape, and there are of course ranges of factors that may affect crown identification, which are discussed in further detail below.
The following short review addresses some of the most relevant variables such as terrain variation, image geometry and georeferencing.
The main aspects that may affect aerial identifications can be summarised in three categories: terrain effects, imagery effects and nature. One of the artefacts that complicate the aerial ID is topography, particularly slope, curvature and slope position. The inclination of the topography creates an irregular canopy
69 surface that changes the visual appearances of the objects, in this case the crown surface. Flat and steep areas are also very influential but depending on the type of forest. For example, flooded zones are usually shallow in comparison to ridges, so a crown may look different if located on flat topography compared with an irregular curvature. Imagery characteristics can also cause problems, for example, the image geometry, which is basically the angle of the image with respect to the ground. When the image axis is not perpendicular to the surface, it loses verticality, which produces a distortion of the objects. Spatial resolution is the typical factor, which affects directly the possibility of identification. In our case, the aerial imagery is high-resolution, but resolution can vary slightly across the study area because of image distortion and variations in flight elevation. The last category is natural effects. The extremely high tree diversity and the vertical structure and tree crown phenology are the basic points affecting the crown ID.
The following review is just a short description of the most relevant natural aspects affecting the crown ID.
2.8.1 Light interaction with canopy
Apart from the diversity of tree composition found in the tropical forests, some of the most common aspects affecting aerial identification and spatial distribution in the landscape are: camera and view angle (Howard, 1970), spatial resolution
(Verheyden et al., 2002), type of imagery (Wulder, 1998), light conditions (Hu et al., 2000), spatial geometry (King, 1998), and atmospheric conditions.
70 2.8.1.1 Shadow effects
Shadows have been a regular obstacle for any analysis of aerial objects; for example the effect of shadows on man-made structures, such as buildings in cities (Irvin and David, 1989). Clearly, the rainforest canopy is not an exception.
Minimising shadowing for canopy aerial analysis is an ideal strategy. However, light conditions are truly variable when aerial surveys are conducted, especially in tropical weather conditions. Light interaction may be considered a complex phenomenon with a great influence on visual identification of tree crowns. In conclusion, shadowing is a problem for identification only when lack of illumination do not allow a clear image of the crowns.
2.8.1.2 Light penetration
Evidence of the correlation between light interaction and canopy structural features, shows that “foliage angle distribution is one of the most important components of canopy architecture, as it affects the penetration patterns of direct and diffuse light within the canopy as well as the leaf area distribution”
(Kuraiwa, 1968). Specific studies were conducted measuring the vertical distribution of foliage angle, leaf area and light penetration patterns (Utsugi,
1999). His results suggest that “the foliage angle distribution within the canopy is an important factor in the estimation of the absorption of diffuse radiation in the forest canopy”.
71 Another example of light penetration effects on canopy observation is the vertical forest structure, which has influence directly on light penetration patterns, as showed in Figure 2. It may be that forest structure is not entirely affecting light penetration. However, a different structure produces different light attenuation for each case (Lowman, 1986). Structures can differ significantly at the local scale (i.e varzea vs. terra firme) and at the regional scale.
Cool temperate rainforest Warm temperate rainforest
Sub-tropical rainforest
Figure 2 Profile diagram of three forest vertical strata in Australia (Lowman,
1986)
72
2.8.1.3 Tree phenology
The appearance of the rainforest’s upper canopy layer from the air might be controlled by the variation in forest structure on the ground. Historically, forest strata have been classified into vertical and horizontal layers. The first stratification method used was the famous profile diagram, which was applied for the first time in the beech wood downs in Sussex, England (Watt, 1924).
However, there are other factors like phenology that may affect aerial recognition of trees. Phenology is the study of timing of natural events, and is another of the many variables affecting crown identification. This thesis does not contemplate temporal analysis, but some aspects should be mentioned in order to illustrate the general basis for the temporal changes that take place in a forest canopy.
Crowns are photosynthetic organs and also play the role of the forest’s roof.
One of the main biological responses to phenology concerns how trees behave with respect to environmental and seasonal changes. Rainforest species generally have complex and different phenological patterns and cycles (Bawa and Filliph
1990), such as flowering, leaf changes and fruiting stages during the year.
Phenological changes seem to be related to spatial aspects (Augspurger, 1983;
Gentry, 1974; Newstrom et al., 1994). Several phenological studies have been conducted in the tropics (Ausgspurger, 1983; Bawa and Filliph, 1990; Gentry,
1974; Newstrom et al., 1994). A seasonal study recently conducted by Carate
73 (2006) demonstrated that phenological patterns in fifteen common tree species within a 1-ha plot in Yasuni National Park were diverse. She found three types of foliage cover patterns: evergreen, semi-deciduous and deciduous tree populations. Two of the three phenological cycles mentioned above might not be directly relevant to this analysis because this research is not a seasonal study.
Foliage changes might therefore be one of the most relevant phenological characteristics affecting visual identification.
2.8.1.4 Tree structure
Changes in tree structural parameters considerably affect the identification or - at least - recognition of trees from above. Asner (1998) provides evidence of aerial crown analysis showing that sun and sensor viewing geometry influence land surface reflectance and light properties, and consequently the diffusion for visual recognition.
Simple geometric studies such as work on the correlation between the hot-spot effect (peak in reflected radiation in the retroillumination direction) and leaf geometry for grass and crop canopies (Qin et al., 2002) are making considerable progress in the understanding of canopy complexity. The variables measured by
Qin and colleagues were leaf shape and size. They developed an algorithm in order to quantify the morphological differences of the features between two typical agricultural crops (wheat and corn). The main results suggest that geometric measurements are likely to be estimated using the hot-spot effect.
However, each agricultural crop shows different results because of the type of canopy and specific geometric forms.
74
On the basis of the review, it is understood that the main potential factors affecting the aerial identification are shadowing, topographical variation, image geometry, forest vertical structure, spatial resolution and the highly diverse tree composition. One of the main lessons learned through the literature review is that advanced technologies do not necessarily guarantee a successful identification process. As a consequence, an empirical approach based on the combination of high-resolution aerial imagery, visual/manual crown identification and terrain analysis is proposed here. Although our approach does not solve the whole problem, it is a contribution towards understanding baseline aspects about using aerial imagery for crown identification and their needs for developing practical applications such as mapping canopy/sub-canopy trees over large areas (i.e. spatial distribution) or making correlations between mapped taxa and environmental variables.
2.9 Spatial distribution of trees in Lowland rainforest
Understanding the spatial distribution of trees in LRF (Lowland Rain Forest) is central to understanding the factors that control diversity in ecological communities. The distribution of LRF trees in the landscape can be determined by many factors. However, the literature indicates three main controls: environmental heterogeneity (Basnet, 1992; Burnett et al., 1998; Clark et al.,
1995; Clark et al., 1998; Clark et al., 1999; Damschen et al., 2006; Grubb and
Witmore, 1966; Harms et al., 2001; Harner and Harper, 1976; Lowman, 1986;
Normand Signe et al., 2006; Palmer and Philip 1990; Phillips et al., 2003a; Pinto
75 et al., 2005; Valencia et al., 2004; Webb et al., 1999), dispersal ability and biotic interaction (Bodmer, 1990; Carate, 2006; Dalling et al., 1998; Defler and Defler.,
1996; Di Fiore, 2004; Ferreira, 2000; Fleming and Heithaus, 1981; Fragoso,
1997; Howe and Steven, 1979; Hubbell, 1979; Kirsten Silvius and Fragoso,
2002; Kirsten and Fragoso., 2003; Loiselle et al., 1996; Mack, 1997; Medellin,
1994; Olmos et al., 1999; Russo and Carol, 2004; Salm, 2005; Stevenson, 2000;
Suarez, 2006; Svenning and Wright, 2005; Wehncke et al., 2003; Wenny, 1999).
Environmental heterogeneity or the spatial distribution of abiotic factors, has considerable influence on a tree community’s spatial distribution, through the so- called “equilibrium theory”. This occurs through the development and existence of a variety of niches and also niche assembly that determines the presence of diversity as demonstrated by several authors (Clark et al., 1998; Hubbell and
Foster, 1986; Lieberman et al., 1985; Terborgh and Andresen, 1998; Tuomisto et al., 2003). In contrast, biotic ecological processes such as seed dispersal and polinisation lead to the development of spatial associations between individuals of the same species and thus exclusion of disconnect species. This fact is known as “dispersal assembly” where generalist individuals are predominant, the so called “non-equilibrium theory” (Condit, 2000; Condit et al., 2006; Janzen and
Liesner, 1980). Finally, there is also the so-called “null hypothesis”, as stated by numerous studies in TRF (Harms et al., 2001; Hubbell and Foster, 1986), where chance events play a significant role in the development and distribution of species diversity.
76 A non random distribution of trees in LRF is commonly reported in the neotropics (Condit, 2000). This fact is related to dispersal processes and niche variation theories (Brokaw and Busing, 2000; Plotkin et al., 2002; Plotkin et al.,
2000; Russo and Carol, 2004; Tabarelli and Peres, 2002; Terborgh and Andresen,
1998). Although there is evidence for and against the abiotic controls on biodiversity, the processes are not clearly understood. The explanation probably includes biotic and abiotic factors as mentioned previously in some studies
(Brokaw and Busing, 2000; Condit et al., 2002; Jarvis, 2005; Normand Signe et al., 2006).
2.10 Landscape variables controlling the spatial distribution of taxa
One of the big questions about tropical forest ecology is which abiotic variables control tree diversity and tree distributions in LRF. A conclusive answer cannot be provided. However, the literature reports that topographical features such as slope, slope position and elevation are the most influential variables (Harms et al., 2001; Phillips et al., 2003a; Pitman et al, 2001; Terborgh and Andresen,
1998; Vormisto et al., 2004). Edaphic conditions are another important control
(Clark, 1998a; Clark et al., 1995; Clark et al., 1998; Clark et al., 1999).
There have been some studies on the role of habitat association in controlling community diversity in the Amazon region (Condit et al., 2000; Condit et al.,
2002). Most of the available literature concerns Central America, with the consequence that relatively little is known about forests near the Yasuni National
Park in the eastern Amazon. However, two relevant case studies about ground
77 tree diversity in relation to terrain properties were found. These studies are discussed in this short review, which deals mainly with the specific geographical region where this research takes place. Reference is also made to other studies on the same topic but which are based in different geographical regions.
A general overview of the studies reveals that species distribution patterns and their relationship with the environment are not consistent between studies.
However, it does give some guidelines about what may be influencing the floral community composition and distribution in lowland forest. A case study conducted by Jarvis (2005) aimed to illustrate spatial species distribution in relation to terrain variability. He uses 10 plots (25 x 25m) within a heterogeneous lowland rain forest in TBS, eastern Ecuador as showed in Figure 3.
Figure 3 Geographical distribution of the 10 25 x 25 m plots established in
Tiputini Biodiversity Station (Jarvis 2005).
78
A significant species diversity variation was reported between plots with an average of 24 families and 35 genera per plot; when related to the distance from rivers, a steady correlation between species richness and distance was found to be significant. Non-significant correlation was reported between elevation and compositional similarity. No correlations were found between habitat associations at the landscape scale between plots, which means that no single family shows a favourite habitat association. Elevation had a significant correlation with composition at genera level, and species were less correlated.
Finally, the slope position shows a more significant correlation for areas located up-slope. In conclusion, “composition is not convincingly correlated to terrain characteristics, and the majority of variability remains to be explained” (Jarvis,
2005). Nevertheless, there were other factors expressing significant correlation with composition, such as toposcale (a measure of absolute exposure, closely related to curvature) and northness (a measure of the seasonality of solar radiation receipt). A study by Valencia et al (2004) showed a similar trend when a broader plot size scale was explored. Valencia sampled and mapped trees > 10 cm DBH over the past 10 years in a nearby 25 ha plot in Yasuni National Park
(Figure 4).
79
Figure 4 50 hectare plot located in Yasuni National Park, 1 km x 0.5 km
(Valencia et al, 2004)
Valencia and colleagues reported three main trends: 25% of the species are generalist, flat areas within the plot show a more variable composition, and vegetation patchiness is not related to topographic variation. In conclusion, topographic variation does not provide enough evidence to explain species variation, however it does create particular niches that show compositional differences.
Non-testable and inconclusive findings have been reported in order to explain how certain species are distributed and the relationship of terrain aspects with their spatial distribution. Another study was made to explore soil types and edaphic conditions in the Amazonian forest of Peru (Phillips et al, 2003). All plants with > 2.5 cm DBH and soil samples were collected within a 100 x 180 sampling grid, covering 1.8 ha of forest in total. Chemical and physical soil properties were analysed and correlated to two types of landscape units. There
80 were no species restricted to one single habitat (the analysis was done using topographic variables such as slope) and 40% of the floristic variation is attributed to measured environmental variation with a further 10% existing because of the spatial autocorrelation between taxa.
This chapter has outlined lowland rainforest tree composition based on traditional sampling techniques including technical information about the structural and morphological aspects of trees. I have also provided a baseline comparison between traditional taxonomic inventories and alternative new approaches such as aerial identification. Although ambiguous results have been reported in the literature, it is clear that environmental aspects play a relevant role for controlling and influencing the spatial distribution of trees in the
Amazonian landscape.
This thesis consequently applies aerial identification keys for mapping trees and all the resulting data are finally correlated to terrain characteristics through GIS to quantify the effect of environment on controlling - or otherwise- the spatial distribution of trees. Finally spatial distribution patterns are explored with the aim of understanding the implications of spatial autocorrelation or cluster aggregation on the spatial distribution of the key taxa in the Tiputini Biodiversity
Station.
81 2.11 Seed dispersal associated to biota
The idea of this review is to illustrate some basic examples about the potential influence that seed dispersal processes such as “seed shadows” and “seed rain” may have on the spatial distribution patterns of trees at landscape scale.
The distribution of seeds within the habitat occupied by the population is called
“seed rain” (Masaki et al., 1994). Apart from the distribution patterns, seeds can equally have different density (number of seeds per area) related to the distance from the seed source, which is called “seed shadow” (Harper, 1977). Seed shadow and rain are closely related to the diversification of plant communities in
Tropical rain forests (Willson, 1992). One of the main biotic causes to act on seed dispersal in rain forests are birds, bats and mammals. Abiotic factors such as wind and water can play also a key role in the distribution of seeds.
Understanding the relationship between seed dispersers and their dispersion patterns could help clarify the possible spatial distribution of seeds that may be influencing the spatial distribution of trees in the landscape. The next question would be to explore which are the main seed dispersers or vectors, as some authors call it.
The number of field studies designed to examine seed shadows have been relatively rare (Hoppes 1988, Kitajima and Augspurger 1989). The differences between seed shadows formed by animals or wind can vary markedly according to the tree species (Clark et al, 2005). For example, one of the conclusions in
Clark’s comparative study was that “animal dispersed species had longer mean
82 dispersal distances than wind dispersed species”. A more specific case is illustrated by Thomas et al, (1988) where seed shadows generated by birds and phyllostomid fruit bats around a single fruiting tree Muntingia calabura
(Elaeocarpaceae) in Guanacaste-Costa Rica are compared. Thomas et al., (1988) used seed collectors in riparian forest and savanna finding that “the bird- generated seed shadow was strongly skewed towards the open savanna, whereas the bat-generated shadow had a strong skew in the direction of the forest edge”.
Primates (monkeys) are also an important group related to seed dispersal for several tropical trees (Chapman 1995).
The number of seeds per area around a particular tree is not the only relevant parameter to understand the influence of seed distribution on the spatial distribution of trees in the landscape. The seed dispersing pattern itself is another source of interesting information related to dispersal processes. Some research conducted in highly diverse forest is available but insufficient so far. However, there is a place in Central America called Barro Colorado Island (BCI) which has a robust permanent plot database set up for about 20 years. One of the BCI studies aims to assess the seed dispersal patterns generated by the faced capuchin monkey, Cebus capucinus, particularly looking at the type of seeds and the distance which seeds are dispersed. Cebus capucinus consume 95 different fruits out of 240 species available and the mean dispersal distance of ingested seeds was 216 m (range 2 - 844 m). Additionally, track routes from one tree to the other one tend to follow straight lines (Wehncke et al, 2003).
83 Apart from primates, terrestrial mammals are also important seed dispersers. As they spend most of their time on the ground moving between trees, many seeds are excreted at ground level and rarely, if ever, collected in traps. The roles of the two species of Opossums in the seed dispersion of Cecropia obtusifolia
(Moraceae) were investigated in La Selva Lacondona (Chiapas, Mexico). The main results were that “most dispersal events consisted of few seeds dispersed short distances, and there never were events of many seeds dispersed long distances” (Medellin, 1994). The dispersal distance found by Medellin (1994) varied between 0 to 71 m for both species. The implication of the patterns generated by terrestrial mammals is that birds and bats are more likely to disperse (seeds) longer distances than small mammals such as opossums.
Seed rain is related to the contribution of seed dispersal across the landscape including several trees. In this case, the scale of analysis is larger as it implies robust field work strategies because of the spatial coverage. Temperate forests are ideal environments to explore this kind of event because the local flora is often less diverse compared to the tropical rain forest, making the understanding of the processes more simple than studies conducted in complex hyper-diverse forests.
Seed rain spatial patterns were sampled in a 27 ha hardwood forest across 14 km² in south-east Michigan. It was found that seeds dispersed by animals compared to those dispersed by wind showed different spatial limitations. The size of the seeds can influence the seed rain patterns. For example, trees with large seeds arrives in a lower percentage of seed traps than wind-dispersed seeds suggesting
84 that “seed dispersion patterns within temperate northern hardwood forests fragments were spatially restricted” (McEuen and Curran, 2004). Similar types of patterns have been reported at La Selva wet forest station in northeast Costa
Rica. Seed rain patterns were observed at five forest locations in tree fall gaps and understory sites. The main result was that seed rain volume of dominant plant families was more similar among habitat types than among forests locations
(Loiselle et al., 1996).
On the basis of these case studies it could be said that seed shadow and rain are likely to be related to biota, which may have an influence on the resulting spatial distribution of trees in the community. This observation is based on the hypothesis that distribution processes influence and play a crucial role in determining the structure and dynamics of plant populations and communities
(Janzen, 1970).
85
CHAPTER 3: RESEARCH STRATEGY AND METHODS
3.1 Introduction
One of the conclusions reached by the World Conservation Monitoring Centre on identifying centres of species diversity was to consider using other taxonomic groups such as genera or families as the basis of diversity measurements instead of species. It is important to clarify that this research shall not use species as the main taxonomical level or species richness as a measure of tree diversity.
Taxonomic crown diversity at higher-taxa (family or genera) level will be the main variable to be analysed since this is the level at which a knowledge of diversity is both more valuable at the landscape scale and also more attainable from aerial photography.
A global assessment that evaluates the overall crown appearance is conducted along side a step-by-step analysis, in which the crown properties are assessed one by one. Both analyses are subjective to some degree, but the difference is that in the overall assessment the crowns are evaluated visually and in the step-by-step analyses the crowns properties are assessed in a semi-objective manner.
The most important aspects to be measured from aerial photography are crown properties, which will be used for developing means of identification since the crown is the most distinctive and visible part of a tree from the perspective of aerial photography. Manual crown identification keys will be developed. These
86 keys will be applied to rapid identification and thus inventory over images covering much larger areas than is possible using ground, plot based techniques.
3.2 Overall research strategy
The method is based on developing manual keys (i.e. keys for use by a human analyst) for crown identification, and to attempt to define more objective crown property -based signatures for identifying each of the taxa studied with the manual keys. The strategy for collecting and analysing the necessary data to accomplish the aims of this thesis are outlined below:
1. To collect high spatial resolution aerial photography over lowland
tropical forest in the Ecuadorian Amazon (Objective 1 and Specific
Objective a).
2. To subjectively analyse crowns in this imagery from a botanist’s
perspective to identify crown features that may be used in taxonomic
separation and identification for different taxa (Objective 2 and
Specific Objective b).
3. To develop a preliminary key for tree identification from these upper
crowns structural characteristics (Objective 3) present in the imagery.
4. To apply this key to a number of crowns in the acquired imagery for
manual tree identification (Objective 3 and Specific Objective c)
5. To use the ground based plot data and roving field identifications to
validate the resulting identifications using traditional identification
techniques (Objective 4).
87 6. To develop and apply techniques to classify the qualitative crown
measures contained in the key in order to develop semi-objective
crown property signatures for each taxa identified on the ground
(Specific Objective c). On this basis to distinguish the most useful
crown properties for the identification of taxa and to analyse the
consistency of signature identification for particular taxa and their
variation within and between taxa.
7. To test the resulting key by having a range of users apply (using an
online key) it and to test their identifications for consistency between
users, common errors, and by comparison with ground-derived
identifications for the same trees (Objective 4).
8. To apply the final key for taxonomic characterisation of an extensive
area of existing aerial photographic imagery in Tiputini, Ecuador and
use the resulting distribution maps to better understand the spatial
pattern and distribution of particular taxa (Objective 5 and specific
objective e) and of taxonomic diversity relative to terrain
characteristics (Objective 6 and Specific Objective f).
Methodological strategy:
The aerial photography will be used to define a range of crown properties that can be used to separate and identify particular taxa. This will be achieved by identifying a range of crowns for each taxon from a sample that correspond with ground-identified specimens in the inventory and defining crown properties of these crowns.
88 These properties will be combined into a dichotomous key, designed to assist the user in the identification of taxa from crown imagery. The properties will be measured for a large sample of trees of known identification (based on ground survey) and an attempt will be made to understand which properties best separate particular taxa and whether particular taxa have specific crown -property signatures that could be used in more objective identification.
The online key will be made web-capable and used with a range of users in an experiment to (a) test the accuracy of user identifications of taxa using the key and (b) understand which properties have the most consistent measurement by users and which are more subjective and thus subject to variable interpretation between users.
3.3 Description of the field site
The fieldwork for this research is being conducted at Tiputini Biodiversity
Station (TBS) in the Amazonian rainforest (Figure 5). It is located on the equator
(0° 37’ S - 76° 10’ W), in Orellana Province, western Ecuador, within the
Amazonian region. The altitudinal variation in TBS ranges from 200 m.a.s.l to
270 m.a.s.l. In total, the reserve covers an area of approximately 2400 ha.
89
Figure 5 Geographical location TBS (From: Keizer, 2007)
TBS is situated within the buffer zone for Yasuni National Park, which is also a
UNESCO biosphere reserve (Myers et al., 2000). Yasuni has an average of 3,200 mm rainfall/year and the average annual temperature is 15° C (Pitman, 2000).
The geological parent material is young alluvium from the Andean range, which dates from the Tertiary period. The upland soils are reddish, acidic and are rich in iron and aluminium. 90% of the land area is terra firme forest, with 10% swamp and riverbank forest coverage (Pitman, 2000). The landscape is also characterised by its frequently bisected topography with small rivers and streams
(Figure 6)
90
Source: Herb Project Image Database, King’s College London. www.ambiotek.com/herb
Figure 6 River Tiputini around Tiputini Biodiversity Station (TBS), Ecuador
Yasuni has a diverse tree community with an average of 229 species > 10 cm
DBH (Diameter at Breast Height) registered in a 1 ha non-swamp tree plot
(Pitman, 2000). According to Pitman (2000), the forest could be described as
“species-rich moist forest with emergent species at 40-50 m and canopy trees around 30 m tall”.
The main woody plant families found in Yasuni are Fabaceae, Arecaceae,
Rubiaceae, Melastomataceae, Lauraceae, Annonaceae, Sapotaceae; reported by
Pitman (2000) and observed previously by Gentry (1988) in his study of the neotropical Amazonian lowland forest regions. In descending order of height from emergent to shrub plants, the most common species are Ceiba pentandra,
Parkia multijuga, Otoba glycicarpa, Pseudolmedia laevis, Iriartea deltoidea,
Matisia malacocalyx, Brownea grandiceps and Rinorea apiculata (Pitman,
2000).
91 The rainforest in the Yasuni National Park area is under constant pressure from settlers following the infrastructure built by oil companies and consequently, there is a great need to coordinate a conservation strategy for the region to protect its flora and fauna, which may be of significant economic value in the future. Currently, the main environmental threat is the opening of new roads and pipelines to exploit the oil reserves. TBS is particularly at risk as it is located in the northern part of the National Park, where oil exploration is slowly encroaching on the protected area from the Eden Yuturi fields.
3.4 Collection of Aerial photography
The technique for this research is based on low altitude image acquisition, which produces high spatial resolution images. The data were acquired at TBS in
Ecuador by the HERB project fieldwork team (Andy Jarvis, Carlos González,
Pablo Vimos, Mark Mulligan and Sophia Burke) on three expeditions undertaken between 2001 - 2003. Due to limitations in fuel capacity, and unexpected deviation from predetermined flight routes because of high winds, it was not possible to cover all of the TBS reserve area by airplane, however, image coverage of over 80% was achieved.
The aerial data strategy was based on taking images with two different techniques: a tethered helium balloon using a Nikon digital camera and a fixed wing aircraft platform with a Kodak digital camera. The cameras were calibrated at the full wide-angle setting, in the manual mode, with no image enhancement
92 and the auto focus on. All of the photos were taken in full colour (red, green, blue).
Ground tethered aerial photography (GTAP). Balloon images coverage is estimated at 10% of the whole reserve area, especially dense around the main river channel where tethered flight was safer and easier. For the balloon images, coverage was sacrificed for resolution as they show a much greater level of detail than the aerial imagery.
3.4.1 Data collection
The GTAP images (Figure 7) were taken during two visits at TBS at heights of
100 m to 250 m from the ground. As can be seen from Figure 18, the flights are strongly controlled by wind direction because the camera is very lightweight and the balloon not very aerodynamic. In order to access different areas of forest, the balloon was used at three contrasting locations: the plots, over the river and from the canopy tower.
Helium balloon NIKON 990 camera attached to the
balloon
Figure 7 Helium balloon with digital camera device
93 3.4.2 Instruments and technical specifications
A high-resolution Nikon Coolpix 990 digital camera was used with the tethered balloon system, using a 28mm lens and resolution of 3.3 mega-pixels. The image size is 2048 x 1536 pixels. The NIKON 990 set-up details are summarised in
Table 3.
Image code: dscn2462.jpg Focal length: f8.2mm (x1.0)
Camera: e990v1.1 Image adjustment: auto
Metering: matrix Sensitivity: auto
Mode: p White balance: auto
Shutter: 1/98sec Sharpness: auto
Aperture: f3.5 Dates: 02.08.2001 11:04
Exposure +/-: 0.0 Quality: full fine
Table 3 NIKON 990 digital camera parameters.
3.5 Description of the technique
The GTAP was directed by a system of wires (Figure 8). The balloon is filled with some 7 cubic metres of helium, and is capable of a maximum load of approximately 3kg. The photos were taken by means of a remote control servo attached to the camera platform. There is no form of propulsion so the balloon’s flight path depends on wind conditions.
94 The basic steps for image acquisition are outlined below and illustrated in Figure
19:
• An aluminium bar is installed at the base of the plastic balloon.
• A conventional digital camera is attached to the bar.
• A string line and nylon is attached as safety lines.
• The helium balloon is inflated, and at the same time, a remote control
system is activated in order to take the photo remotely.
Figure 8 GTAP method for helium balloon image acquisition from forest gaps
(From Jarvis, 2005)
This technique can be used only in open or elevated spaces, such as towers, roads, rivers and areas with canopy gaps, where trees have fallen. In the case of canopy gaps a secondary tether is used to avoid snagging on nearby trees.
95 3.5.1 Airborne Aerial Photography (AAP)
This approach used light fixed wing aircraft-based aerial photography to acquire lower-resolution but more extensive images for the whole of the TBS field site.
3.5.1.1 Data collection
The basic equipment is illustrated in Figure 9, with the camera used on the right hand side of the platform.
Figure 9 Aerial photography equipment.
A flight path was designed to acquire images at three elevations, producing images with a spatial resolution of approximately 30cm (1200m elevation), 21.4 cm (600m elevation) and 10cm pixel resolution (300m elevation) with the Kodak
DCS420 (Figure 10). It is important to mention that the images taken at 600m were the only imagery used for analysis in this thesis. Despite the better geographical coverage for the 1200m images, this set does not have as detailed a
96 spatial resolution as the 600 m imagery. The 600 m imagery represents a good compromise between coverage and detail.
Airplane KODAK DCS420
Figure 10 Frontal view of the camera used for aerial photography
The flight path was loaded as a background file in the Trimble ProXL GPS, and used to navigate during the flight. The planned and the actual flight paths are shown in Figure 11.
Reserve Region 600m swaths
load Flight path wn a do loaded into Dat Trimble Asset
Surveyor s th a w s m 0 0 6 1200m L ose altitude to 600m swaths
ach pro Deviation from ap 300m 0m Planned flight path 120 swath
load To airport wn a do Dat
Planned flight path Actual flight path
Figure 11 Flight route for aircraft surveys over study region at TBS, Ecuador
(From Jarvis, 2005)
97 3.5.1.2 Instruments and technical specifications
The procedure is fully described in Jarvis (2005) from which this outline is derived. AAP is not limited by equipment weight, so two cameras were used to ensure trouble free data capture and maximise the possibility of full coverage: a
Canon digital video camera fitted with an AF Nikkor 50 mm lens, and a Kodak
DCS420 professionally calibrated digital aerial photography camera, which takes
1524 x 1012 pixel images. The latter was attached to a frame rate generator to automatically take photos at a given time interval. The imagery is written to a 1
Gb capacity PCMCIA memory card, and requires a minimum of 4 seconds between photos to completely save the images to memory. At the flight height of
600m the imagery provides a spatial resolution of 21.4 cm and a spatial coverage of 524m x 348m. The camera holds a maximum of 201 images in its memory before they must be downloaded to a computer.
3.5.1.3 Georeferencing
Table 4 summarises the number of images acquired, how many of them were georeferenced, their approximate ground coverage (Jarvis, 2005).
Altitude of image Total number of Total number of images Average resolution Area covered by acquisition (metres images acquired georeferenced of images (cm) images (Ha) above ground level) 1200 60 33 33.6 1006 600 154 46 21.4 300 300 18 14 8.4 24 100-200 (balloon) 679 30 7.6 51
Table 4 Image acquisition and ground coverage
Jarvis (2005) and colleagues describe the process as follows:
98 “The aerial photos were georeferenced using GPS data through an iterative approach, starting with the 1200m imagery. First of all, in-flight GPS data and camera characteristics derived from the calibration were used to approximately georeference the images. The orientation of the image is calculated based on the change in position from the GPS for the second prior to and after image acquisition. The GPS height is used to calculate the spatial coverage, and geographic positions of the corner points are calculated, and used as control points in the georeferencing. Starting with the image closest to the accommodation cabins, identifiable features were selected in the crudely georeferenced image.
The approximate coordinates of each identified feature were entered into a
Garmin12 GPS unit, and this location then visited in the field. Once the precise point was found, the Trimble ProXL GPS unit was left to take a geographic position for at least 10 minutes. In each image at least 5 GPS points were taken, whenever possible covering the corners and a central point. ERDAS Imagine
(Leica, 2002) was used to georeference the images, using bilinear interpolation.
Once the first image was georeferenced accurately, adjoining images were georeferenced, firstly based on a crude stitching to the 33% overlap with the first image. This crudely georeferenced image was then used to locate features in the field, and the final georeferencing of the image was performed using bilinear interpolation of the GPS points. This “dispersive” method was used to georeference all 1200m images. The root mean square error was approximately
3-5 pixels (equivalent to 1-2m). Once the 1200m images had been georeferenced,
99 they were mosaiced together to form a single image of the study site. This mosaic was then used as a base map with which to georeference the 600m and
300m imagery, also using bi-linear interpolation. In this case common features were identified for control points, using at least 10 per image.”
3.5.1.4 TBS canopy forests dataset
The Herb project has had several researchers based in TBS. As a result of five years of research a series of datasets have been produced. The manual crown delineation process started in 2001 (delineated crowns were not used in this analyses) by Mark Mulligan and Mike Salazar. The following year Pablo Vimos started another stage of crown delineation because his thesis was aiming to characterise the forest canopy structure (Vimos Calle, 2007). There was later a
Masters dissertation about mapping palms (Cuervo, 2002). Afterwards the tendency went to explore more the individual crowns in terms of their internal crown structure Keizer (2007) and floristic composition by the author of this thesis.
3.6 Field data collection
Most of the existing tree inventory in the Amazon has been done at the plot scale
(see section 2.3.2 in chapter 2). As scaling up inventory from plot to landscape scale is one of the objectives for this thesis, the traditional plot sampling technique is used where data already exists in that form but the core data
100 collected for the thesis is collected outside of plot structures. The main practical reasons to decide to use a non-plot sampling strategy were:
• To cover large spatial extent and a range of microhabitats and taxa and
imagery coverage,
• To focus on individual crowns rather than input a considerable effort to
inventory a plot of which only a few trees will be visible from the air,
• Ensure effective sampling and tree location with respect to known trails
and other features identifiable in the imagery.
3.6.1 Sampling strategy
Three expeditions to TBS were conducted between September 2005 and June
2006. Each one had at least 3 weeks of full fieldwork activities. The first fieldtrip took place in October 2005, the second one happened between February and
March 2006 and the last trip was conducted between May and June 2006. A total of 804 crowns were located, identified and mapped from throughout the coverage of the 600 m imagery, from that total just 600 were used for the main analysis
(Figure 12).
101
Figure 12 Spatial distribution of the crowns mapped and collected during fieldwork in TBS. The red lines correspond to the network of paths and the dots represent each of the crowns located and identified during the fieldtrips (red for the first, green for the second and yellow for the final field visit). Red and green were imagery centred crown selection and yellow were field centred data collection.
3.6.2 Plots stratified by landscape
The strategy for choosing crowns for analysis and ground sampling is based on three main criteria:
1. Imagery centred crown selection and later field centred data collection
(See table 5). During the first data collection, an imagery centred strategy
was used (more information in section 3.6.2.1.), this means that tree
crowns to be collected during fieldwork were chosen previously from the
computer. Secondly, a field centred sampling strategy was applied (more
102 information in section 3.6.2.2.), which refers to crowns chosen from the
ground and then later matched with the existing imagery. From the
computer, the 600 m mosaic was used as a guide to choose the crowns to
be sampled on ground. ArcView shape files extension was applied to
produce the point collection maps. These maps were then matched with
the original TIF imagery for each section of the mosaic and then
individual images were printed out in order to have a collection map.
2. Stratified by landscape position as a combination of altitude, distance
from the river, topographic wetness index and slope position
3. Proximity to paths for easy access
Table 5 shows the number of individuals that were included within the sampling strategy and further analysis.
Table 5 Number of individual in sampling strategy
Altitude is a criterion that was clearly fulfilled because the paths and thus the fieldwork collections cover elevations from both the lower (200 m) and upper
(270 m) parts of the reserve. In order to fulfill criteria 2, 3 and 4, crowns were collected alongside the paths and also at pre-defined sampling units established by Jarvis (2005). The sampling units represent all possible combinations of a range of factors: a topographic elevation model (DEM) for elevation, upslope area indicative of soil wetness, proximity to river and slope position. According
103 to Jarvis (2005), “all permutations of these two factors -elevation or DEM and upslope- with four classes produced the 16 sampling units” (see text below table
7). The aim was to ensure that the ground collections match within most of the
“sampling units”. Table 6 shows the results of the 406 image centred collection as an example see how many individuals are represented within the sampling units. In this case, most of the cases were well represented within each sampling unit with the exception of four units that were not represented at all. The sampling units 9 and 12 had more than 30 individuals, while only 3 sampling units had less than 30 but more than 13 individuals within a unit.
DEM UPSOLE (M) 190-210 0 210-220 1-3 220-236 4-8 236-269 9-21+
Table 6 Number of individuals represented within the sampling units for the image centred collection case (top) and sampling units factors (bottom)
104 It was not possible to represent all of the sampling units present at TBS because of the lack of imagery over certain areas around TBS or because the areas covered by the units are small and thus to maximize the efficiency of collection, these small units were omitted. However, table 7 illustrates the number of times that a single unit is represented within each studied taxa.
Table 7 Representation of the sampling units per taxa
The red areas covering the image edges illustrate the sampling units that were not covered (4, 8, 12 and 16). It is estimated that those areas represent approximately
5% of the whole set of 600m imagery (Figure 13). The missing areas tend to be the highest altitudes, which are not covered by the 600 m imagery.
105
Figure 13 Missing sampling units (red areas) around TBS.
3.6.2.1 Imagery centred selection
A selection of crowns was made using the imagery already collected around
TBS. All of the crowns were visually selected (semi-randomly) [it means that no random point assignation technique was applied and mapped with ArcView and the individual crowns chosen were stored as shape files (point distribution maps).
During the first two expeditions, each of the crowns visually selected from the imagery was then located individually on the ground following a roving ground based collection plan, assisted by GPS. Most of the crowns validated on the ground during the first and second expedition belonged to the main dominant families (Fabaceae and Moraceae) in terms of floristic composition. This pilot result suggested that additional field centred data collection at different taxonomic levels was required in order to select a wider range of families (which could then subsequently be identified from the imagery).
106
3.6.2.2 Field centred data collection
Field centred data collection was conducted directly on the ground instead of by selecting crowns from the images. The purpose of the field centred method was to ensure that a statistically significant number of crowns were sampled in each genus and family. This method made it possible to gather data at different taxonomic levels (genera and species, as required; see Appendix 2 with the main morphological characteristics of the sampled families in TBS and also see
Appendix 4, which explains the criteria and fieldwork techniques used to identify the taxa during the field centred data collection), which is a key part of the research. See appendix 3 with a floristic list for several families that were plot based collections before this thesis started. The traditional ground taxonomic identification technique was applied to trees found near the collection points located during the first and second expedition. One of the most relevant criteria was to select taxa that were easy to recognise taxonomically in the field. Two palm species were selected mainly due to their great abundance in the local landscape, although they are also highly reliable for taxonomic identification.
The genera and species selected after the imagery centred selection were:
• Genera: Ceiba, Parkia, Guarea, Cecropia, Pourouma, Inga, Pouteria and
Otoba
• Palm species: Astrocaryum chambira and Iriartea deltoidea
The taxa and number of crowns sampled per family, genera and species are summarised in Table 8. A sample size of at least 60 individuals per genus was sampled in order to have sufficient statistical samples (see table 8). The only
107 exception was Sapotaceae (Pouteria), as it was found during fieldwork that this genus was less common than first thought.
Table 8 Total number of individuals per taxa collected in TBS
Bearing in mind the aim of this thesis, the sampling scheme fulfils two additional criteria: coverage of a wide range of taxa belonging to the most common genera and families on the basis of the floristic composition found previously, and a broad representation of terrain characteristics and sampling in areas with easy access.
3.6.3 Data collection
The field collection was conducted in four stages:
1. Finding the trees
2. Identifying the trees
3. Labeling the trees
4. Storing the information
108 3.6.3.1 Finding the trees
TBS path network maps were used to map the collection areas. The first step for finding the trees on the ground was to select visually (and digitize) a number of crowns in the image (imagery centred data collection) with ArcView, before going into the field. Geographical coordinates were extracted from each of the digitized crowns previously selected (imagery centred collection) to approximate the location of the sampling areas. The coordinates were transferred into a traditional Garmin 12XL GPS, which was used for tracking down the trees. The collection took place in sectors following the path network progressively further away from the camp (Figure 14).
14 13
15 12 16
11 10 9 1 8 8 20 7 22 8 1 2 17 21 18 3
4 23 19 24 6 5 25
Figure 14 Collection sectors (blue circles or rectangles) at TBS. The base camp is located within the rectangle number 21. Red and green were imagery centred crown selection.
109 A fieldwork book with a set of original colour aerial images (not georeferenced) corresponding to each of the collection sectors was printed out and used as the field guide map. The images were taken from the original photographic dataset in order to have a more precise and detailed view of each individual crown to be found. These images were mainly used as a visual support, alongside the set of the original georeferenced printed images, which were the main visual guide, as can be seen in Figure 14.
The GPS was used on approaching the collection sectors showed in Figure 14, but a more precise approach to finding the trees was conducted, as illustrated in
Figure 15. As well as visual assessment of location based on analysis of the printed image against the local areas images, a traditional compass and a measuring tape were used in the field to locate the trees within the collection sectors relative to objects that could be identified in the imagery. Before fieldwork for each sector a path of bearings and distances was identified connecting trees that needed sampling. In order to find the trees within the sectors, it was necessary to identify particular features on the image such as palms, gaps, dead trees or emergent large trees that were non-transient, conspicuous and easy to locate from the ground.
Such points were used as control points to ensure accurate connections between crowns on the image and trees on the ground. After a control point was established, the closest visible tree was located and used as the ‘starting point’.
From the starting point, an ArcView tool called “line length and azimuth” was
110 used to find out the distance and azimuth between A and B, as illustrated in figure 15, these figures were then used in the field.
C
Azimuth between A and B B A Distance Control point from A to B (m) Starting point Final point
Figure 15 Image centred data collection technique for mapping trees in
Amazonian rain forest, TBS, Ecuador
The bearing angle and distance obtained was used in the field for moving from the starting point to the following trees previously chosen and located on the image. As observed in Figure 15, trees A and B were situated near the starting point in order to have a kind of triangular position, which ensures more accuracy in locating the next tree to be sampled and labelled. In order to keep a straight line between the origin point and the target tree, an intermediate point was located using the azimuth. In other words, direction corrections were a routine field practice and were more carefully applied when the topography was irregular. The tree locating technique described above was applied just for the trees that were visually chosen from the imagery and needed to be located in the field (expeditions 1 and 2).
111 The second part of the strategy for finding the trees was a field centred data collection method in which the trails were walked and trees of particular taxa chosen in the field and then looked up in the imagery (expedition 3). In the field- centred method, the taxa to be collected were located directly on the ground, so only individual crowns near the collection sectors from one of the seven specific families were collected. After each tree was located, the individuals were labelled and taxonomically confirmed.
3.6.3.2 Identifying the taxa
The taxonomic identification process had two steps: first, the closest branches were observed carefully from the ground using binoculars. Secondly, if the tree was not visually identifiable, a catapult was used in order to collect leaves and branches for a more detailed confirmation of the botanical entity. The identification was conducted to family and genera level. However, two palms were identified to the species level because they can be reliably and easily recognised, on the ground and even from the air. For practical reasons, and also since our purpose does not require-species level identification, formal botanical museum specimens were not collected. However, pictures of individual leaves for most of the mapped trees were taken as part of the collection record. The taxonomic identification level was clearly achieved in the field with a high certainty.
112 3.6.3.3 Labelling the trees
Previous experience within the HERB project has proved that using metal tags nailed to the trunk is an effective way of labelling trees. All the trees mapped were numerically ordered and labelled using self constructed aluminium or copper tags.
A database including a total of 804 individuals was compiled, of which 58 genera were identified, distributed across 29 families. Taxonomic identification was made to the family level for 99% of the individuals, and to genus level for approximately 75%.
3.7 Analytical methodology
After each data collection period the following strategies for data analysis were applied and then used for the purposes of this thesis.
3.7.1 Developing a crown identification technique
A technique for identifying crowns using upper crown properties was developed, including the steps described below:
1. Confirmation of identifications and storing field data in an accessible
database (Specific objective a). This task was related to applying the
traditional techniques for conducting a successful taxonomical
identification. One of the activities to ensure the accessibility to data
was to store it in a simple folder organised alphabetically according to
113 each botanical family, which was later uploaded into the web-based
tool.
2. Identifying the crown property signatures for particular families and
their exclusivity to that family (Objective 1 and specific objective b).
After having organised the crowns in a simple dataset, the next step
was to observe, describe and classify each crown according to a set of
pre-established properties.
3. Quantifying and measuring predefined crown properties for key
families, genera and two palm species (Objective 2).
4. Developing a dichotomous key using these properties for
identification of key species, genera and families (Objective 3). A
dichotomous manual web page based key was established, once the
efficiency of the objective and subjective classification was
determined using a numerical test. A value was assigned to each taxa
on the basis of the objective and subjective measurements within or
between families or genera.
5. Improving the dichotomous key based on these analyses (Objective 3
and Specific objective c). The weaknesses previously identified in the
key were addressed to make it more effective in terms of taxa
separation. For example, as foliage texture was the most successful
property in terms of splitting the taxa, more categories were added
within this property.
6. Testing the key with a variety of users (Objective 4 and Specific
objective d).
114 3.7.2 Methodological strategy
3.7.2.1 Storing data
Individual TIF crown files for each sample were extracted manually using the software PhotoShop (Figure 16) from the imagery and were stored in folders separated into families and genera. Summaries of the field data associated with these crowns were then organised in an Excel spreadsheet.
Figure 16 Using Photoshop to make a visual dataset for each crown collected
3.7.3 Development of a visual interpretation key
A standard crown property description was produced derived from Trichon’s
(2001) terminology for crown properties for each of the crowns contained in the database. It is relevant to mention that Trichon’s criteria were developed for
French Guiana rain forest trees. Initially there were 6 crown properties and 18 classes (Table 9) but only 4 properties and their correspondent classes (crown
115 type, foliage texture, foliage continuity and crown shape) were finally used for the analyses because they were more frequently repeated within the whole sample. The frequency calculation (see section 3.8.1) was one of the criteria used to identify the more useful crown properties. The decision to choose the final properties that are relevant to differentiate them was a mixture of the frequency calculation and some of the Trichon’s terms and definitions. To use strictly the same Trichon’s terms would be unpractical due to notorious visual and compositional differences between the French Guiana and Amazonian canopy.
That is why there is a relative use of Trichon’s criteria plus an adapted interpretation to the Amazonian trees based on the frequency calculation.
Leaf Woody Crown Foliage Foliage Crown presence elements Type Texture Continuity Shape With leaves Branches Single Smooth Continuous Flat Without any Trunk Multiple Mottled Discontinuous Rounded leaves Some parts No visible Granular without leaves Smoky Grainy Dotted
Table 9 Properties used for describing the TBS tree crowns
The crown property terminology was defined as follows (these are illustrative for the main properties, and it has to be clarified that they are not necessarily a precise description because there is variability of properties between crowns)
3.7.3.1 Leaf presence
With leaves: trees fully covered by a upper layer of leaves
Without any leaves: trees without leaves on the upper crown layer
Some parts without leaves: trees with some parts of the crown without leaves
116
3.7.3.2 Woody elements
Branches: when branches are visible through the crown
Trunk: when apart from branches, some parts of the main branches (trunks) are visible through the crown
No-visible: when neither branches nor trunks are visible through the crown
3.7.3.3 Crown type
Single: a crown with a well-defined centre or without clear division within the crown (Figure 17).
Multiple: A crown has two or more divisions within the crown, each component resembling an individual crown (Figure 17). The definition for “Multiple” used here is a Tricho’s modification because branches are not the main criteria to say that divisions within the crown exits. The main reason to say that here is because of the presence of clumps (formed by foliage) resembling individual crowns. It is important to remember that our criteria are based on a hierarchical classification concept, top-down approach, which means that a crown can be multiple at a higher hierarchical level and dotted at the same time because of the fact that dotted occupy a lower level in the hierarchy, it would not be affecting the classification.
117
Figure 17 Multiple and single crown properties
3.7.3.4 Foliage texture
Smooth when the upper crown surface looks constant (Figure 18) and regarding
Flat, an almost horizontal surface.
Mottled when the upper crown surface is irregular but not clumped (Figure 18).
Figure 18 Smooth and mottled crown properties
118 Granular: repetitions of larger particles like clumps; these particles can be clusters of leaves or crownlets (Figure 19).
Smoky: repetition of leaves not compacted; opaque or light foliage with leaves that are not compacted (Figure 19).
Figure 19 Granular and smoky crown properties
Grainy: repetition of small leaves (Figure 20).
Dotted: when the foliage is light and the leaves are separated, appearing such as individual large leaf spots (Figure 20).
Note: A dotted crown cannot be classified as discontinuous because the foliage texture is not clumped. Discontinuity refers to irregularity in terms of crown architecture but not in the textural context.
119
Figure 20 Grainy and dotted crown properties
3.7.3.5 Foliage continuity
Continuous: foliage regularly distributed within the crown (Figure 21).
Discontinuous: foliage concentrated in some parts of the crown; branches are visible in other parts (Figure 21).
Figure 21 Continuous and discontinuous crown properties
120
3.7.3.6 Crown shape
Flat: an almost horizontal shape (Figure 22).
Rounded: a defined surface curvature (Figure 22).
Figure 22 Flat and rounded crown properties
Once the terminology was familiarised, the assignation of the properties was conducted crown by crown, and an interpretation key was created (Figure 23).
This key is a dichotomous hierarchical tree or ‘family tree’ built within a set of pre-established crown properties. This key in Figure 23 was just used as a guide to show to the reader how the properties are organised hierarchically. It does not have an identification purpose. Therefore, some terminology may not be mentioned because it is just an example with methodological purpose.
121
Figure 23 Interpretation key (find an A3 size version of this figure in appendix
6)
3.8 Analysing crown imagery
The first step in the analysis of the crown separation system was to conduct a global assessment based on the crown appearance. Secondly, a step-by-step analysis on the basis of identified crown properties was done. The global purpose was to see if crown properties identified as being globally distinct (by eye) correspond in any way to particular families, genera or species as identified by traditional means. The step-by step approach had two purposes (a) to identify
122 particular properties that are good for separating families, genera or species and
(b) to see if forming groups of properties could be used for identification of particular families, genera or species. These two approaches would be used to test whether step-by-step is better than a global approach at separating crowns on a family, genus and species level.
The objective visual separation had two purposes:
(a) To identify particular properties that are good or otherwise for separating
families, genera or species
(b) To see if particular families, genera or species have specific property
signatures that can be used in the identification
On the other hand, the subjective separation of crowns had another purpose:
(a) To see if crown types identified as being distinct subjectively (by eye)
correspond in any way to particular families, genera or species as
identified by traditional means
3.8.1 Development of a crown separation system
Two analyses were applied to the main crown properties. First, a simple frequency calculation was conducted in order to identify the most common crown properties (CP) and the combinations of them, which we term ‘crown property signatures’ (CPS).
123 A frequency measure was implemented in order to find out how many times that property was represented within the sample of properties. All the possible signatures were identified and the number of times that the same signature repeated within the sample of the sampled trees was counted.
Secondly, a simple measure of dominance was applied. The measure of dominance called exclusivity index was used for quantifying how exclusive a crown property/property signature or crown type is to any particular taxa. The index represents a measure of exclusivity, with values between 0 and 1, that indicate how many times the most frequent property signature for each taxon appeared in relation to all the signatures including all taxa. The closer the values to 1.0 the greater exclusivity which means that the property signature appears with higher frequency than the other property signatures. Values close to zero mean that the property signature was poorly represented in relation to all the signatures for that taxon.
Exclusivity-Index = Sd / N
Where, Sd- number of times that a property/property signature appears in
a family or genus
N- total number of times that the property/property signature was repeated
(Frequency) within the entire sample
124 3.8.2 Developing an online key
The purpose of this identification key is to achieve better identification accuracy and, at the same time, to discover the implications of using two different keys.
Key 1 has only a drawing illustrating the crown property and Key 2 has the drawing information and a picture of the crown (imagery). The basis of the identification task is that two options are contrasted, but only one final decision must be taken. The decisions made are used to calculate: (1) the percentage of correct identifications (2) the percentage of incorrect identifications. Those measurements of performance produce a quantified identification accuracy.
3.8.2.1 The concept of the key
Key respondents have to identify ten different crowns (taxa) using Key 1 and
Key 2 as shown in Figure 24.
Figure 24 Conceptual basis used for the identification: A. Key 1 on the top is the approach using a diagram, and B. Key 2 uses a real image in addition to the drawing and a diagram.
125 3.8.2.2 Instructions given to the key respondents
The basic information given to the people to explain how to use the key (in the case of remote contact) was using a general email and sometimes personal contact by text as shown and summarised in Table 10. In contrast, there were group sessions with University students mainly, in which every computer was set up with Google Earth version 4 and the Internet link to access the key. A short verbal explanation was given to the students regarding the basic aim to be achieved with the exercise.
126 Dear all,
We have prepared an initial database for distributing the data that we have collected at TBS over the years to others working there who might want to use it. That database which is integrated with Google Earth has been incorporated into an online key for tree identification from high-resolution aerial photography. The tree allows users to make identifications of 10 trees in the TBS imagery using series of questions. I would be very grateful if you would take a short time to identify 10 trees for me. This exercise contributes to my PhD projects, which aims to develop better keys for tree identification from the air.
Key instructions:
You can find instructions and access to the key and imagery at http://www.ambiotek.com/carlos/Templates/2.html
Your computer needs to have a copy of Google Earth version 4, which is available at http://earth.google.com/download-earth.html
I cannot identify which users have given particular answers but will be able to provide some general feedback to the group of users as a whole. PLEASE ALSO FORWARD TO OTHERS WHO MIGHT CONTRIBUTE.
Regards,
Carlos E. Gonzalez
Department of Geography,
King's College London
Table 10 Instruction emailed to the key respondents about how use the online key
3.8.2.3 Key respondents
One hundred people served as key respondents. The key users, which are called respondents, are dealing with unfamiliar natural images, such as tree crowns. The variety of user is wide because one of the expectations is that any person should be able to use the identification key, even if they are not related to environmental
127 issues. There were two groups of key users, from 100 users, 50 were university students and the other half were from various professions from around the world.
Regarding the other 50 users, there was a mixture of respondents within this group. Personal communications and comments were received from about 30 people and for the rest of the respondents it was not possible to identify their origin.
3.8.2.4 Crowns to be identified
The ten crowns to be identified (using the online key) are displayed in figure 25.
The original spatial resolution for all the crowns is approximately 21.4 cm, which is obtained from a flight of 600 m above the canopy as can be seen on Figure 25.
Figure 25 Images of the 10 crowns to be identified and their Latin names. From left to right: upper row from left to right Arecaceae- Iriartea deltoidea;
Arecaceae- Astrocayum chambira; Fabaceae- Inga; Fabaceae- Parkia; Moraceae-
Cecropia. Bottom row from left to right: Moraceae- Pourouma; Meliaceae-
Guarea; Myristicaceae- Otoba; Bombacaceae- Ceiba; Sapotaceae- Pouteria
128 3.8.2.5 Web-interface
Once the key was developed, it is converted to a series of html pages with embedded forms for the binary decision making and integrated with the aerial photography draped over the landscape in Google Earth (full printed version of the online key in appendix 6). The key is available at www.ambiotek.com/treeid.
Now, it remains as an example of the online key mechanism
To be more specific, the ordinary steps to conduct the tree identification is as follows:
1. Before starting the key, Google Earth version 4 must be installed in your
computer (this is available at http://earth.google.com/download-
earth.html).
2. To start the key, type the following address in the internet finder
(http://www.ambiotek.com/treeid)
3. This address will take you automatically to the initial page of the key as
can be seen on Figure 26. Note that this process is not immediate because
the aerial photography mosaic must be loaded and sometimes the
efficiency also depends on the type of Internet connection the user has
access to.
129
Figure 26 Access to key using GoogleEarth
4. Once the images are loaded. The photo mosaic and screen are displayed
in Google Earth. The crown to be identified appears as a red placemark.
Zoom in (as close as the image resolution allows you to see clearly the
canopy) on the place mark because under it there is the crown to be
identified. Clicking on the red placemark, displays the front page for
starting the key as shown in Figure 27.
Figure 27 Starting the key
130 5. Once the radio-button 1 is active, and when clicking on KEY ONE, this
html contains the first step of the key (Figure 28). At this time the user
must have the GoogleEarth imagery displayed and be looking at the
crown to be identified. Then, the crown identification start with this first
Html page. Let’s say that the user chose to click on the option called
PALM on figure 28.
Figure 28 First step for choosing if the crown looks like a palm or not
6. The next Html page to appear gives the final identification options. In this
particular case, the final identification can be either Iriartea deltoidea or
Astrocaryum chambira (Figure 29). Then, the key user must click on the
box and then to choose the sample is working on and finally to press
submit.
131
Figure 29 Final step for the identification of the palm tree
7. The same process has to be conducted for the other nine trees of KEY
ONE and repeated for the ten trees using KEY TWO.
8. Each time the users submit a response the system sends an email, which
indicates the question number and the response. There is currently no way
to identify the responses of particular users, though these could be useful
to add in future.
3.8.2.6 Key validation: identification accuracy
In order to test the key with multiple users, a series of users were identified from the forest ecology/botany community. The observers are asked to use the key to identify 20 crowns (ten with key one and the same ten with key two) that were
132 previously identified on ground. One crown was selected within each of the existing families making a total of 10 crowns per key. The criteria for selecting the key respondents is based on choosing a range of people that have been involved in some way with conservation issues, but also including some interpreters with no background in biological studies.
The results were stored automatically to an online database and organised in
Excel. Finally, the overall identification accuracy per taxa was calculated as follows:
Where,
OA = NC/ T
OA= Overall identification accuracy
NC = Number of crowns correctly identified per taxa
T = Total number of identified crowns
Error analyses were also carried out in order to identify at which point misidentifications occurred (i.e. which properties were consistently judged) and which showed inconsistencies between users. The incorrect identification accuracy percentage was calculated in a very similar way as the correct ID accuracy. On the basis of wrong identifications, taxa error matrices (section
4.2.2.2 in Chapter 4) were used in order to visualise the inconsistencies.
133 3.8.3 Mapping higher taxa based on the aerial identification key
The purpose of the following two sections is to examine the spatial distribution patterns of families/genera/species with respect to each other, and then with respect to landscape (terrain) properties.
A total of ten taxa were mapped. The main objective of this strategy was to produce point distribution maps. The basic research question to be addressed was: are the families clustered or dispersed and how are they located in relation to landscape?
One map for each of the following key taxa was produced: Arecaceae (Iriarthea deltoidea and Astrocayium chambira), Fabaceae (Inga and Parkia),
Bombacaceae (Ceiba), Moraceae (Cecropia and Pourouma), Meliaceae
(Guarea), Myristicaceae (Otoba) and Sapotaceae (Pouteria). The total number of individuals per taxa that were mapped are summarised in Table 11.
Genera (Family) Number of individuals Astrocaryum chambira (Arecaceae) 303 Iriartea deltoidea (Arecaceae) 740 Inga (Fabaceae) 355 Parkia (Fabaceae) 105 Cecropia (Morace) 317 Pourouma (Moraceae) 184 Ceiba (Bombacaceae) 61 Otoba (Myristicaceae) 105 Pouteria (Sapotaceae) 61 Guarea (Meliaceae) 102 TOTAL 2333
Table 11 Number of mapped taxa using the aerial identification key
134 The aerial identification key was used for producing distribution maps corresponding to areas that do not match the maps that already exist. The 600m georeferenced mosaic imagery was used for this purpose. The original TIF images corresponding to each mosaic area were used as a complementary guide to observe in detail some of the crown characteristics that were not recognised on the mosaic as a result of image distortion introduced in the process of georeferencing. The whole set of images were observed carefully one by one in order to identify crowns and also each identified crown was confirmed by looking at the dataset previously developed. All of the imagery was of sufficient quality to identify taxa and the taxa are distributed throughout the imagery.
Identification was possibly easier in some areas because of better image contrast where the imagery was shaded by clouds.
3.8.4 Spatial patterns
Once the maps per taxa were produced. In order to evaluate the spatial distribution patterns, four analyses were applied: the Moran’s I spatial autocorrelation index and a measure of spatial aggregation (Ripley’s-K), distance analysis, clustering analysis. Moran’s I was useful to identify the spatial distribution on the basis of spatial autocorrelation and Ripley’s-K was used to quantify the degree of spatial aggregation. The basic idea is to know whether or not the mapped taxa are clustered, which is approached with the Moran-I index as the first step. Then we explore how clustered the spatial distribution is, therefore the Ripley’s- K was applied. Then, a simple nearest distance analysis was conducted. The idea with this test is to identify if taxa tend to distribute in a
135 particular way, along lines for example. Finally, a clustering analysis was conducted.
3.8.4.1 Moran-I spatial autocorrelation index
The reason why Moran’s was used is because it is an index designed for the measurement of spatial autocorrelation. This index is a simple way of exploring spatial aggregation. It is based on comparison of the values of neighbouring areal units and describes spatial autocorrelation. For example, whether or not a taxon is aggregated, dispersed or randomly distributed. The values of Moran's I range from +1 meaning strong positive spatial autocorrelation, to 0 meaning a random pattern to -1 indicating strong negative spatial autocorrelation. Moran-I can be defined as
Where n is the number of zones or cells, wij is the spatial proximity of zones i
and j, x is the value in zone i and j, W is spatial proximity
3.8.4.2 The Ripley-K measure of aggregation
The second moment spatial aggregation measure called Ripley’s-K was applied
(Ripley, 1976). For all key taxa mentioned above, Ripley’s-K was calculated using the spatial analysis K-function tool in ArcView 3.2. Specifically, a multiscale analysis was done for K (d) for 7 values of distance d between 0 and
136 250 meters (20, 25, 50, 100, 200 and 250 m) for each taxa separately. In general, clustering increases K, whiles regularity decreases it. This measure is being well used for many purposes but specifically for analysis of spatial point patterns.
According to Plotkin et al., (2002) the K statistic “computes the number of conspecifics within a distance d from an individual, averaged over all individuals in the data set”, in our case it means a calculation of distance for each conspecific individual. It is crucial for us to explore the spatial distribution pattern for each for mapped taxa individually. Ripley’s K function is defined as
K (d) = λ -¹ E (number of extra events within a
distance d from an arbitrary event)
Where λ is the density (number per unit area) of events and E is events
3.8.4.3 Distance analysis
The analysis of the spatial patterns requires not only having a Moran’s I and K-
Ripley measure. It also requires mapping that enables visualisation to determine if the distributions cluster in groups or along lines. For better interpretation of the existing distribution maps, a raster grid of distance to the nearest neighbour of the same taxa was created for each taxa. The utility of these maps is that they more clearly pick out spatial patterns than the original point data do. Figure 30 shows just one example of the Cecropia map. Section 5.3.1 in Chapter 5 has visual illustrations for each taxa.
137
Figure 30 Example of a distance distribution map at TBS (meters)
3.8.4.4 Clustering analysis
Apart from the distance analysis, which tells us the basic patterns of distribution in the landscape. One of the objectives is to identify whether or not the taxa are clustered. Therefore, a clustering analysis was conducted separately for each of the taxa. As a result, ten maps were obtained. The computation consisted of using PCRASTER to calculate the overall number of individuals of a given taxa in a window of radius 125m, as can be seen one example for Cecropia in Figure
31. This measure of stem density for the taxon is then ratioed by the mean stem density for the entire region in order to provide a map where values >1 are areas with higher than average concentrations of individuals and areas with values <1 are areas with lower than average concentrations.
138
Figure 31 Example of the clustering patterns for Astrocaryum chambira at TBS
(stem density relative to the mean stem density for the taxon for the study area).
3.8.5 Taxa distribution in relation to terrain
Once the spatial distribution was determined, the resulting maps were correlated to terrain properties. The terrain features analysed were curvature, elevation, slope gradient, slope position, eastness, northness, solar radiation and soil wetness. For more information, section 6.2.1 in Chapter 6 has a detailed description and maps for each of the terrain characteristics. These terrain characteristics where chosen because according to the conclusions of Jarvis
(2005) it was found that these features “determine some of the essential resources for plants (light, water and temperature principally)”. And also have explained some variability in composition, diversity and structure, however, not
139 fully because some variation still remains unexplained. All this analysis contributes to achieve objective 6, which is basically to correlate the spatial distribution of key taxa in relation to terrain at TBS. The basic research question was:
• Is crown spatial distribution apparently clustered around particular terrain
features?
The main source of information for the DEM generation were the 1:50,000 cartography (sheet P111-D4 Zamora Yuturi), at the same time it was scanned and georeferenced using ArcView. Some features such as river and contours were digitised on screen, and the TOPOGRID command in Arc/info used to produce a
25 m spatial resolution DEM called TOPO DEM. According to Jarvis (2005) the
“TOPO DEM is hydrologically sound, and shows predominantly smooth networks of ridges and valleys, but fails to capture some of the micro-scale topographic variation observed on the ground”.
The following explanations about DEM production and extraction of terrain characteristics are based on Jarvis’s PhD thesis. Jarvis (2005) implemented the measure of each terrain feature from the TOPO DEM using different models. For example, the solar radiation receipt was calculated from the BENDUM hydrological model reported by Mulligan (1999). Aspect was converted to northness and eastness because they are relevant in terms of solar radiation receipt and exposure to wind. In the case of mean curvature, Jarvis calculated it using the method of moving windows of sizes 3, 5, 7, 9, 11 and 15 cells. The
140 Topmodel Wetness Index was calculated using PCRASTER, which uses a third order finite difference method for flow direction and accumulation calculation, to capture the main river channels mainly. Slope position was calculated using
ArcInfo Arc Macro Language (AML) (Jarvis, 2005).
3.8.5.1 Terrain maps
The original TOPO DEM was used to produce terrain maps for the section of
TBS where our mapped taxa are located. Eight terrain maps were obtained from the original TOPO DEM derived terrain variables used by Jarvis (2005) in TBS.
The calculations were carried out using PCRASTER.
3.8.5.2 Landscape analysis
The maps were used to produce an integration of the terrain properties average over a window with 125 m radius and ratioed to reflect difference from the map mean for each property in order to provide more meaningful data for comparison with the tree distributions. A complete description and interpretation of the landscape maps can be found in section 6.5 in Chapter 6.
The next step was to extract and assign terrain values from the grids for the locations occupied by each of the randomly chosen points and also the located taxon points. This procedure was done using the ArcView 3.2 ® grid analyst tool. The random points were taken only in the area of the terrain data occupied by aerial photography (i.e. the same area that mapped points could also occupy).
141 3.8.5.3 Statistical analysis of terrain variables
The aim of the Kolmogorov-Smirnov test was to identify statistical significance of any differences between the frequency distributions of the mapped and randomly chosen terrain characteristics. In other words, to describe to what extent the tree locations are showing a preference for a particular terrain characteristic. The statistical package called STATGRAPHICS Centurion XV.I was used to compute the Kolmogorov-Smirnov test (K-S). The non-parametric
K-S test is relevant because it compares the groups of data and determines whether or not they have statistically significant similar distributions. The K-S test is based on the maximum difference between the sample cumulative distribution (mapped) and the hypothesized cumulative distribution (random).
Concerning the frequency distribution analysis, the aim was to quantify the frequency values for each of the terrain variables and taxa contrasting mapped against random points. If the randomly chosen frequency distribution of terrain values was statistically similar to the mapped distribution then taxa are not showing a preference for particular terrain types. Finally, in order to quantify the mean variation between the two distributions, the difference of the random and mapped mean values was calculated as a fraction of the total variation of the terrain value. This approach indicates the degree of preference in elevation or slope gradient in relation to the range of those variables encountered in the measured area.
142 3.8.5.4 Diversity analysis
The final analysis consists of tree diversity measurement (related to richness).
Although diversity measurement is not the main objective of the thesis, this approach is complementary and valuable because one of the aspirations of implementing aerial identification and thus landscape-scale application is to feed into a better understanding of local hotspots for research, inventory and conservation or management purposes. This analysis identifies the areas with the greatest number of identified taxa within the mapped area. The diversity maps were produced for the ten mapped taxa. The calculation consists of integrating the number of all taxa windowed over 250 and 500 m. At the end, two “diversity maps” were produced, one for 250 m diameter and another one for 500m diameter windows. The results can be found in section 6.7 in Chapter 6.
The next chapter (4) presents the first analysis based on developing and testing crown properties and property signatures for separating the taxa and also to demonstrate the identification accuracy when a visual-manual crown identification key is used with a number of users.
143 CHAPTER 4: DEVELOPING AND TESTING CROWN PROPERTIES
AND SIGNATURES FOR KEY TAXA
4.1 INTRODUCTION
This chapter explores whether or not crown properties could be used for identification of trees from aerial photography. The aim is to understand key features of tree crowns that enable separation or identification by eye (General objective 1, 2, 3, 4 and specific objectives b, c and d).
The first step in this chapter is to identify and classify the most suitable crown properties (CP) and crown property signatures (CPS) for separation of taxa, and subsequently to examine which particular CP and CPS are effective or inefficient for separating particular families or genera. Once the best properties have been chosen, the plan is to create a simple manual and visual crown identification key.
The last part of the chapter was to make the identification key available through an online interface in Google Earth, in order to test the accuracy of identification using the key with a number of users and obtain feedback concerning which
CPSs are objective and repeatable between users and which not.
Little research has been conducted about manual and visual aerial tree identification in tropical rainforest; the main studies can be found in just a few articles (Myers and Benson, 1981; Myers, 1982; Sayn - Wittgenstein. et al.,
1978; Trichon, 2001; Trichon and Julien, 2006). Some authors conclude that aerial tree identification using crown characteristics is possible (Myers and
Benson, 1981; Trichon and Julien, 2006), while others achieved less optimistic
144 results, obtaining lower identification accuracies (Sayn - Wittgenstein. et al.,
1978; Trichon, 2001).
At the moment, much more research has been conducted using semi-automated techniques for delineating or separating crowns in simpler temperate or sub- tropical regions (usually without canopy closure) (Brandtberg., 2002;
Brandtberg. and Walter., 1998; Erikson, 2004; Gougeon, 1995; Gougeon, 1997;
Gougeon, 1999; Gougeon, 1998; Gougeon., 1992; Gougeon. and Leckie., 2001;
Held, 2003; Leckie et al., 1999; Leckie et al., 2003a; Leckie et al., 2003; Leckie. and Gougeon., 1998b; Warner and Jong, 1998). As might be expected, current studies have not provided sufficient information to understand and explore the basic concepts related to the visual discrimination of crown properties at different taxonomic levels, specifically Amazonian tree crowns.
4.2 RESULTS AND DISCUSSION
4.2.1 Development of the key
A preliminary key is obtained as the first hierarchical classification of the crowns. This version can be seen in appendix 7. The key has four mayor categories (crown type, foliage continuity, crown architecture, foliage texture) and several sub-classes. From this key, only the main properties (crown type, crown shape, foliage texture) are used for the development of the online key (see a full printed version of the online key in Appendix 6).
145 4.2.1.1 Crown properties (CP)
Great variability in crown properties has been found in the samples analysed.
Figure 32 illustrates the crown properties to be quantified at different taxonomic levels. Woody elements, leaf presence, foliage continuity (see the terms explanation in section 3.7.3 in Chapter 3) could be considered as impractical properties as a means of identification because most of the individuals fall into the same sub-classes of this property. On the other hand, crown type, crown shape and foliage texture could be more efficient properties because different taxa fall into different subclasses for those properties.
Figure 32 This is a schematic example (profiles and original crown images) for each of the main CP and its sub-classes.
146 4.2.1.1.1 CP at family level
Once the main properties were identified, then a measure of the crown property variation is needed. This section conducts an analysis to compare the behaviour of different properties at different taxonomic levels.
If we now examine the exclusivity of crown properties within families, the result in figure 44 suggests that moderate crown variability is reported. None of the families exceed 45 % of exclusivity with the exception of Moraceae (dotted texture) and Fabaceae (flat shape) who reach > 60 %. With regard to crown type, the same output is reported; there is no exclusivity over 43 % for any of the families (Figure 33). In general, foliage texture is the best property for separating families and genera while crown type and shape are less efficient (Figure 33).
From the results it is clear that most of the families have a single crown type with the exception of Moraceae which shows a predominantly multiple crown type with 40 % exclusivity (middle of figure 33). Fabaceae has half single and half multiple crown type. In relation to crown shape, most of the taxa have a rounded crown, with some exceptions such as Fabaceae (60 % exclusivity) with a flat crown shape predominating over a rounded shape.
147
Figure 33 Crown property exclusivity for foliage texture (top), crown type
(middle) and crown shape (bottom) at family level. Values close to 1 signify high exclusivity.
4.2.1.1.2 CP at genera level
The exclusivity index shows that in terms of foliage texture, dotted is the best mean of identification to separate Cecropia. The rest of the taxa share many of the properties, which may reflect the low exclusivity due to the minimum frequency values when more taxa are within the same property. Crown shape is another efficient property but only for Inga (flat shape) because the exlusivity
148 value decreases from 0.6 at family level to 0.4 at genera level, eventhough it is shared with Parkia (Figure 34). This result show that families with genera that are more common in the local flora tend to have higher exclusivity values and families with unique genera have lower exclusivity values maybe because they are floristically less frequent. In other words, the exclusivity index seems to be frequency dependent. However, this trend would be reflecting the well-known fact that dominant families in Lowland rain forest such as Fabaceae has more individuals that the others, possibly this is why some families show even lower exclusivity index values.
Figure 34 Structural variation of the three main crown properties at genera level. Values close to 1 signify high dominance.
149 In general it can be said that most of the crowns are single and rounded. With respect to foliage texture, it is clear that a textural gradient exits, with a range of patterns from smooth to smoky.
The palm species analysed are Astrocaryum chambira and Iriartea deltoidea.
High exclusivity values are reported for both of them because of the palms have a unique shape and at the same time they are very easy to discriminate in the canopy due to be extremely different from the other crown types (Figure 35).
Figure 35 Comparison of crown type between two palm species against non- palms. Values close to 1 signify high exclusivity.
Unfortunately, the evidence to use this particular approach as an efficient crown property variability measure is not strong enough for a firm conclusion.
However, due to the fact that exclusivity values are lower than 50 %, it may be that the exclusivity index approach fails to quantify efficiently some of the crown
150 property variability. In contrast, it can be mentioned that it works better as an alternative method for identifying or classifying crown properties, which is worthy because it is useful to confirm the existence of high structural variability of the crown properties in Amazonian trees.
4.2.1.2 Crown Property Signatures (CPS)
Now we will look at the results for combinations of crown properties (crown property signatures) as a means of separating taxa (see explanation about CPS in
Section 3.8.1. in Chapter 3).
4.2.1.2.1 CPS at family and genera level
Twenty-one CPS were shown to be the more common on the basis of the frequency analysis, but only five are the most repeated as shown in Table 12. The frequency for the most repeated CPS (0200+01) is 286. The frequency for the
CPS 0201+01 is 176, 18 for 0200+00, 27 for 0204+01 and 26 for 0212+11. The frequency for the less common CPS is between 1 and 18. The total number of individuals within the whole sample is 490.
Property Leaf Woody Crown Foliage Foliage Crown shape Signature Presence elements type Texture continuity 0200+00 0= With 2= No visible 0= Single 0= Smooth 0= Continuous 0= Flat leaves 0200+01 0= With 2= No visible 0= Single 0= Smooth 0= Continuous 1= Rounded leaves 0201+01 0= With 2= No visible 0= Single 1= Mottled 0= Continuous 1= Rounded leaves 0204+01 0= With 2= No visible 0= Single 4= Grainy 0= Continuous 1= Rounded leaves 0212+11 0= With 2= No visible 1= 2= 1= 1= Rounded leaves Multiple Granular Discontinuous
Table 12 The most frequent CPS at family level
151 The relevance of the CPS approach is to see if forming combination of properties
(one by one) could be an alternative method to understand the crown property discontinuity and also to identify at what hierarchical level the properties can affect the means of identification (Figure 36).
Figure 36 An example of four taxonomic groups (families) obtained using three hierarchical levels
CPS exclusivity values decrease from family to genera level mainly for Fabaceae and Moraceae. This output could be because there are two genera per family, sharing the total frequency. Another result is related to the fact that CPS present different values between genera, in the case of Fabaceae, it means that each taxa is expressing preference for certain combinations than others (Figure 37). For example, Inga and Parkia have different values for different CPS and there is also a predominant CPS for each one (0200+00 for Inga and 0200+01 for
Parkia).
152
Figure 37 CPS for families (top) and genera (bottom) level. Values close to 1 signify high dominance.
153 In summary, our results may indicate that, using specific indicative crown properties such as foliage texture, crown type and shape instead of all of them together as signatures seems to be most appropriate for the visual separation of crowns. Although separation of crowns using groups of properties called CPS is possible, it is not the most practical approach to quantify crown variability.
However, it does make an important contribution to prove the existence and implications of hierarchical levels on classifying crowns. Finally, in order to further investigate the crown properties are stable separators of particular taxa across a range of users. Another approach based on validation of the crown properties using an online key is conducted in the next section.
4.2.2 The online key and their validation
The main goal of this section is to understand the implications of using a manual- visual identification system called “online key” for the taxonomic classification of ten trees (Objective 2, 3 and specific objective c). In order to create a key that represents the range of crown properties. The best exclusivity index expressed in terms of CP and CPS are used as baseline information for the conformation of the online key. The so-called main properties (crown shape, crown type and foliage texture) are then taken as means of identification to develop the final online key (see a full colour version in Appendix 6).
The basic questions to be answered are:
(1) How many people identify the trees correctly and how many do not?
154 (2) Is a key which provides line drawings and diagrams (key 1) better than a
key which provides images, line drawings and diagrams (key 2)
(3) Which are the most common incorrect identifications and what are the
implications for the value of the crown properties concerned?
There are two basic assumptions to be tested:
Assumption 1: at least 50% of the observers identify the trees correctly.
Assumption 2: identification accuracy is higher when a crown image is part of the key.
4.2.2.1 Identification accuracy
The Identification accuracy (ID) results suggest that at least 50 % of the key respondents were able to identify correctly at least 50 % of the taxa (Figure 38).
For example, Inga (60 %), Pourouma (approx 12%), Guarea (5 %), had higher
ID accuracy with Key 1. In contrast, Iriartea deltoidea (98 %), Astrocaryum chambira (70%), Parkia (58 %) and Cecropia performed better with key 2. In detail, Iriartea, Astrocaryum, Inga, Parkia and Cecropia show a higher ID accuracy with both key 1 and key 2.
Overall, this key has 50% identification accuracy. The identification accuracy for
Iriartea deltoidea, Astrocaryum chambira and Cecropia was superior to 70%.
The identification accuracy is between 40-50 % for Inga and Parkia. In contrast, the other five taxa (Pourouma, Guarea, Otoba, Lauraceae and Pouteria) show less ID accuracy (approx 30%). Finally, as can be seen in figure 38, the palms
155 taxa (Iriartea deltoidea and Astrocaryum chambira) are by far the better identified, with ID accuracy between 70-90 %.
Figure 38 Identification accuracy percentage for ten taxa using a sample size of around 100 key respondents.
ID accuracy is not as high as the case reported by Trichon and Julien (2006), where 87 % ID accuracy (on average) was obtained between two photo- interpreters in French Guiana. Our online key obtained in average 50 % of ID accuracy. The differences in results would be explained because Trichon’s strategy includes two expert photo-interpreters while here 100 non-specialist key respondents conduct the identification. Therefore, it is relevant to clarify that methodological differences exist between these studies due to the approach and keys used are different.
If the key results are interpreted in terms of crown properties per se, it can be said that taxa with multiple crowns and/or with single regular surfaces are better identified than taxa with irregular textures, for example, the crowns belonging to
156 the genus Pourouma, Guarea, Otoba, Lauraceae and Pouteria, where a range of variation in terms of foliage texture properties is present.
The evidence suggests that the most reliable properties for aerial identification are either very clumpy or exceptionally regular textural properties. On the contrary, the main limitations are the intermediate textural gradients such as mottled and similar because of the difficulty to interpret and identify correctly as shown by the low ID accuracy in figure 38.
4.2.2.2 Crown properties mis-identified
The most commonly mis-identified crown properties are observed in B at the bottom of figure 39. In contrast, the crowns shown on the top of figure 39 had the best ID accuracy over 50 %, and the crowns in B had 20 % or less of ID accuracy. In essence, crowns with a complex texture pattern are more likely to be misidentified as demonstrated by quantitative analysis in figure 39. The differences between crowns in A and B are visually obvious, pictures on B represent a gradient of rough textures, while A shows a more constant pattern in terms of texture. This output support the idea that future visual keys using characteristics similar or close to the ones analysed in our approach add some reliability on identification success but of course just for certain crown properties.
157
Figure 39 Crown properties with the least mis-identification (A) and properties with most mis-identifications (B).
The error matrix (Table 13) confirms that Key 2 is more efficient than Key 1 in terms of total number of mis-identified properties (556 for Key 1 Vs 463 for Key
2. This is an evidence to say that key respondents improve ID accuracy when images are provided. Key respondents achieve better results to separate the two main contrasting crown properties with key 1 and 2 (palms against other crown forms). When crown properties different to palms are observed, the ID accuracy decreases because of the number of error increases between all the other crown types. For example, Pourouma is confused with Parkia 34 times with key 2 and
40 times with key 1. In contrast, the the less confused taxa is Iriartea with no errors in Key 1 and one mis-identification with Astrocaryum in Key 2.
158
Table 13 Error matrix for Key 1 and Key 2 based on 100 key respondents
4.2.2.3 Key limitations
One third of the 100 key respondents replied with comments and limitations or positive opinions about the key. Some of the most common technical limitations and observations are summarised on Table 14.
159 Key Difficulties Recommendations Respondent
1 -The size of the crowns in the photos -To include a reference sample is to small, -To improve the schematic -Image quality some times is poor illustrations -Criteria involving size is confusing 2 - Difficult interpretation of the -To work on clarifying the quality images to the reality of the images in the key
3 -Resolution changes when using -To suggest an ideal zoom in GoogleEarth scale in GoogleEarth 4 -Crowns with similar characteristics -To work on choosing better crown samples
Table 14 Some of the respondents comments about the online key
Regarding the personal communications, several respondents indicate that they had similar difficulties. For example, the amount of information describing each step of the key should be explained in detail, for example, if any crown shape features are mentioned (small or large), it is convenient to indicate a reference size scale in order to minimise the objectivity and increase the accuracy.
Many criticisms were down to image quality. For example, each computer has a different screen image, which depends on the quality of the machine therefore each respondent may observe some differences when looking at the Google Earth interface. In contrast, some key respondents complained about the size and resolution of the individual crown pictures shown in the key, but since they were extracted directly from the imagery, this is the kind of data from which identifications would have to be made if this system were to become operational.
Given all of these problems, to have obtained 50 % of identification success is an encouraging output. Although many artefacts are present, the main personal
160 difficulty was related to human collaboration because to gather and persuade 100 people to try the key was a difficult and time-consuming task.
Recently, this tool is in consideration to be certified as part of the initiative called
EDITS (European Distributed Institute of Taxonomy) at http://wp5.e- taxonomy.eu/blog/index.php, which is developing an e-platform for new online identification tools. This key fits within their concept of a novel type of e- taxonomy, called CyberTaxonomy. CyberTaxomomy is defined as “a taxonomic work process that involves the use of standardised electronic tools to access information (databases, e-publications) and/or to generate knowledge bases (identification keys) (Ebach, 2007). However, it needs to be improved. For this purpose, the next section reviews the most common mis-identifications.
4.3 CONCLUSIONS
Mixed results were found. Nevertheless, the main conclusions are:
• The concept of using eye visual crown classification approaches based on
aerial photography can be applied for distance tree identification.
• Splitting tree crowns by eye into taxonomic groups seems to be complex
but is possible, as already demonstrated by Myers (1982) and Trichon &
Julien (2006).
• Despite obtaining moderate results, it is usually impossible to conclude
that a single crown property or even a signature of many crown properties
is highly exclusive to particular taxa, the exception being palms and
Cecropia.
161 With regard to the properties that were choose for the online key validation, there are certain crown properties, which are the most useful for identification purposes. Foliage texture is the best example perhaps because it shows the more structurally related crown variability. In terms of other taxa, different properties could be used. In conclusion, this evidence is demonstrating the fact that crown texture variation is related to and can be expressed in a taxonomical way.
162 CHAPTER 5: THE SPATIAL PATTERNS IN THE DISTRIBUTION OF
KEY TAXA
5.1 INTRODUCTION
Baseline information about spatial distribution patterns of tropical lowland rainforest (TLR) trees at the landscape scale is poorly documented. There is more literature concerning patterns in density from plot studies rather than their spatial distribution. Studies have concentrated on describing spatial distribution at the plot or transect scales and identifying habitat associations with edaphic conditions or topography is another factor used for measuring environmental heterogeneity. However, few studies have concentrated at the landscape scale, over broad spatial extents, because of the significant effort required to do so on the basis of ground studies. One potential application of the imagery is for deriving large-scale datasets of tree distribution of the major readily identifiable taxa.
Previous studies were conducted around Tiputini Bidiversity Station in order to build up a forest canopy dataset with information such as crown structure and crown delineation (see more details in section 3.5.1 in Chapter 3). Therefore, this part of the thesis aims to map the spatial distribution of particular taxa at the landscape scale and describe the configuration of this distribution in terms of spatial point pattern but also relative to the properties of the underlying landscape. The analysis presented in this chapter focuses on describing the spatial distribution of the taxa under study, and provides some ecological
163 analysis of the possible driving factors. Chapter 6 takes the analysis further by looking at the possible abiotic factors that might be generating the spatial patterns examined here.
The main objective of this analysis is to quantify the spatial configuration of taxa distribution in the landscape. The key is used to identify individuals across the entire area in TBS where suitable imagery is available, and the spatial patterns are explored through applying the following approaches: (1) a simple mapping of the point taxa distribution in TBS (2) a distance distribution analysis to identify the spatial configurations of clustering (3) spatial autocorrelation using Moran’s -
I index, (4) a taxa clustering analysis using a moving window function to identify areas where densities are greater than the average for the entire study area (5) and finally the Ripley-K measure of spatial aggregation. These analyses contribute to achieve objective 5 of this thesis, and specific objective e, which are related to understanding the spatial distribution of some taxa at the landscape scale.
5.2 RESULTS AND DISCUSSION
5.2.1 Spatial patterns
There is little information about the spatial distribution of common botanical families in TRFs. The purpose of this analysis is to explore the type of spatial distribution of mapped trees and their spatial patterns at landscape scale. The main questions to be explored here are: (a) what are the spatial distribution patterns and (b) how much spatial aggregation exists between the mapped taxa?
164 The analyses are applied to ten mapped genera (higher taxa) and two palm species: Arecaceae (Iriartea deltoidea, Astrocarium chambira), Bombacaceae
(Ceiba), Fabaceae (Inga and Parkia), Meliaceae (Guarea), Moraceae (Cecropia and Pourouma), Myristicaceae (Otoba) and Sapotaceae (Pouteria). All those taxa are considered as part of the most common tree composition in the
Ecuadorian and Peruvian Amazon plots network. The dataset used here is based on empirical information. Each taxa has a minimum statistical sample of 60 individuals. The results produced in Chapter 4 about the online identification key were obtained by 100 novices, whilst the main dataset used here was produced by a few experienced interpreters.
Moran’s autocorrelation index was applied to know what kind of spatial distribution the mapped taxa display (see section 3.8.4 in Chapter 3). A more detailed approach is needed in order to discover whether or not the distributions form any spatial pattern (i.e concentration across lines). The spatial patterns per taxa were explored with a cluster analysis using ratio function.
5.2.1.1 Point distribution maps
Figure 40 shows the point distribution maps for the ten mapped taxa. The covered area is equivalent to a section within TBS of about 180 ha. The idea of showing these maps is just to provide an initial visual illustration of the mapped distributions.
165
166
167
168
169
170
Figure 40 Point patterns for the spatial distribution of ten taxa at TBS. From top to the bottom the taxon maps are Arecaceae (Iriartea deltoidea, Astrocaryum chambira), Bombacaceae (Ceiba), Fabaceae (Inga, Parkia), Moraceae
(Cecropia, Pourouma), Meliaceae (Guarea), Myristicaceae (Otoba) and
Sapotaceae (Pouteria).
Besides exploring the spatial distribution of the mapped taxa, it is vital to investigate the possibility that the distribution of the taxa may form specific patterns (i.e. linear or groups) across the landscape. It is important to approach this aspect because further analysis in Chapter 6 will provide additional terrain infomation, which might then be correlated with the specific patterns.
171 5.2.1.2 Distance distribution analysis
Spatial patterns change from taxa to taxa. However, the forms of distribution that have been identified can be classified into two main groups: linear and non- linear. Non linear refers to distributions that do not show a clear lines pattern, usually they are well spread out over the landscape covering large areas such as
Iriartea deltoidea. In contrast, the linear form is represented by taxa showing lines as the main pattern. Taking into account that palms are very common in
Amazonian environments, it is important to mention that mapping canopy palms based on aerial photography is highly accurate as demonstrated in Chapter 4 where palms are clearly visible and distinguishable from the air as proven by the high identification accuracy (90 %) found in both palm species. Each of the mapped palms has a different distribution pattern as mentioned by Vormisto et al., (2004) who mapped 56 palm species in the Amazonian Rain forest in NE
Peru and reported that palms distribute non-randomly, unfortunately they do not provide more specific information about the spatial configurations as reported in this thesis for TBS. The explanations that underly these patterns are explored more deeply in Chapter 6.
5.2.1.2.1 Non-linear
Cecropia and Inga belong to the non-linear group but differ from Iriartea deltoidea because they tend to create corridors. Note that Iriartea deltoidea has a more dispersed spatial configuration, which means points more randomly located in the map (See top of figure 41). While Cecropia and Inga tend to form a more
172 constant and narrow paths. This means that they are forming long transects through the landscape, instead of short lines. That is the reason why they are considered a non-linear pattern. Cecropia and Inga are taxa with a high abundance and are floristically common in TBS and the region itself (Valencia,
2004). The distance distribution maps (Figure 41) are encouraging output because they are suggesting the possibility that aerial mapping in some cases would be able to pick up the patterns that have been reported by ground sampling.
Iriartea deltoidea (Arecaceae)
173 Cecropia (Moraceae)
Inga (Fabaceae)
Figure 41 Non linear distribution for Irartea deltoidea, Cecropia and Inga
174 Seed dispersal is a function of many factors. Bats, small rodents and several species of monkeys (spider, white-faced) play a role in dispersing many seeds, which may contribute to the aggregated dispersion pattern of large- seeded tropical forest trees. As previously mentioned the seed dispersion pattern produced by spider monkeys, is a possible factor generating the observed tree distribution pattern at TBS. There is a possibility that dispersion of seeds by mammals plays a key role on the distribution of taxa. Large-seeded neotropical trees, such as some palm species, are characterised as being dispersed by large rodents, primates and sometimes mammals. For example, tapirs, which are large mammals in the Amazon, appear to be an important dispersal means for some palm species. Iriartea deltoidea is dispersed mainly by woolly monkey
(Lagothrix lagothricha), white-bellied spider monkeys (Ateles belzebuth) and red side-necked turtle (Phrynops rufipes) (Stevenson, 2000).
Moraceae are usually canopy or sub-canopy trees. Cecropia is an early succession species, typical of secondary forest occupying gaps. Dalling et al,
(1998) found that seedlings were non-randomly distributed among gaps. Another characteristic about Cecropia is that it is a pioneering tree with a high density and abundance in Central Amazonia.
Seed dispersers such as opossums (Delphis marsupialis) also play a determinate role to produce evidence that dispersal distances are relatively short, which may result in clustered distributions. Medellin (1994) showed that Cecropia obtusifolia seed dispersal distances varied between 0 and 71 m. In contrast to terrestrial mammals, Cecropia seeds represent a major food item for several
175 species of bats. For example, a genus called Carrolia, which is a very common frugivirous bat in tropical forest, was the main seed disperser of Cecropia glaziovii in the Picasicaba river, Brazil (Medellin, 1994). More precise evidence about non random distribution of seeds is provided by Loiselle et al., (1996), where spatial foraging activity and habitat preference of seed dispersers results in a non-random patterns of seed rain in a tropical wet forest in northeast Costa
Rica for this genus.
Fabaceae is the family that contributes most to the diverse tree floristic composition in the Amazonian forest. Because of the fact that it is a very common family, one would expect them to be widely distributed. This assumption is confirmed with the distribution map because of their constant coverage across the landscape (see figure 41). Inga is considered one of the most common genera in the Ecuadorian and Peruvian Amazonian and also is a hyperdiverse genus. While, Parkia is not a highly diverse genus, it is one of the more striking emergent trees in Amazonian forests.
5.2.1.2.2 Linear distributions
The evidence to indicate that linear patterns are formed is that certain taxa, for example, Guarea and Otoba show lines generally with distribution patterns pointing SE/NW (Figure 42). It is important to clarify that not all the lines always point in the same direction and also note that the lines do not appear as a constant shape because there are gaps between areas. As a result, different types of lines can be found, as can be seen in figure 42, Astrocaryum chambira and
176 Pourouma (point towards NW direction) trend to have longer lines than Guarea and Otoba that have formed groups of lines.
All these patterns may be related to mammal behaviour (especially woolly monkeys) as mentioned by primatologist at TBS (Mike Montauge from NYU,
Pers. Comm.) that different monkeys (depending of the species) move in different manners (lines, circles etc). A research study conducted in the same region of this thesis mentioned that “ Yasuni spider monkeys travel routes tend to be composed of long, straight paths linking together a series of primary feeding trees” (Suarez, 2003). One case reported that the mean daily path length is
3311m/d and the mean maximum distance between patches across follows is
1393 m (Suarez, 2006).
Guarea (Meliaceae)
177 Otoba (Myristicaceae)
Astrocaryum chambira (Arecaceae)
178 Pourouma (Moraceae)
Figure 42 Linear distribution for Astrocaryum chambira, Pourouma, Guarea and Otoba
There are few reports about the spatial distribution of Guarea in the literature. A recent publication confirmed that four Guarea species have an aggregated seed pattern distribution in Yasuni National Park (Link and Di Fiore, 2006). The reasons related to this kind of pattern could be explained due to vertebrate seed dispersal. As an example, Waorani’s indigenous in the study region call the
Guarea tree “manzano”, which means “fruits that look like an apple”. The rounded shape, reddish external colour and sweeter smell of the succulent pulp
(when ripe) make it attractive to monkeys. Particularly, field observations and conclusions of primate studies in Yasuni National Park conducted by Link and
Di Fiore (2006) confirm that spider monkeys (Ateles sp) are dispersers of
179 Guarea species, with an average dispersal distance of 200 m from the feeding patches. Therefore, it might be assumed that the spatial patterns obtained could be related to the grouped seedling patterns.
On the other hand, bird species (Myiarchus crinitus, Catharus ustulatus, Vireo olivaceous and Vermivora peregrina) have been reported to visit and remove the seeds from Guarea glabra in Barro Colorado Island, Panama. The Otoba tree is also one of the top five genera preferred by Woolly monkeys. They tend to produce long-distance seed dispersion from the parent tree of between 100 and
500 m (Di Fiore 2006), which is very similar to the spider monkey range.
According to Salm (2005) “the size and shape of seeds can also influence plant establishment probability by affecting the distance over which seeds can be dispersed”. As an issue related to plant distribution, large seeds may present lower abundance and narrower distribution. For example, it was demonstrated by
Link and Stevenson (2004) that spider monkeys disperse Iriartea and
Astrocaryum seeds but almost all of them are destroyed, which may contribute to them maintaining narrow spatial distributions. They also noticed that the vast majority of seeds defecated by spider monkeys were deposited > 100 m away from the parental feeding patches but in average no further than 334 m.
Consequently, “these results could imply that the general pattern of defecation by spider monkeys is highly clumped in space”.
180 The interesting aspect of relating monkeys and spatial distribution of trees is the fact that most of the literature report non-random and aggregated seed dispersal patterns. Di fiore (2004) mentioned that monkeys go from tree to tree until a conspecific tree is not present on their range (it means that a primary feeding tree is out of the monkey paths), then logically the next step would be to start again to find another primary or secondary feeding tree with a result of making a chain of lines nearly everywhere in the landscape. In conclusion, it is still unclear to what extent other landscape factors control the distribution of trees in the TRF. This is discussed in Chapter 6.
Astrocaryum is usually dispersed by squirrel (Sciurus spadiceus), monkeys
(Cebus apella), rodent scavenger (Dasyprocta variegata), wild pig (Tayassu pecari) and small rats (Proechmys spp and Oryzomys spp). Non-random distribution is a well-reported spatial pattern for palm trees in TRF. Our findings are in agreement with the literature because Iriartea deltoidea and Astrocaryum chambira present a non-random distribution pattern, but Astrocaryum chambira shows a different form of distribution than Iriartea deltoidea.
Taking into consideration that not only primates influence the agreggated distribution of forest trees in the Amazon, there are large scale studies reporting that large mammals such as Tapir (Tapirus terrestris) in TRF in Brazil, feed on palm fruits called Maximiliana, and produce a clumped dispersal pattern of the seeds along the tracks.
Pourouma differs from Cecropia in that it is not typical of secondary forest gaps.
Although Pourouma is also clustered, Figure 42 shows a difference in the point patterns compared to Cecropia. Pourouma is known to be dispersed more by
181 monkeys instead of bats as demonstrated by Oliveira and Ferrari (2000) where black-handed tamarins disperse seeds with a regeneration function in the eastern
Amazon. Again, literature and our spatial pattern results provide evidence that a mixture of niche-based and dispersal-limited processes may be controlling the spatial distribution of Cecropia and Pourouma.
Ceiba, Pouteria and Parkia are part of the non-linear pattern, they could be referred to as a sub-section of the lines-forming groups but with the difference that each cluster tends to be separated from each other (Figure 43).
Ceiba (Bombacaceae)
182 Parkia (Fabaceae)
Pouteria (Sapotaceae)
Figure 43 Generally non linear (i.e. isolated individuals or groups forming semi- circular shapes) distribution for Ceiba, Parkia and Pouteria
183 A number of different ecological and biophysical factors may be controlling the observed spatial distribution. Ceiba may be occupying a very specific microhabitat, or the ecological processes controlling their distribution are extremely different to the rest of the taxa. This observation may suggest that the spatial distribution of Ceiba is affected by an ecological process such as the role of wind in seed dispersal “anemochory”. The seeds are embedded in dense masses of silky hairs inside large woody capsules, the taxa has evolved to use wind as its primary dispersion mechanism, which is unusual for tropical forest trees. The seed is such that the hairs promote long-range dispersion, and this may create a spatially dispersed pattern with large distances between individuals.
Fabaceae is a family characterised with having fat-rich, juicy and sugar-sweet arils. In particular, Inga is a mammal-dispersed genus, with spider monkeys being one of the main dispersers as demonstrated by Link and Di Fiore (2006) in
Yasuni National Park, Ecuador, just 50 km away from the study site. In contrast,
Parkia is more commonly a bat-dispersed and pollinated genus. There is also concrete evidence showing that bats produce clustered spatial seed dispersal in
TRF. In conclusion seed dispersal syndromes seem to have a relevant effect on
Inga and Parkia distribution.
It is known that “spider monkeys are significant dispersers for plants in the neotropics”, and Sapotaceae is one of the main families that the monkeys feed on. According to Julliot (1997) an aggregation pattern of seed dispersal is produced by red howler monkeys (Alouatta seniculus), for example, “seedlings on sleeping site plots are distributed in clumps”, which suggests to have some
184 relation with the observed distribution pattern for the trees mapped using the aerial photography technique in TBS. The red-rumped Agouti (Dasyprocta leporine) concentrates their feeding habits close to fruiting trees. Therefore,
Kirsten and Jose (2003) concluded that Agoutis are a short-distance seed disperser, which may contribute “to the aggregated dispersion pattern of large- seeded tropical forest trees”.
In conclusion it can be said that there are two predominant forms of distribution in the studied area, taxa that seem to fall along linear pathways, isolated lines or grouped taxa. Now it is necessary to know if the distribution of the taxa throughout the study area is statistically clustered or not. To address that question, the following section measures spatial autocorrelation by applying the
Moran’s Index.
5.2.1.3 Moran-I spatial autocorrelation index
Results from Moran-I are summarised in table 16. As expected, at the landscape scale (180 ha), nine of the ten taxa studied were significantly clustered (values in grey are clustered), the only exception was Ceiba (Bombacaceae), which has a dispersed distribution pattern. The predominantly positive Moran’s autocorrelation signify that all similar values appear together and are therefore spatially autocorrelated. The main output suggested from Moran’s I is the fact that most of our mapped taxa are not randomly distributed. This observation should not be consider as a wholly negative output because this results are in accordance with the literature about tree spatial patterns, which demonstrate that
185 many of the species in neotropical forest tend to be spatially aggregated rather than randomly distributed (Condit et al., 2000). Therefore, it is important to clarify that our study works at larger scales than some of the other cases (Link and Di Fiore 2006) because the sampling strategy is based on using aerial photography and as a result covering larger areas.
Taxa Moran’s I z-score I E Arecaceae- Iriartea 0.3830 -0.0013 1.8e-005 deltoidea Arecaceae- Astrocarium 0.4666 -0.0033 0.0001 chambira Bombacaceae- Ceiba -0.02246 -0.0166 0.0010 Fabaceae- Inga 0.4228 -0.0028 4.9e-005 Fabaceae- Parkia 0.2789 -0.0096 0.0007 Moraceae- Cecropia 0.3941 -0.0031 9.3e-005 Moraceae- Pourouma 0.5136 -0.0054 0.0002 Meliaceae- Guarea 0.2413 -0.0099 0.0005 Myristicaceae- Otoba 0.2500 -0.0096 0.0006 Sapotaceae- Pouteria 0.4951 -0.0166 0.0024
Table 15 Spatial autocorrelation measure using Moran’s I C index at TBS. For
Moran’s I: (1) if I > E (I) then clustered patterns (2) if I ~= E (I) then random and
(3) if I < E (I) then dispersed. Z-scores < 0.05 is statistically significant. Values in grey indicate positive spatial autocorrelation and clustered distribution.
The Moran’s I analysis demonstrates that most of the taxa are clustered.
However, Moran’s I does not indicate how clustered they are nor the type of clustering displayed. In order to answer this question, the next sections conduct two analyses: (1) a distance to nearest conspecifics measure (2) and a measure of spatial aggregation called Ripley’s K.
186 5.2.1.4 Clustering analysis
The aim is to measure the average number of individuals present in a radius of
125 m around each individual, it means 250 m², for each of the taxa. This analysis is repeated for all individuals to derive a map of the ‘stem density’ for each of the taxa. This is then expressed as a ratio of local stem density to the mean stem density of the entire study area in order to characterise which areas have higher than average stem densities and which lower. Looking at the maps, areas with values >1 would be more clustered than average and areas <1 would be more dispersed.
Clumped spatial patterns are predominant; as taxa tend to aggregate in the landscape, at least at 0-2 km scale. Each taxon has a particular clumping structure. The clustering index values for the taxa in Figure 44, range more often between 2 and 3, which means an intermediate clustering value. The spatial distribution of Iriartea deltoidea suggests a clumped but regular distribution.
While Astrocaryum chambira have a more constant clumping, but the group of clumps are less interconnected than I. deltoidea (Figure 44). It can be said that there is a loss of regularity in the distribution but groups of clumps that are all spread out are recognised, particularly for Inga, which seems to form a netwok of clumps in a linear pattern, but Cecropia is still closer to the patterns shown by
Iriartea deltoidea and Astrocaryum chambira.
187 Iriartea deltoidea
Astrocaryum chambira
188 Cecropia
Inga
Figure 44 Clusters with regular distribution pattern for Iriartea deltoidea,
Astrocaryum chambira, Cecropia and Inga.
189 These differences in the shape of the clumping are interesting because they may suggest some interconnection with terrain variables, for example, a possible correlation of Iriartea deltoidea with ridge tops. In relation to Astrocaryum chambira perhaps there is a relation with mid-slopes and low elevations. These assumptions will be tested in Chapter 6.
Parkia, Pourouma, Guarea and Otoba have an irregular clumping structure because it is conspicuously patchier (Figure 45). The clustering index values for the taxa in Figure 45 range mainly between 3 and 4. Taking into consideration that higher values of clustering (> 3) are reported for this group (Figure 45), this observation may suggest that those taxa would be occupying a more specific niche in the landscape, whilst the last group (Figure 45) could be more adapted to a wide environmental range, resulting in a more dispersed distribution pattern.
Parkia
190 Pourouma
Guarea
191 Otoba
Figure 45 Clustered with aggregation patterns for Parkia, Pourouma, Guarea and Otoba.
Ceiba and Pouteria represent a typical case of an irregular dispersed pattern
(Figure 46). The clustering index values are lower (between 1 and 2 but more values less than 1) in comparison to the rest of the taxa. The kapok tree, Ceiba, is usually one of the emergent trees in the Amazon (field observations), and has a dispersed spatial pattern with very few clusters as indicated here.
192 Ceiba
Pouteria
Figure 46 Dispersed pattern for Ceiba and Pouteria
193 As a general observation, it is suggested that more clusters are found in the northeast part of the studied area. Why does this happen? Is this area a local hotspot? Or is there any technical or measurement artefacts influencing the result? This result may be related to the fact that the topography is less rough
(flatter) on the western part of the studied area (see section 6.2.1 in chapter 6). A more regular landscape may lead to a less variable environment, which could explain the presence of more clustering of taxa on the northeast section that, at the same time, demonstrates a more variable topography (see maps on section 6.5 in Chapter 6). Alternatively, it is been reported that river banks or so-called
“varzea forests” are less floristically diverse (Pitman et al., 1999).
Another aspect that could be related is that certain taxa are underneath the canopy layer, which is not observable from aerial photography. Another reason for aerial identification might be the imagery lighting conditions, which are clearly more cloud shadowed on the eastern part of the studied area (see appendix 1).
5.2.1.5 The Ripley-K measure of aggregation
The objective of this test is to identify the degree of spatial aggregation of the mapped taxa via a multi-scale analysis using Ripley’s K. It is important to conduct this test because it is necessary to explore if the measure of clustering changes with the search radius used. Results from Ripley-K are summarised in
Figure 47. The Ripley-K outputs reveal closely what was found with the ratio distance map. They are not precisely equal but in general the patterns are similar.
194 It means that the curves in figure 47 do not change pattern but intensity of K does change from taxa to taxa (the ten taxa studied), which is important to know because this is supporting the fact that spatial aggregation is specific to taxa. In other words, figure 47 corroborate that the proposed three clustering groups are revealed by the K function.
The curves are saying that Iriartea deltoidea, Astrocaryum chambira and
Cecropia show a higher degree of aggregation. In contrast, Ceiba and Pouteria have a more dispersed spatial distribution pattern as demonstrated with Moran’s approach. Finally, an intermediate degree of aggregation is found for the rest of the taxa (Pourouma, Guarea, Parkia and Otoba).
Figure 47 Multi-scale Ripley-K spatial aggregation analysis for ten key taxa distributed across TBS. Clustering increases K, while regularity decreases K.
195 5.4 CONCLUSIONS
The nature and degree of spatial aggregation changes between the taxa studied.
Two distribution forms are reported: linear and non-linear. Regarding the clustering analysis, the data indicates that three groups can be proposed: (1) taxa clustered with mean index value of 3-4 (2) taxa with regular distribution with mean index value between 2-3 (3) taxa dispersed with mean index value 1-2
Concerning taxonomic rank, higher taxa (families and genera) seem to be an efficient taxonomic level to capture a relevant proportion of the ground tree composition using aerial photography. As a demonstration, Higgins and Kalle
(2004) commented that in a ground based-sampling “genus-resolution inventory captured 80% of the floristic pattern of the full inventory, with an 80% reduction in number of taxa sampled, while family level preserved roughly one-third of the information in the full inventories with 6 % of the total number of taxa”.
Therefore, to some extent it is possible to say that canopy based mapping using aerial identification at higher taxa levels works for understanding ecological processes and their correlation with spatial distribution of trees in the landscape.
However this technique is not suitable for detailed compositional studies requiring information from the sub-canopy layer.
When the spatial patterns are defined per taxa, Arecaceae (Iriartea deltoidea and
Astrocaryum chambira), Fabaceae (Inga and Parkia), Moraceae (Cecropia and
Pourouma), Myristicaceae (Otoba) had a clustered pattern. In contrast,
Bombacaceae (Ceiba) shows a dispersed distribution. All the evidence seems to
196 support the idea that clustered distributions are predominant, especially for those taxa whose seeds are animal dispersed.
The following chapter analyses to what degree landscape properties (elevation, slope, slope position, etc) are affecting or controlling the distribution of taxa in the landscape in order to provide a more complete picture of the drivers of spatial distribution of these taxa in TBS.
197 CHAPTER 6: THE DISTRIBUTION OF KEY TAXA IN RELATION TO
TERRAIN AT TBS
6.1 INTRODUCTION
To correlate landscape-mapped taxa with eight known terrain characteristics across the reserve is the fundamental issue to be approached in this chapter. The purpose is to produce a quantified terrain analysis in the context of GIS in order to understand the implications of DEM-derived terrain characteristics on the distribution of key taxa at TBS (upscaling to the landscape scale from the plot scale work in this area carried out by Jarvis (2005). The aim is to better understand the terrain properties that control the distribution of taxa (General objective 6 and specific objective f).
6.2 RESULTS AND DISCUSSION
6.2.1 TERRAIN VARIABLES
Eight terrain characteristics derived from the topographic DEM by Jarvis (2005) have been used. These diagrams that follow show the terrain variables calculated for the sections of 600 m imagery analysed within TBS. The terrain variables analysed were curvature, elevation, slope gradient, slope position, eastness, northness, solar radiation and soil wetness. The terrain characteristics were chosen because they may represent a broad range of terrain properties that control spatial distribution and tree composition. A brief description is provided in this thesis in a sub-section called taxa distribution in relation to terrain in
198 Chapter 3. Alternatively, a general explanation about what the terrain indices or
variables mean are summarised in Table 16.
Terrain variable (units) Description General meaning Elevation (meters) Variation of the terrain terms The elevation gradient of altitude (193 to 270 meters) Mean Curvature (index) Degree of curvature Negative numbers signifies (minimum of –1.9 and max concave slopes of 2.6) Positive numbers signify convex slopes Slope (degrees) Variation in inclination Degree of inclination (min of 0.0 and max of 31.2) Slope position (index) Measures the relative 0 signifies valley bottoms position of a cell from valley 50 signifies mid-slopes floor to ridge (min of –48 100 signifies close to the and max of 149) ridges Eastness (index) Measure of aspect in receipt Positive values signifies east of solar radiation (min of –1 facing slopes and max +1) Negative values signifies west facing slopes Northness (index) Measure of aspect in receipt Positive values signifies of solar radiation (min of –1 south facing slopes and max +1) Negative values signifies north facing slopes Solar radiation (J/m²) Potential solar radiation Total solar radiation receipt receipt (min of 8657 and max of 9662) TopModel (index) Soil moisture wetness index Rivers have high TopModel (min of 7.1 and max of 25.4) index and ridges and peaks have very low wetness values
Table 16 Summary of the terrain variables for TBS (From Jarvis 2005).
Using the derived TOPO DEM mentioned in table 18, eight terrain maps are
produced. The maps were calculated using PCRASTER and include just the
section of TBS where the mapped taxa are located. The mean terrain values for
the mapped areas (for the ten taxa) and the random points are presented in separate table as part of the description of the terrain characteristics (section 6.6 in Chapter 6, Jarvis 2005).
199 6.2.1.1 Elevation
The elevation ranges between 190m and 245m (Figure 48), higher elevations occur in the northern part of the reserve and lower elevations closer to the
Tiputini riverbank (in pink). Most of the elevation range for the area reported by
Jarvis (193-270 m) is represented within the area of 600m imagery, with the exception of highest peaks at 270 m.
Figure 48 DEM for the area of 600 m imagery analysed (meters above sea level)
200 6.2.1.3 Mean curvature
Mean curvature represents the trend in change of slope. Jarvis (2005) reported indices with a minimum value of –1.9 and a maximum of 2.6. The blue areas in figure 60 represent zones with concave slopes and the green areas show the topography with convex slopes. The curvature values vary between a minimum of – 0,6 and a maximum of 0,7 (Figure 49). This means that the majority of the studied area is represented by moderate curvature. There are more complex changes in slope in the east, especially the northeast.
Figure 49 Mean curvature for the mapped area (index)
201 6.2.1.4 Slope
The following map (Figure 50) shows the degree of inclination (slope gradient) in the landscape. The variation in inclination at TBS reported by Jarvis (2005) has a minimum of 0.0 and a maximum value of 31.2 degrees. The study area for this project shows a slope variation from 0 to 22 degrees. Again it is representing most of the slope ranges presented for the all TBS. The northeastern part of the area has more variable slopes. In contrast, the west part has a more homogenous topography in terms of inclination.
Figure 50 Slope for the mapped area in TBS (degrees)
202 6.2.1.5 Slope position
Slope position is another derived variable, which represents whether a cell is at the top, middle or base of a slope. The index values for the area covered by the
600m imagery range between –10 and 110 (Figure 51). The variations of the all
TBS have a minimum of –48 and a maximum of 149. The ranges of slope positions covered by the imagery are thus representative for the entire TBS area.
Large rivers are picked out clearly as are ridge tops. These kinds of changes may be important in determining taxa distributions since they affect the disturbance regime (by wind and flooding)
Figure 51 Slope position for the mapped area in TBS (index)
203 6.2.1.6 Eastness
Eastness is derived from slope aspect and may be important because of its impact on exposure to wind and the diurnal pattern of direct solar radiation receipt
(Figure 52).
Figure 52 Eastness for the mapped area in TBS (index)
6.2.1.7 Northness
Northness is also derived from aspect and can indicate the annual variation in radiation receipt, though it is unlikely to result in very significant differences in radiation load as near to the Equator as TBS is. The southwest part of the study area shows the stronger northness values (Figure 53), they are positive (meaning a south facing slopes) and range from 0,7 to 1.
204
Figure 53 Northness for the mapped area in TBS (index)
6.2.1.8 Solar radiation
Solar radiation indicates the potential solar radiation receipt (excluding the impact of atmospheric clouds). The highest solar radiation values are on the eastern part of the study area (Figure 54). The solar radiation receipt obtained for
TBS ranges from 8657 – 9662 Wm-² (Jarvis, 2005). The study area represents a range in general between 9100 – 9700 Wm-², but most of the area studied has values from 9580 to 9700 Wm². In general, it represents most of the data reported for TBS. The spatial variation in solar radiation is rather minor in percentage terms.
205
Figure 54 Solar radiation for the mapped area in TBS (W/m²/yr)
6.2.1.9 TopModel wetness
The TopModel wetness index represents the potential of landscape positions to receive or shed water. It is calculated as the sum of the upslope area (the accumulation) divided by the local slope (the capacity for shedding) and essentially captures local water availability. The TBS main river channel is well captured by the TOPO DEM as demonstrated by the yellow and red colours in the map (Figure 55). Unfortunately, the internal network of small channels is rarely captured by the 25m resolution DEM as mentioned by Jarvis (2005).
However, the green lines in the map denote the presence of streams. The blue areas in the map indicate low wetness index values, in this case ranging from 8 to
13.
206
Figure 55 TopModel wetness for the mapped area in TBS (index)
6.2.2 LANDSCAPE PROPERTIES
In order to examine the spatial distribution of these variables more closely and indicate to what extent they may be affecting the distribution of taxa, we first average them over a moving window of 125m radius around each cell and then express them as a ratio of the local (windowed) value to the average value of the area as a whole.
The landscape properties calculation was conducted using a 125 m radius moving window analysis around each crown. A moving window was used in order to provide average landscape properties centred on the crown locations.
Eight maps were produced - one for each of the variables mentioned in table 16.
207 Any identified preference is then further investigated in the following section, which undertakes a statistical analysis correlating the spatial distribution of the taxa and the same terrain variables. The landscape properties are described only for those that were identified as statistically significantly related to taxon distributions in the following section.
6.2.2.1 Elevation
The elevation index ranges from 0, 9 to 1,14 (Figure 56). The blue areas are the lowest elevations and the red parts the higher. Iriartea deltoidea and Cecropia have an index value ranging from 0,94 to 1,14 which means that they are distributed throughout the altitudinal gradient (200-245 m) with the exception of not reaching the highest points. Otoba show a distribution with elevation values between 0,95 and 1,08, which means to have an elevation between 205 and 230 m but at the same time covers a similar range to Iriartea and Cecropia. Guarea is a similar case to Otoba but it has a more significant presence in mid elevations
(215 – 230 m). Parkia, Pourouma and Astrocaryum chambira are distributed mainly on mid-upper elevations (1,04 to 1,07), which could be between 220 and
230 m.
208
Figure 56 Relative elevation for the mapped area at TBS
6.2.2.2 Mean curvature
The mean curvature index ranges from –160 to 140. Blue areas mean slopes with a concave terrain and from green to red signify a more convex topography
(Figure 57). Only Astrocaryum chambira and Iriartea deltoidea are statistically significant to the mean curvature. Astrocaryum chambira has a particular preference for slopes with a moderate convexity because most of the distribution matches with index values from –20 to 50, which means to be located on low terraces or toe mid slopes. While Iriartea deltoidea do not seem to have a specific curvature preference, it is distributed nearly over the whole range of curvature.
209
Figure 57 Relative Mean curvature for the mapped area in TBS
6.2.2.3 Slope
The slope index ranges from 0 to 4, meaning that the inclination on the pink areas of the map (Figure 58) is minimum or close to 0 and the yellow or green spots are steeper. Iriartea deltoidea covers nearly all the slope index values. The other palm species, Astrocaryum chambira, is distributed mainly on areas with a low inclination (index value from 0 to 2), which means an inclination between 0 and 12 degrees. Cecropia is a taxon that prefers areas with even less inclination than Astrocaryium chambira because it is reported to have an index value ranging from 0 to 0,16 or in terms of degrees equivalent to 0 to 8. Pouteria tends to occupy slope inclinations between Astrocaryum chambira and Pouteria, so that it is distributed on areas with moderate slopes (index values ranging between
0,8 and 1,6) such as 5 and 8 degrees. Finally, Guarea has a similar distribution to
210 Pouteria, with the difference that it also occupies lower slope ranges (index values 0,2 to 1,6) meaning degrees between 0 and 8.
Figure 58 Relative slope for the mapped area in TBS
6.2.2.4 Slope position
This ranges between values 0 and 2 in the map, with the red areas as the ridges and the blue and pink representing more the valleys (Figure 59). Astrocaryum chambira, Otoba and Guarea are taxa that prefers either valleys or mid-slopes
(index values from 0,4 to 1,4), whilst, Iriartea deltoidea and Cecropia are covering a broader range of positions in the topography (0,4 to 2), which means from mid-slopes to ridges. Inga is a tree that seems to have more clear preference for mid-slopes mainly (index values from 1,1 to 1,4), which is mainly the green area in the map.
211
Figure 59 Relative slope position for the mapped area in TBS
6.2.2.5 Eastness
Iriartea deltoidea was the only taxa with a significant statistic for correlation with eastness. The index values in the map vary from pink to red, which means that areas with high east facing slopes are greater than 0 (equivalent to the areas in green or red in Figure 60). Iriartea deltoidea do not seems to have a preference for a particular value, just it is more frequent on the green belt which values range from 0 to 12.
212
Figure 60 Relative eastness for the mapped area in TBS
6.2.2.6 Relative northness
The northness values range between –5 and 5 (Figure 61). Inga and Cecropia have a preference for positive values, which means south facing slopes.
However, there is one part of the Inga distribution that tends towards north facing slopes. Cecropia seems to have higher south facing values than Inga (3-5).
The other taxa relating to northness is Pouteria, it has more preference for a north facing slopes.
213
Figure 61 Relative northness for the mapped area in TBS
6.2.2.7 Solar radiation
The index values range from 0,974 as the ones receiving the less exposure and the areas in red with 1,006 are greater than 1 and reflect a high solar radiation receipt (Figure 62). Iriartea deltoidea distribution match very close to the red areas, meaning that most of the taxa are expose to high solar radiation (values index 1 to 1,006) and Inga is also more affected by values greater than 1 but with values more often between 1 and 1,003, which means lower exposure than
Iriartea deltoidea. The other taxa is Otoba that reflect a mixture of been expose to values close to 1 but around 0,988. Since the differences in radiation are minor the effect is unlikely to be strong.
214
Figure 62 Relative solar radiation for the mapped area at TBS
6.2.2.8 TopModel wetness index
Iriartea deltoidea is distributed on areas with moderate soil moisture, (values less than 1), which are the blue zones in the map (Figure 63).
215
Figure 63 Relative TopModel wetness index for the mapped area in TBS
A detailed statistical analysis is conducted in the following section. The purpose of the statistics is to validate and confront statistically what has been obtained and described using the landscape maps.
6.2.3 STATISTICAL ANALYSIS OF TERRAIN VARIABLES
The basic idea is to see if there is any statistical significance between two frequency distributions (representing random points and the mapped points occupied by the taxa) for ten taxa in relation to terrain. In every case, data are examined variable by variable using frequency histograms and tables. In order to prove that both distributions (mapped and random) have similar or different behaviour, the Kolmogorov-Smirnov (K-S test) statistical test was carried out. At
216 the same time, the Kolmogorov-Smirnov test (K-S) does not assume that the population is normally distributed.
To make the decision, the subsequent rules were followed:
If the p-value is greater than or equal to 0.05 (90% confidence level), there is not a statistically significant difference; which signifies that the sample distributions are statistically equal. It will then be concluded that the terrain characteristic is not having an effect on the presence of the taxa in the landscape since there is no difference between the frequency distributions of terrain occupied by the taxa and those occupied by the random points: in other words the taxa are not indicating a preference for particular types of terrain but rather a random distribution across the range of terrain values encountered in the landscape.
This piece of analysis composes of two parts, a frequency distribution chart contrasting the mapped distributions against the random distributions, and the difference between the mean terrain value of the taxa locations and the mean terrain value of the portion of the DEM covered by the aerial imagery used.
Summaries of the mean values for the mapped and random points are presented just for the taxa that are statistically significant. If there is a discrepancy between the mean datum, then it suggests that the elevation of the taxa carries an ecological significance, for example if the mean of the mapped distributions is higher, it suggests that trees situated in a higher elevation have an ecological advantage over those at a lower elevation. The magnitude of the difference will
217 represent the magnitude of that effect (so long as the K-S has already indicated the differences to be statistically significant).
From the results it is clear that the terrain characteristics do influence the distribution of taxa, but vary greatly from taxa to taxa across TBS. Nine of ten of the taxa have shown statistically significant differences in their terrain characteristics (Table 17). It is also clear that topography and elevation have a high degree of influence on the distribution of taxa and to a lesser extent some environmental variables such as solar radiation, soil wetness (TopModel), northness and eastness. This result provides evidence to say that nine of the ten taxa analysed show some association with the environment. However, Ceiba is the exception because the results show that the terrain characteristics have no impact on the Ceiba’s distribution.
Table 17 Summary of the statistical significance (K-S test) between terrain characteristics and ten key taxa trees in TBS, the statistically significant cases are highlighted in grey.
Iriartea (Arecaceae) show to be most susceptible to the terrain characteristics used in this study, with seven of the eight terrain variables showing statistical significance. Astrocaryum and Cecropia showed statistical significance for four
218 of the eight terrain characteristics, but Inga (Fabaceae), Guarea (Meliaceae) and
Otoba (Myristicaceae) have shown statistical significance on three of the eight terrain characteristic. Pouteria (Sapotaceae), Parkia (Fabaceae), and Pourouma
(Moraceae) just show statistical significance with one or two of the terrain characteristics.
As a summary, four possibilities could be proposed: (a) taxa without association to any terrain feature such as Ceiba (b) taxa associated to topographic features
(slope, slope position, curvature) and elevation such as Iriartea, Astrocaryum,
Guarea and Cecropia (c) taxa partially associated to terrain features such as Inga and Otoba and finally (d) taxa poorly associated to terrain characteristics such as
Pouteria, Pourouma, Parkia.
In general, slope position, which is a terrain derivative that measures the relative position of a cell from valley floor to ridge, and soil moisture, which can be refered as a terrain characteristic indicative of soil-water availability to plants, have been the most relevant environmental control for micro-habitat variability at the plot scale reported for the neotropics (Clark, 1995; Harms et al., 2001;
Normand, 2006; Phillips et al., 2003a; Pitman, 2001; Svenning, 1999; Tuomisto et al., 2003; Valencia et al., 2004; Vormisto, 2004). Topographic position was found to be more related to microhabitat variability in the Yasuni 50 Ha plot in
Ecuador (Svenning, 1999) as demonstrated as well by Valencia et al., (2004).
Slope relates positively to habitat association in the Barro Colorado 50 Ha plot in
Panama (Harms, 2001). It is interesting to note that parallel findings are found in
TBS with respect to slope position and slope, Furthermore, the results confirm
219 that elevation and topography are the most significant terrain controls over the distribution of taxa. The following sub-sections illustrate the results taxa by taxa using the K-S test and frequency distribution analysis.
6.2.3.1 Arecaceae
Kolmogorov-Smirnov test
Iriartea deltoidea occupies a significant different subset of terrain values for seven of eight variables (compared with a random distribution) whilest
Astrocaryum chambira show significance only with four of eight (Table 18).
Table 18 Statistic and p-value using Kolmogorov-Smirnov test for Astrocaryum chambira (left) and Iriartea deltoidea (right) related to eight terrain characteristics in TBS. Statistical significance (p<0.05) are highlighted in grey.
220 The fact that Iriartea deltoidea is significant to all the terrain characteristics except northness, indicates that it is a wide environmentally adapted palm as demonstrated also by the chapter 5 spatial distribution results. In contrast, curvature, elevation, slope and slope position are the relevant factors for
Astrocaryum chambira (Table 19) suggesting that it is more specifically affected by topographical features.
6.2.3.1.1 Astrocaryum chambira
Frequency distribution and difference in mean
According to Henderson et al., (1995) Astrocaryum chambira prefer habitats with non-inundated soils, and grow more commonly in disturbed areas, at lower elevations. At least, with respect to elevation, the results of this chapter are in agreement with the Henderson et al., (1995) discovery. In contrast, soil moisture is consider also as the main environmental control variable for palm species distribution in Peru (Normand, 2006). A specific case is documented by Clark et al., (1995) where two Astrocaryum species are abundant in swamps at La Selva,
Costa Rica.
These kinds of inconsistencies demonstrate that either different field methodologies or analysis approaches may be affecting the patterns and their interpretation – or that the sites are fundamentally different or the scales of analysis are different or that the DEM used is only weakly representative of terrain or represents terrain at a different scale to that used in plot scale studies.
221 Another possible explanation would be the fact that similar habitats can have contrasting features when located in different geographical locations, for example, as demonstrated in several plot case studies in Iquitos -Peru- and
Yasuni National Park –Ecuador- (Svenning and Wright, 2005; Vormisto et al.,
2004).
Table 19 provides a summary of values for each terrain variable.
Table 19 Summary of the mean values for Astrocaryum chambira
This demonstrates that Astrocaryum chambira prefers valleys rather than planar slope segments, with a specific tendency to be located on low angle slopes with a mean value of 4.18 degrees (Figure 64). Further investigation suggests that the difference in mean curvature is 4 % higher for the mapped mean compared to the random mean. With regard to elevation, where the difference in mean is 7 % higher for the mapped mean compared to the random mean, Astrocaryum chambira is distributed between 199 and 240 m, however they prefer elevations of 216 m. This observation illustrates that even small changes in elevation has an impact on the distribution of the taxa, which may carry significant ecological
222 implications. For example, higher elevations may be more exposed to a disturbance regime because of occasional windthrow while low areas could be more sheltered, of course the intensity of the impact would depend on the wind direction but in general open areas are under more disturbance. In contrast, the valleys tend to be more exposed to the flooding dynamics, which in terms of environmental influence makes a difference when comparing to the dry zones on the top of the ridges.
223
Figure 64 Frequency distribution for the difference in mean of the terrain variables between the mapped and random points for Astrocaryum chambira
224
In terms of slope position, A. chambira, shows a preference for flat areas which is in agreement with other studies in Ecuador and Peru respectively (Svenning,
1999; Vormisto, 2004). A chambira has a mean value of 57.7, where the difference in mean is 6 % higher for the mapped in relation to the random. These results suggest that A chambira occupies mid-slopes (Figure 65).
Figure 65 Profile diagram for A chambira (the palm with the green crown) showing slope position.
6.2.3.1.2 Iriartea deltoidea
Frequency distribution and difference in mean
Iriartea deltoidea is a very common lowland forest palm species (Clark et al.,
1995). It is reported that I deltoidea occupy a variety of habitats, and seem to be more commonly distributed along stream and river margins (Henderson et al.,
1995). It is interesting to note that Normand et al., (2006) reported Iriartea deltoidea to be more common along forest terraces, with no preferences either for wet or dry soil. In contrast, it did not show up the same preference according to Clark (1995), Svenning (1999) and Vormisto (2004).
225
Table 20 provides a summary of values for each terrain variable.
Table 20 Summary of the mean values for Iriartea deltoidea
I deltoidea has a mean elevation of 217 m (Figure 66) which is 1 m higher than A chambira (difference in mean is 7 % higher for the mapped in comparison to the random). This indicates that elevation does not seem to be an obvious variable in understanding the difference in distribution between A. chambira and I. deltoidea. However, ecologically speaking it could be seen to be relevant because the difference of 1 m in a low level elevation, could signify the difference between flooded and dry terraces, which are known to be a good indicator for species distribution and habitat preferences. If this is the case, it is probable to assume that elevation can be a controlling factor for both palm species at TBS.
226 The mean curvature for I deltoidea has a value of 0.036 (the difference in mean is 5 % higher for the mapped in relation to the random) (Figure 66), indicating that it prefers convex ridges. Regarding eastness, the tendency is east facing slopes of about +0.10 (difference in mean is 4 % higher for the mapped in relation to the random) (Figure 66). This result might be ecologically significant because the wind direction is usually easterly in TBS as mentioned by Jarvis
(2005). It appears logical to assume that eastness may have some relation to wind because I deltoidea is a canopy palm, which exposes it more openly to the wind.
Therefore, this indicates that the east facing slopes may be more susceptible to gap formation and the ingress of fast growing palm species (which tend to be more indicative of disturbed areas) causing an impact on the distribution of I deltoidea. As further evidence, solar radiation receipt is statistically significant, it is another reason to argue that “light” seems to be a relevant variable for I deltoidea spatial distribution because of the fact that the areas where the palms are found match with the zones that present more average greater potential solar radiation receipt in TBS.
In solar radiation, the difference in mean is 5 % higher for the mapped in relation to the random. Because the potential solar radiation is greater than 9000 W/m²/yr
(Figure 66), these values can be considered to be high, supporting the theory that
I deltoidea is a palm which is well adapted to positions of high light exposure.
227
228
Figure 66 Frequency distribution for the difference in mean of the terrain variables between the mapped and random points for Iriartea deltoidea
229 Topographic characteristics such as slope and slope position are significant variables for I deltoidea. The mean value for slope position in TBS is about 63, which means that I deltoidea at TBS occupies upper mid-slopes (Figure 67).
Valencia (2004) reports that Iriartea deltoidea prefers mid-slopes. In support of this, I deltoidea was determined as an omnipresent taxa occupying all the topographic positions, as demonstrated in La Selva Biological Station- Costa
Rica (Clark et al., 1995) but at the same time it did not show an association with topography. In general, our results are in agreement with the literature about
Amazonian environments but the central America forests seems to shows a different topographic position preference.
Figure 67 Profile diagram for I deltoidea (the palm with the red crown) showing slope position.
6.2.3.2 Fabaceae
Kolmogorov-smirnov test
As can be seen from Table 21, both genera are influenced by different environmental variables. Parkia is just significantly distributed in relation to
230 elevation, while Inga has shown preferences for northness, slope position and solar radiation.
Table 21 Statistic, p-value and Kolmogorov-Smirnov test for Inga (left) and
Parkia (right) related to nine terrain characteristics in TBS. Statistical significance (p<0.05) is highlighted in grey.
Parkia shows little evidence of environmental control, and is well spatially dispersed in the landscape. This result supports an argument that Parkia is a unique taxa type with restricted environmental adaptability, which may require a particular habitat to survive in the TRF. Inga in contrast has some level of
231 clumped spatial aggregation in the landscape (see chapter 5). This observation suggests that Inga could be controlled by a mixture of biotic aspects and some environmental conditions.
According to Clark et al., (1999) and Cattanio et al., (2002) several Inga species have shown a relation to topographic position in neotropical forest, this research is in support of the current literature as Inga has demonstrated that it prefers habitats located on mid-slopes. For example, Inga fagifolia, a Central American species from the TRF in Luquillo, Puerto Rico is shown to be “more frequent on ridges and slopes than valleys” (Basnet, 1992). Studies conducted in Yasuni
National Park, Ecuador, report that Inga auristella has a preference for mid- slopes over ridges and valleys (Valencia et al., 2004). In relation to Parkia, the situation is different because elevation is the only significant characteristic controlling it.
6.2.3.2.1 Inga
Frequency distribution and difference in mean
Table 22 provides a summary of values for each terrain variable.
Table 22 Summary of the mean values for Inga
232
As can be seen in Figure 68, the slope position value for Inga is 51.25, as previously mentioned this means that it is location in the mid-slopes.
Figure 68 Profile diagram for Inga showing slope position.
With regard to solar radiation, it can be mentioned that values up to 9000 are suggesting a high potential solar radiation receipt, here particularly; the mean value obtained was 9615 W/m²/yr (Figure 69).
233
Figure 69 Terrain controls for Inga (Fabaceae).
234 6.2.3.2.2 Parkia
Table 23 provides a summary of values for each terrain variable.
Table 23 Summary of the mean values for Parkia
Parkia appears to only be influenced by elevation, with a mean value of 216 m
(Figure 70). Looking in detail, the difference in mean is 7% higher for the mapped compared to the random. This may carry an ecological significance because there is a difference of nearly 3m between mapped and random which is proportionally relevant. Considering the fact that the elevation gradient for this genus varies between 213 and 216 m, the Parkia could be considered to be restricted considering the fact that the elevation range goes from 193 to 270 m for the all TBS and 190 to 245 for the mapped section.
Figure 70 Terrain controls for Parkia (Fabaceae).
235 6.2.3.3 Meliaceae
Guarea is controlled by the following topographic characteristics: elevation, slope and slope position (Table 24). The spatial distribution for Guarea could generally be described to be clustered.
Kolmogorov-smirnov test
Table 24 Statistic, p-value and Kolmogorov-Smirnov test for Guarea
(Meliaceae) related to nine terrain characteristics in TBS. Statistical significance
(p<0.05) is highlighted in grey.
236 6.2.3.3.1 Guarea
Frequency distribution and difference in mean
Table 25 provides a summary of values for each terrain variable.
Table 25 Summary of the mean values for Guarea
The range of elevation for Guarea in TBS is reduced (197-237 m considering that the elevation for TBS range from 193 to 270 m, and the elevation range for the mapped section goes from 190 to 245 m. however the mapped mean is 11 % higher than the random. This means an elevation difference of 4.3m, equivalent to 218m (Figure 71), which within a small elevation range would carry an ecological significance. With regard to slope, there is a difference of 0.86 degrees of inclination between the mapped and the random. Consequently if they both show a statistically significant value, this might indicate the existence of a strong correlation between the variables.
One case study in the Puerto Rico rain forest, found a strong correlation between
Guarea guidonia and ridges-slopes, as well as a preference for mid elevation for
G. guidonia, with more association to valleys (Basnet, 1992) . Although this generalisation is complicated, to some extent our results are in agreement, however, there are still inconsistencies, for example, Guarea khuntiana in the
Central Brazilian valley forest, MatoGrosso was found to correlate to stream
237 habitats (Pinto et al., 2005). In this situation, it is not wise to generalise but there is enough evidence to suggest that changes from region to region exist and also that within regions the trends are more likely to be similar.
Figure 71 Terrain controls for Guarea (Meliaceae).
238 Apart from elevation, topography also plays a relevant role in controlling the distribution of Guarea and is expressed in terms of inclination and slope position. The mean slope position is 8 % higher for the mapped over the random mean. This percentage is equivalent to saying that Guarea prefers a topographical position in mid-slopes with a value of approximately 60 (Figure
72).
Figure 72 Profile diagram for Guarea showing slope position.
Taking into consideration our terrain evidence here in addition to the literature about seed dispersal syndromes and the clustered spatial distribution, (as shown in chapter 5) it may be correct to assume that Guarea as Inga are more controlled by biotic factors than terrain or environmental characteristics.
6.2.3.4 Moraceae
Kolmogorov-smirnov test
Cecropia and Pourouma are interesting cases because they show the second most statistical significance after palms. Elevation, northness, slope and slope position are significant variables for Cecropia, whereas Pourouma only relates to elevation and slope position (Table 26). Elevation and slope position are common variables but slope and northness make a difference for Cecropia. This
239 result may suggest that Pourouma could be more restricted taxa in terms of environmental tolerance. In contrast Cecropia may be a taxa which occupies a broader range of habitats.
Table 26 Statistic, p-value and Kolmogorov-Smirnov test for Cecropia (left) and
Pourouma (right) related to eight terrain characteristics in TBS. Statistical significance (p<0.05) is highlighted in grey.
240 6.2.3.4.1 Cecropia
Frequency distribution and difference in mean
Table 27 provides a summary of values for each terrain variable.
Table 27 Summary of the mean values for Cecropia
Cecropia prefer topographic positions in mid-slopes (Figure 73), and also show a difference in mean for slope position about 4.57 % higher for the mapped than the random, which could carries an ecological connotation. The difference in mean for Pourouma is found to be 9% that is nearly twice the value of Cecropia.
Pourouma prefers topographical positions with mid-slopes as illustrated in figure
84, this result confirm that Pourouma have a preference for areas with more topographic inclination in relation to Cecropia, which is more commonly located in flatter areas.
241
Figure 73 Slope position representation for Cecropia (Top) and Pourouma
(Bottom).
The minimum and maximum elevation range for Cecropia are 195 to 240 m with the mapped mean 6 % (216 m) higher than the random mean (Figure 74) demonstrating that Cecropia is located at a lower elevation than Pourouma.
Concerning northness, there is a small but statistically significant difference between the random and mapped taxa. The value is 0.33 for the mapped taxa, which is higher than the random. This means that south-facing slopes are receiving the greatest amount of solar radiation.
242
Figure 74 Terrain controls for Cecropia (Moraceae).
6.2.3.4.2 Pourouma
Frequency distribution and difference in mean
Table 28 provides a summary of values for each terrain variable.
243
Table 28 Summary of the mean values for Pourouma
Cecropia prefer a lower elevation and a flatter topographic position.While
Pourouma prefer higher elevations and topographic positions than Cecropia. The mean elevation value for Cecropia is 216 m in comparison to Pourouma, which is 217 m (Figure 75). These represent a difference in mean of 9 % higher for the mapped than the random. This means about 3.75 m of difference of elevation between the two, making a difference of 1 m with respect to Cecropia.
244
Figure 75 Terrain controls for Pourouma (Moraceae).
6.2.3.5 Myristicaceae
Kolmogorov-smirnov test
As can be seen in table 29, elevation, slope position and solar radiation appear to influence the distribution of Myristicaceae- Otoba. This might be interesting to note that Otoba is statistically significant for nearly all the same variables as
Astrocaryum, and Inga (Table 29).
245
Table 29 Statistic and p-value using F, t and Kolmogorov-Smirnov test for
Otoba (Myristicaceae) related to nine terrain characteristics in TBS. Statistical significance (p<0.05) is highlighted in grey.
6.2.3.5.1 Otoba
Frequency distribution and difference in mean
Table 30 provides a summary of values for each terrain variable.
Table 30 Summary of the mean values for Otoba
Figure 76 represents the location of Otoba in terms of slope position. The interesting observation here is that it has the second highest mean value (61.7) in relation to the other taxa, with the exception of the palm Iriartea, which shows a value of 62.9. This type of case provides valuable information in support of the
246 possibility of the sharing of ecological habitats. The meaning of this finding is that Otoba prefers the upper part of the mid-slopes as illustrated in figure 87.
Figure 76 Profile diagram for Otoba showing slope position.
The mapped mean elevation is 12 % higher than the random (Figure 77), this variation signifies that there was a difference of 3.85 m in elevation, which may have an ecological implication as confirmed for most of the other taxa. Solar radiation is a complex variable to interpret, however it seems that Otoba has a similar solar receipt to Inga or Iriartea, with a value of about 9624 W/m²/yr.
247
Figure 77 Terrain controls for Otoba (Myristicaceae).
6.2.3.6 Sapotaceae
Kolmogorov-smirnov test
Northness and slope are the only variables that have an impact on the distribution of Pouteria (Table 31).
248
Table 31 Statistic and p-value using F, t and Kolmogorov-Smirnov test for
Pouteria (Sapotaceae) related to nine terrain characteristics in TBS. Statistical significance (p<0.05) is highlighted in grey.
6.2.3.6.1 Pouteria
Frequency distribution and difference in mean
Table 32 provides a summary of values for each terrain variable.
Table 32 Summary of the mean values for Pouteria
Northness is the only variable captured by Pouteria (Figure 78). Apart from northness, slope is also a significant variable (Figure 78). The fact that Pouteria was significant with aspect (northness) and slope, is suggesting that solar radiation might be an important control.
249
Figure 78 Terrain controls for Pouteria (Sapotaceae).
Because of the fact that most of the statistically significant factors in this analysis are related to topography, perhaps it is not a surprise to find out that many of these terrain variables are collinear or dependent on each other, as demonstrated by Jarvis (2005). Although a full explanation has not been obtained, this observation suggests that there would be a mixture of biotic and environmental factors controlling the distribution of taxa in the landscape.
The final analysis is an attempt to use these mapped taxa to assess some measure of tree diversity at the landscape scale.
250 6.2.4 DIVERSITY ANALYSIS
The basic idea of this section is to measure the diversity (richness related) of taxa in windows of 250 and 500 m and the relative diversity of taxa observed to the diversity of elevation using the same windows. Since the purpose of the thesis is to better understand landscape scale distributions of tree diversity, the final analysis calculates a 125 and 250m radius for each point in the mapped area and calculates the number of taxa observed: the taxa diversity.
These maps allow us to identify the potential local diversity “local hotspots” for the ten taxa within TBS. This approach of calculating a window-diversity (the diversity within a given radius) is then applied to the integerised DEM in order to map landscape diversity (and thereby see any relationship with taxon diversity).
6.2.4.1 Tree richness
The areas in red in Figure 92 indicate the higher average number of taxa. These areas include seven of the ten mapped taxa. Comparing the 250m window (top
Figure 79) against the 500 m window map (bottom figure 79), the main “local landscape hotspots” are located in the northeast part of the studied area. The areas on lower elevations present fewer taxa, whilst upper elevations present higher number of taxa. It is important to note that the more the scale of analysis is reduced (from 500 m to 250 m diameter), the smaller the “local hotspot” gets.
These hotspot areas could potentially be more relevant for conservation and management because they contain the majority of the mapped taxa. There are
251 some spots distributed in other areas (green color), they contain between four and six of the ten mapped taxa. In contrast, the areas on the northwest and southwest have four or less than four taxa.
Figure 79 Diversity maps (richness related) for 10 tree taxa at TBS. 250 m diameter map (top) and 500 m diameter map (bottom)
252 6.2.4.2 Tree diversity in relation to terrain
As was mentioned, elevation is the variable with significant influence on controlling the distribution of the mapped taxa (and is the fundamental basis of all of the other derived terrain variables). Figure 80 suggests that the areas with more taxa are located on ridges or mid-slopes mainly (index values from 0,55 to
0,8) and usually higher than 220 m.
Figure 80 Diversity of taxa in relation to elevation at TBS
One of the aims is to explore the possibility to use aerial mapping for conservation and management purposes. On the basis of these analyses a local hotspot map for TBS is suggested (Figure 81). The areas with green and red colour on the diversity maps may be important areas to set up tree diversity
253 mapping plots. The analysis also indicated that even in this apparently uniform forest the precise location of any diversity plot can have significant implications for both the resulting measure of species richness and the species composition.
Figure 81 Local hotspots map for TBS
6.3 CONCLUSIONS
This chapter has investigated using aerial photography and terrain characteristics in order to better understand the distribution of some canopy tree taxa. In TBS, a number of statistically significant results were found between landscape properties and the distribution of key taxa. For example, elevation, slope position and slope are the most significant factors controlling the distribution of key taxa at TBS, while solar radiation and northness were less statistically significant. In a general sense, these outcomes may signify the presence of strong environmental
254 controls. Given this evidence it is reasonable to assume that terrain characteristics are important factors in influencing the distribution of key taxa at landscape scale (0 - 2 km approx) in an Amazonian lowland forest in eastern
Ecuador, particularly Tiputini Biodiversity Station.
With regard to the 10 taxa analyzed, it should be mentioned that different degrees of statistical significance were reported, for example the terrain characteristics that had the most influence on the distribution of the key taxa were elevation, slope, slope position and mean curvature. This statistical significance suggests that environmental differences could serve as a good quality predictor in relation to terrain at landscape scale. The lack of statistical significance of some of the variables might be attributed to the fact that the 25m cell size TOPO DEM is not detailed enough to capture some landscape-terrain features, such as those which are water related such as TopModel. This topographical artifact might be linked to the reality that aerial photographs produce a map of the canopy level topography not the ground level topography, which makes it appear smoother.
Since the topographic maps from which the topo DEM is constructed are derived from these aerial photographs, the terrain will be somewhat smoothed with respect to the reality.
When taxa are analysed to explain the relationship between composition and landscape properties, the most statistical significance is found in taxa such as palms (Iriartea deltoidea and Astrocaryum chambira), Moraceae (Cecropia and
Pourouma), Fabaceae (Inga) and Meliaceae (Guarea). In contrast, Sapotaceae
(Pouteria), Myristicaceae (Otoba), Fabaceae (Parkia) and Bombacaceae (Ceiba)
255 are the least significant taxa. Astrocaryum chambira and Iriartea deltoidea are better explained by mean curvature, elevation, slope and slope position. Inga,
Otoba, Cecropia and Guarea are affected by elevation, slope and slope position.
While the least statistically significant taxa, Parkia-Pourouma-Pouteria, are explained more by elevation, northness, slope and slope position.
When aerial mapping is analysed. TOPO DEM was not able to detect effectively the influence of terrain characteristics such as soil moisture, northness, eastness and mean curvature for the distribution of taxa at TBS. Evidence in support of this argument is the information that mean curvature just appears to be significant in one taxa, which is clearly suggesting that TOPO DEM is not very efficient at recognising how concave or convex an area of terrain is. This observation may be related to some discoveries in Tabonuco (Puerto Rico) where the greatest number of stems were found to be located on ridges and slopes
(Basnet, 1992) compared with valleys. Basnet explains this by arguing, “ridges are more protected sites than valleys because shallows and bedrock on ridges provide support and anchorage to the roots of the trees”. On the other hand,
TOPO DEM did not capture soil moisture. Coincidentally, Basnet’s evidence is in agreement with our patterns about topographic features being the most statistically significant controls.
Overall, the results of this chapter suggest that the distribution of key taxa in the landscape is related to some degree, to terrain characteristics at TBS. However, it cannot be stated that a single pattern exits, and the terrain variables used in this study certainly do not explain all the complexities of the distribution of taxa. This
256 signifies that either the variables themselves are not capturing the real environmental conditions, or the spatial distribution of key taxa is controlled by unmeasured factors that cannot be explained using the TOPO DEM. Since many of these terrain variables are collinear, it will be useful to explore further the correlation between them in further studies. Finally, the diversity maps identified key areas within TBS having a greater number of observed taxa. These areas could be considered as local hotspots, which can be further explored for inventory, research and conservation purposes.
257 CHAPTER 7: FINAL CONCLUSIONS
The aim of this thesis is to develop a manual/visual identification technique suitable for the characterisation of tree genera and families from colour airborne aerial photography. The rationale for the development of such methods is to expand the analysis of tree taxa distributions from the current emphasis on small plot scale studies to an emphasis on the type of landscape scale studies that are only possible through aerial photographic analysis. Using taxa distribution datasets generated, this thesis has attempted to understand (a) the patterns of landscape-scale spatial distribution of key taxa and (b) the relationships between their distributions and various terrain characteristics that may control the distribution of trees in lowland Amazonian forest, Ecuador.
Aerial identification keys are developed on the basis of an objective determination of individual crown properties and the subsequent development of per-taxa property signatures as an ‘objective approach’. A subjective approach is also used in which a user identifies taxa on the basis of a dichotomous key in which a series of traits for that taxon are examined one by one to arrive at an identification (Objective 1, 2, 3, 4 and specific objectives a, b, c, d). The exclusivity index (the exclusivity of particular properties to particular taxa) has been useful for understanding some aspects of crown property variability for classification and identification. The subjective and semi-objective concepts are useful for the development of a manual/visual crown identification technique.
Three of six crown properties (crown shape, crown type and foliage texture) were defined as the most suitable and reliable characteristics for splitting taxa
258 into taxonomic groups (Objective 1). Usually, foliage texture is the best means of identification and separation of taxa. Grouping properties as “signatures” in a hierarchical way is an appropriate approach to classify them.
An online identification key was produced based on a hierarchical (subjective) examination of various crown properties to arrive at an identification. When this key is tested with 100 users an overall identification accuracy of 50 % has been obtained, that is half of the responses are correct identifications and half are not.
More specifically, Astrocaryum chambira and Iriartea deltoidea present an identification success > 90 %, whilst Inga, Parkia and Cecropia have had approximately 40 - 60 % of identification accuracy (Objective 2, 3 and 4). These results alone present sufficient evidence to state that aerial identification is possible but is not equally accurate for all taxa. There are key aspects that may contribute to improve ID accuracy; the quality of the imagery (spatial resolution) and the image geometry, (the less distorted the imagery the better). Another suggestion would be the reduction of shadows because homogeneous imagery
(without high brightness) better reflects the textural forms and shapes of the upper crown and therefore can be more useful to identify individual crowns.
When it comes to ground validation of identifications, the improvement of image georeferencing is a key aspect that could be very useful to improve image quality because it makes the ground based accuracy higher. Clearly the experience of the user is also critical to identification accuracy. The wide range of users who took part in the validation exercise likely had very different accuracies but unfortunately in order to make the system as simple as possible for users, the online system used was not able to identify the origin of particular responses.
259
When spatial patterns in the landscape distribution of key taxa are examined. 90
% of the mapped taxa present a clustered distribution, with exception of Ceiba, which is dispersed (Objective 5 and specific objective e). According to the
Ripley-K spatial aggregation measure, clustered patterns were identified for 90
% of the taxa. This analysis demonstrates that aerial identification can be a useful technique for understanding spatial patterns beyond the plot scale. A valuable contribution of this thesis is the fact that using a non plot-based mapping approach enables linkage to a GIS-based terrain analysis for understanding landscape controls on taxon distributions.
When the spatial distribution of key taxa in relation to terrain characteristics is examined some terrain controls were found to be important (Objective 6 and specific objective f). Three of eight terrain variables (elevation, slope, and slope position) play an active role in the distribution of approximately 50% of the mapped taxa. Variables such as curvature, eastness, northness solar radiation and soil moisture influence taxon distributions less and for approximately 30 % of the analysed taxa. The spatial distribution of the remaining taxa is unexplained by any of the terrain characteristics used in this research.
When diversity analysis are examined. Three maps were obtained that allowed sections to be identified that could be considered as “local tree diversity hotspots”. The north-eastern part of TBS seem to be the most relevant in terms of average number of taxa. A map for conservation and management is proposed on the basis of the diversity analysis. In conclusion, the aerial mapping is a potential
260 technique for developing new tree diversity assessments at larger scales than plot based sampling.
This results leads to the possibility that a combination of equilibrium (niche differentiation) (Phillips et al., 2003a) and non-equilibrium theories (balance of extinction and immigration) (Hubbell and Foster, 1986) processes might be controlling the spatial distribution of key taxa in the landscape at TBS, Ecuador.
Similar findings have been reported previously by Jarvis (2005a) at the plot scale
(25 × 25 m) in the same study area.
Overall this thesis provides encouraging results supporting the idea that the spatial distribution of key taxa are in some way related to certain terrain characteristics, at least over an area of up to 2 km. Although aerial mapping would not expect to replace the traditional ground-based identification techniques, in general, aerial mapping plus terrain analysis and spatial distribution have shown themselves to be a potentially useful technique and also, to some extent, help to explain some environmental correlations and further help to understand key aspects about the influence of biotic/abiotic aspects on the spatial distribution of trees in one of the most diverse tropical areas in the neotropics.
Such innovative techniques have the potential to be applied for better forest management and conservation planning. However, there are some difficulties, for example, the constraints of aerial mapping for capturing the full taxonomic composition around TBS is evident, because aerial photography views only
261 canopy and sub-canopy trees and only a subsample of these are sufficiently structurally differentiated as to be identifiable. Therefore, a full understanding of the diversity of tree species on ground cannot be investigated through this method, however a high diversity of canopy crowns may relate to a high diversity of trees in general.
7.1.1 General limitations
The main limitations in this analysis so far have been as follows: imagery characteristics, DEM quality, ground sampling, crown properties descriptions and crown properties quantification. Apart from the technical issues, the principal limit is the diversity of tree species encountered in an Amazonian context. In relation to practical issues, one of the difficulties of the aerial technique has been to locate on the ground, the tree crowns observed in imagery, which might be related to errors in georeferencing, topographic variation and human spatial location skills. Secondly, to obtain a standard crown topology is complex due to the high diversity of crown surface characteristics. Regarding image characteristics, dealing with different conditions such as shading, brightness and view angle makes the visual classification difficult. Finally, DEM quality is always a limitation because of the capacity to capture certain terrain features in a forested environment where the elevational differences are small.
This has implications for all derived terrain variables.
7.1.2 Future Research Avenues
There are two questions that come up at this stage: (1) what would be done differently if the thesis were to start now and (2) what are the potentially fruitful future avenues for research in this field? Although this research has produced
262 interesting results and conclusions, there are some aspects, which should be taken into account for further improvements for future applications.
1. To refine and produce new crown property descriptors, particularly for
the random textures, which usually fail in ID accuracy. The idea would be
to focus on developing a new crown typology just for the Amazonian
trees.
2. To investigate a new way of quantifying crown properties because the
Exclusivity Index seems to be more appropriate for crown properties
classification rather than an effective quantification technique.
3. To evaluate how the method would perform in different field sites where
data are available, for example, in central Brazil
4. To translate the taxon ID rules for those with the highest ID accuracy to
an appropriate computer algorithm in order to develop semi-automated
identification systems
5. To conduct a large scale identification test in a massive image repository
such as Google Earth based on the most reliable identifications
6. To certify the identification key under international standard parameters,
for example, the EDIT project (http://www.e-taxonomy.eu/) which aims
to identify cyber-taxonomy tools that can be used for future online
applications
7. To produce detailed digital elevation models with better sensitivity to the
types of terrain features which exist in these environments
263 8. To link the aerial composition estimation to ground based species using
diversity existing dataset in order to explore if canopy diversity represent
ground tree diversity
9. To create a research network in order to socialise the new concepts on
aerial taxonomy
10. To plan an expedition to TBS in order to validate the diversity maps,
particularly sampling trees on the areas that were identified as local tree
diversity hotspots.
Finally the most interesting future avenue would be to make attempts at semi- automatisation of the technique on the basis of empirical visual/object crown identification. Ultimately, to identify and measure the crowns using CASI or
LIDAR data and the eCognition software because a more quantitative analysis might be produced. Thinking even further to implement the same ideas but using the new generation of commercial satellite such as QuickBird and now
Worldview II which will provide finer and finer resolution imagery of the similar resolution to the aerial photography used in this thesis.
264
REFERENCES
Alford, M., Hailstone E and J, H., 1974. The measurement and mapping of land resource data by point sampling on aerial photographs In: E.C. Barret and L.F. Curtis (Editors), Environmental Remote Sensing: Applications and achievements Edward Arnold London.
Arango, d.V., 1972. Introduccion a la Dendrologia de Colombia. Universidad Nacional de Colombia, Medellin, 351 pp.
Asner, G.P., Braswell, B.H., Schimel, D.S and Wessman C.A, 1998. Ecological research needs from multiangle remote sensing data. Remote Sensing of Environment, 63(2): 1-11.
Atkinson, P.M., 1993. The effect of spatial resolution on the experimental variogram of airborne MSS imagery. International Journal of Remote Sensing, 14(5): 1005-1011.
Ausgspurger, C.K., 1983. Phenology, flowering syncrony and fruit set of six Neotropical shrubs Biotropica, 15(4): 257-267.
Basnet, K., 1992. Effect of Topography on the Pattern of Trees in Tabonuco (Dacryodes excelsa) Dominated Rain Forest of Puerto Rico. Biotropica, 24(1): 31-42.
Bawa, K.S. and Filliph, S.P., 1990. Phenology-Commentary. In: B.A. Handley (Editor), Reproductive Ecology of Tropical forest plants. MAB- UNESCO, Paris, pp. 17-20.
Berg, 2004., Moraceae, Flowering plants of the neotropics. Princeton University Press, New Jersey, pp. 253-254.
Birnbaum, P., 2001. Canopy surface topography in a French Guiana forest and the folded forest theory. Plant Ecology, 153: 293-300.
Bodmer, R.E., 1990. Fruit patch size and frugivory in the lowland tapir (Tapirus terrestris). Journal of Zoology London, 219: 457-467.
Bongers, F., 2001. Methods to assess tropical rain forest canopy structure: an overview. Plant Ecology, 153: 263-277.
Brady, N. and Field, D.J., 2000. Local contrast in natural images: normalisation and coding efficiency. Perception, 29: 1041-1055.
Brandtberg, T., 2002. Individual tree-based species classification in high spatial resolution aerial images of forests using fuzzy sets. Fuzzy Sets and Systems, 132(3): 371-387.
265 Brandtberg., T. and Walter., F., 1998. Automated delineation of individual tree crowns in high spatial resolution aerial images by multiple-scale analysis. Machine Vision and Applications, 11: 64-73.
Britannica Concise Encyclopedia. Britannica Concise Encyclopedia. 2006 Encyclopædia Britannica, Inc. All rights reserved. http://www.answers.com/topic/pixel
Brokaw N. and Busing, R.T., 2000. Niche versus chance and tree diversity in forest gaps. Trend in Ecology and Evolution, 15(5): 183-188.
Burnett, M.R., P. V. August, J. H. Brown and Killingbeck, K.T., 1998. The influence of geomorphological heterogeneity on biodiversity. I. A patch scale perspective. Conservation Biology, 12: 363-370.
Burslem, D.F., C., G.N. and Thomas., S.C., 2001. Enhanced: Tropical Forest Diversity- The plot thickens. Science, 291(5504): 606-607.
Campbell, D.G., 1994. Scale and patterns of community structure in Amazonian forest. In: P.J. Edwards, May R.M and Webb N.R (Editor), Large-scale ecology and conservation biology. Blackwells, Oxford, pp. 179-197.
Carate, D., 2006. Phenological aspects of Amazonian Rain forest trees, Yasuni National Park, Ecuador Universidad Catolica Quito, 140 pp.
Cattanio, J.H., Anderson, A.B. and Carvalho, M.S., 2002. Floristic composition and topographic variation in a tidal floodplain forest in the Amazon Estuary. Revista Brasileira de Botanica, 25(4).
Chapman, C. A. 1995. Primate seed dispersal: coevolution and conservation implications. Evolutionary Antropology, Vol 4, pp 73-110
Chazdon, R.L., Robert K. Colwell, Julie. S. Denslow, 1999. Tropical tree richness and resource-cased niches. Science, 285: 1459a.
Chirimuuta, M., Clatworthy, P.L. and Tolhurst, D.J., 2003. Coding the contrast in natural images by visual cortex (v1) neurons: a Bayesian approach. Journal of the Optical society of America A. Optics, Image Science, and Vision, 20(7): 1253-1260.
Clark, D.A., 1998a. Deciphering landscape mosaics of neotropical trees; GIS and systematic sampling provide new views of tropical rainforest diversity. Annals of the Missouri Botanical Garden, 85: 18-33.
Clark, D.A., Clark, D B., Sandoval, R. M & Castro Vinicio M. C, 1995. Edaphich and human effects on landscape-scale distributions of tropical rain forest palms. Ecology, 76: 2581-2594.
266 Clark, D.B., Deborah, C. and Read, J.M., 1998. Edaphic variation and mesoscale distribution of tree species in a neotropical rain forest. Journal of Ecology, 86: 101-112.
Clark, D.B., Palmer, M. and Clark, D.A., 1999. Edaphic factors and the landscape-scale distributions of tropical rain forest trees. Ecology, 80(8): 2662-2675.
Clark, C. J., Poulsen J. R, Bolker B.M, Connor E. F and Parker V. T. 2005. Comparative seed shadows of birds-monkey-, and wind-dispersed trees.
Clatworthy, P.L., Chirimuuta, M., Lauritzen, J.S. and Tolhurst, D.J., 2003. Coding the contrasts in natural images by populations of neurons in primary visual cortex (V1) Vision Research, 43: 1983-2001.
Condit, R., Peter Ashton, Sarayudh Bunyavejchewin, Dattaraja H. S, Davies S, Esufali S, Ewango C, Foster R, Gunatilleke I.A, Gunatilleke C. V, Hall P, Harms K. E, Hart T, Hernandez C, Hubell S, Itoh A, Kiratiprayoon S, LaFrankie J, Loo de Lao S, Makana J-R, Nur Supardi Noor Md, Kassim A. R, Russo S, Sukumar R, Samper C, Suresh H. S, Tan S, Thomas S, Valencia R, Vallejo M, Villa G and Zillio T. 2006. The importance of demographic niches to Tree diversity. Science, 313: 98-101.
Condit, R., Hubbell S. P, LaFrankie J. V, Sukumar R, Manokaran R, Foster R. B and Ashton P. S. 1996. Species-area and species-individual relationships for tropical trees: a comparison of three 50-ha plots. Journal of Ecology, 84: 549-562.
Condit, R., Ashton P. S, Baker P, Bunyavejchewin S, Gunatilleke S, Gunatilleke N, Hubbell S. P, Foster R. B, Itoh A, LaFrankie J.V, Lee H. S, Losos E, Manokaran N, Sukumar R and Yamakura T. 2000. Spatial Patterns in the Distribution of Tropical Tree Species. Science, 288: 1414-1418.
Condit, R., Pitman, Nigel C.A., Leigh, Jr., Egbert Giles, Chave, Jerome, Terborgh, John W., Foster, Robin B., Nunez Vargas, Percy, Aguilar, Salomon, Valencia, Renato, Villa Munoz, Gorky , Muller-Landau, Helene C., Losos, Elizabeth C. and Hubbell, Stephen P. 2002. Beta- diversity in tropical forests. Science, 295: 666-669.
Contreras, W.F., E. Gibaja Galindo, A. Bailon Morillas and P. Moral Lorenzo. 2003. An application of experts systems to botanical taxonomy. Expert Systems with Applications, 25: 425-430.
Croat, T., 1978. Flora of Barro Colorado Island. Stanford University Press, Stanford.
Cuervo, P., 2002. Modelling palms distribution in the Ecuadorian Amazon using GIS techniques, Kin'g College London, London.
267 Dalling, J.W., Stephen P. Hubbell and Silvera, K., 1998. Seed dispersal, seedling establishment and gap partitioning among tropical pioneer trees. Journal of Ecology, 86(4): 674-689.
Damschen, E.I., Haddad, N.M., Orrock, J.L., Tewksbury, J.J. and Levey, D.J., 2006. Corridors increase plant species richness at large scales. Science, 313: 1284-1286.
Defler, T.R. and Defler., S.B., 1996. Diet of a group of Lagothrix lagothricha lagothricha in southeastern Colombia. International Journal of primatology, 17: 161-190.
Di Fiore, A., 2004. Diet and feeding ecology of Woolly Monkeys in a western Amazonian Rain Forest. International Journal of Primatology, 25(4): 767- 801.
Diaz, S. and Cabido, M., 1997. Plant Functional types and ecosystem function in relation to global change. Journal of Vegetation Science, 8: 463-474.
Dodson, C. and Gentry, A.H., 1978. Flora of the Rio Palenque Science Center. Selbyana, 4: 1-628.
Duckworth, J.C., Kent, M. and Ramsay, P.M. 2000. Plant functional types: an alternative to taxonomic plant community description in biogeography? Progress in Physical Geography, 24(4): 515-542.
Ebach, M.C., 2007. What is Cybertaxonomy? European Distributed Institute of Taxonomy.
Edwards Marion and Morse, D.R., 1995. The potential for computer-aided identification in biodiversity research. Tree, 10(4): 153-158.
Erikson, M., 2004. Species classification of individually segmented tree crowns in high resolution aerial images using radiometric and morphological image measures Remote Sensing of Environment, 91: 469-477.
FAO, 1997. State of the world's forests, Rome.
Ferreira, L.V., 2000. Effects of flooding duration on species richness, floristic composition and forest structure in river margin habitat in Amazonian blackwater floodplain water forest: implications for future desing of protected areas. Biodiversity and Conservation, 9: 1-14.
Fleming, T.H. and Heithaus, E.R., 1981. Frugivorous Bats, Seed Shadows, and the Structure of Tropical Forests. Biotropica, 13(2): 45-53.
Fosberg, F., 1950. Ecological Notes on the Upper Amazon. Ecology, 31(4): 650- 653.
268 Fosberg, F.R. and Sachet, M., 1965. Manual for tropical herbaria. I.B.P.T. and N. Regnum Veget., 39: 1-132.
Fragoso, J.M.V., 1997. Tapir-generated seed shadows: scale-dependent patchiness in the Amazonian rain forest. Journal of Ecology, 85: 519-529.
Franklin, S.E., R.J. Hall, L.M. Moskal, A.J. Maudie and M.B. Lavigne, 2000. Incorporating texture into classification of forest species composition from airborne multispectral images. International Journal of Remote Sensing, 21(1): 61-79.
Gaston, K.J. and May, M.R., 1992. Taxonomy of taxonomist. Nature, 356: 281- 282.
Gaston, K.J. and Willimans, P.H., 1993. Mapping the world's species- the higher taxon approach. Biodiversity Letters, 1: 2-8.
Gentry, A. H., 1990. Four Neotropical Rainforests. Yale University Press, New Haven.
Gentry, A. H., 1995. Patterns of diversity and floristic composition in neo- tropical montane forests, Biodiversity and Conservation of Neotropical Montane Forests. New York Botanical Garden, New York.
Gentry, A. H., 1974. Flowering phenology and diversity in tropical Bignoniaceae. Biotropica, 6(1): 64-68.
Gentry, A. H., 1982. Patterns of neotropical plant species diversity. Evolutionary Biology, 15: 1-84.
Gentry, A. H., 1986. Species richness and floristic composition of Choco region plant communities. Caldasia, 15: 71-91.
Gentry, A. H., 1988. Tree species richness of upper Amazonian Forest. Proc. of the National Academy of Science, 85: 156-159.
Gentry., A. H., 1990. Floristic similarities and differences between Southern Central America and Upper and Central Amazonia. In: A.H. Gentry (Editor), Four Neotropical Rain Forest. Yale University Press, London, pp. 141-157.
Gentry, A. H., 1988. Changes in plant community diversity and floristic composition on environmental and geographical gradients. Annals of Missouri Botanical Garden, 75(1): 2-35.
Gentry, A. H., 1992. Tropical forest biodiversity: distributional patterns and their conservational significance. Oikos, 63: 19-28.
269 Gentry, A.H., 1993. A Field Guide to the Families and Genera of Woody Plants of Northwest South America: With Supplementary Notes on Herbaceous Taxa, Washington DC, 575 pp.
Gitay, H. and Noble, I.R., 1997. What are plant functional types and how shouldwe seek them? Plant Functional Types. Cambridge University Prees, Cambridge, 3-19 pp.
Gougeon, F.A., 1995b. A crown-following approach to the automatic delineation of individual tree crowns in high spatial resolution aerial images. Canadian Journal of Remote Sensing, 21(3): 274-284.
Gougeon, F.A., 1997. Recognizing the forest from the trees: individual tree crown delineation, classification and regrouping for inventory purposes. In: A.A. ERIM International Inc., MI. (Editor), Proceedings of the Third International Airborne Remote Sensing Conference and Exhibition, Copenhagen, Denmark, pp. 807-814.
Gougeon, F.A., 1999. Towards semi-automatic forest inventories using individual tree crown (ITC) recognition, Canadian Forest Service, Pacific Forestry Centre, Victoria British Columbia.
Gougeon, F.A., D. G. Leckie, D Paradine, I Scott., 1998. Individual tree crown species recognition: the Nahmint study. In: D.G.L. D A Hill (Editor), International forum: automated interpretation of high spatial resolution digital imagery for forestry, Proceedings: Symposium. Natural Resources Canada, Canadian Forest Service, Pacific Forestry Centre, Victoria, British Columbia, pp. 209-223.
Gougeon., F.A., 1992. Individual tree identification from high resolution MEIS images. In: M.D.G. D.G. Leckie (Editor), Airborne multispectral scanning for forestry and mapping (with emphasis on MEIS)., Proceedings: Workshop. Information Report PI-X-113. Forestry Canada, Petawawa National Forestry Institute, Val-Morin, Quebec, Canada.
Gougeon., F.A., 1995. Comparison of possible multispectral classification schemes for tree crowns individually delineated on high spatial resolution MEIS images. Canadian Journal of Remote Sensing, 21(1): 1-9.
Gougeon., F.A. and Leckie., D.G., 2001. Individual tree crown image analysis - A step towards precision forestry. In: C.F. Service (Editor), Precision Forestry Symposium. University of Washington, Seattle, pp. 17-20.
Grubb, 1963. A comparison of lowland and rain forest in Ecuador: The forest structure, physiognomy and floristic. Journal of Ecology, 51: 567-601
Grubb, P., J and Withmore, T., C, 1966. A comparison of lowland and rain forest in Ecuador: II: The climate and its Effects on the distribution and physiognomy of the forest. Journal of Ecology, 57(2): 303-333.
270 Gutierrez, N., Gongora, C. and Arguello, A., 1996. Manual practico para la identificacion de las principales especies maderables de la zona del rio San Juan Nicaragua. Boletin No 19. CATIE, San Jose, 114 pp.
Haffer, J., 1969. Speciation in Amazonian forest birds. Science, 165: 131-137.
Haley P et al., 2004. Variation in potential for isoprene emissions among Neotropical forest sites. Global Change Biology, 10: 630-650.
Halle, F., Oldeman R. A and Tomlinson P.B, 1978. Tropical trees and forest. An architectural analysis, New York.
Haralick, R.M., 1979. Statistical and structural approaches to texture. Proc. IEEE, 67(5).
Harms, K., Condit R, Hubbell S. P and Foster R. B. 2001. Habitat associations of trees and shrubs in a 50-ha Neotropical forest plot. Journal of Ecology (89): 947-959.
Harner, R.F. and Harper, K.T., 1976. The role of area, heterogeneity, and favorability in plant species diversity of pinyon-juniper ecosystems. Ecology, 57: 1254-1263.
Harper, J. L. 1977. Population Biology of plants. Academic Press, London, UK.
Held, A., Ticehurst C, Lymburner Leo and Nicole Williams. 2003. High resolution mapping of tropical mangrove ecosystems using hyperspetral and radar remote sensing. International Journal of Remote Sensing, 24(13): 2739-2759.
Henderson, A., Gloria Galeano. and Bernal, R., 1995. Field guide to the palms of the Americas. Princeton University Press, New Jersey, 352 pp.
Heywood., V.H., 1995. Global Biodiversity Asessment, Cambridge.
Hicks, A.J. and Hicks, P.H., 1978. A selected bibliography of plant collection and herbarium curation. Taxon, 27(1): 63-99.
Higgins, M.A. and Kalle, R., 2004. Rapid Tropical Forest Inventory: a Comparison of Techniques Based on Inventory Data from Western Amazonia. Conservation Biology, 18(3): 799-811.
Hilborn, W.H., 1978. Application of remote sensing in forestry. In: B.F. Richardson (Editor), Introduction to Remote Sensing of the Environment Kendall/Hunt, Dubuque.
Holdridge, L.R., 1972. Dendrología práctica de los trópicos americanos. Centro Científico Tropical, San José, 32 pp.
271 Hoppes, G. W. 1988. Seedfall patterns of several species of Bird-dispersed plants in an Illinois Woodland. Ecology, Vol 62, No 2, pp 320-329
Howard, J., 1970. Aerial-photo ecology. Faber and Faber, London.
Howe, H.F. and Steven, D.D., 1979. Fruit production, migrant bird visitation, and seed dispersal of Guarea glabra in Panama Oecologia, 39(2): 185- 196.
Hu, B., Lucht, W., Strahaler, A.H and Schaaf, C.B and Smith, M, 2000. Surface albedos and angle-corrected NDVI from AVHRR observations of South America. Remote Sensing of Environment, 71: 119-132.
Hubbell, S.P., 1979. Tree dispersion, abundance, and diversity in a tropical dry forests. Science, 203: 1299-1309.
Hubbell, S.P. and Foster, P., 1986. Biology, chance and history and the structure of tropical rain forest tree communities, Community Ecology. Harper and Row, New York, pp. 314-329.
Humphries, C.J., Williams, P.H. and Vane-Wright, R.I., 1995. Measuring Biodiversity value for Conservation. Annual Review of Ecology and Systematics, 26: 93-111.
Irvin, B., R., and David, M., 1989. Methods for exploiting the relationship between buildings and their shadows in aerial imagery. IEEE Transactions on Systems, Man and Cybernetics, 19(6): 1564-1575.
Jain, S.K., 1977. A handbook of field and herbarium methods. Today and Tomorrow. Printers & Publ, New Delhi, 157 pp.
Janzen, D. H. 1970. Herbivores and the number of tree species in tropical forests. American naturalist, 104, pp 501-528
Janzen, D.H. and Liesner, R., 1980. Annotated checklist of Lowland Guancaste Province, Costa Rica, exclusive of Grasses and nonvascular cryptograms Brenesia, 18: 15-90.
Jarvis, A., 2005. Measuring and modelling the plot to landscape scale spatial variation in diversity and structure of a tropical lowland and tropical montane forest, PhD, King's College London, London.
Jimenez, S.H., 1998. A new efficient methodology to teach plant determination in the tropics. Centro Cientifico Tropical, San Jose.
Johnson, N.C. 1995. Biodiversity in the balance: Approaches to setting geographic conservation priorities. Washington, D C, Biodiversity Support Program
272 Julliot, C., 1997. Impact of Seed Dispersal by Red Howler Monkeys Alouatta Seniculus on the Seedling Population in the Understorey of Tropical Rain Forest Journal of Ecology, 85(4): 431-440.
Kitajima, K and Carol K. Augspurger. 1989. Seed and seedling ecology of a monocarpic tropical tree , Tachigalia versicolor. Ecology, Vol 70, No 4, pp 1102-1114
Keizer, E., 2007. High resolution remote sensing of tropical forest canopies, PhD, King's College London, London.
Keller, R. 2004. Identification of tropical woody plants in the absence of flowers. Birkhauser verlag, Berlin, 294p.
Killeen, T., Garcia, E. and Beck, S., 1993. Guia de Arboles de Bolivia. Missouri Botanical Garden, La Paz.
King, D.A., 1998. Influence of leaf size on tree architecture: first branch height and crown dimensions in tropical rain forest trees. Trees, Structure and Function, 12(7): 438-445.
Kirsten M. S and Fragoso, J. M, 2002. Pulp Handling by Vertebrate Seed Dispersers Increases Palm Seed Predation by Bruchid Beetles in the Northern Amazon. Journal of Ecology, 90(6): 1024-1032.
Kirsten M. S and Fragoso, J. M, 2003. Red-rumped Agouti (Dasyprocta leporina) Home Range Use in an Amazonian Forest: Implications for the Aggregated Distribution of Forest Trees. Biotropica, 35(1): 74-83.
Kuraiwa, S. 1968. A new calculation method for total photosynthesis of plant community under illumination consisting of direct and diffused light In: E. Fe (Editor), Functional of terrestrial ecosystems at primary production level. UNESCO, Paris, pp. 391-397.
Lavorel, S., 1997. Plant functional classifications: from general groups to specific groups based on response to disturbance. Trends in Ecology and Evolution, 12: 474-478.
Leckie D G., M.D.G., F A Gougeon, Lodin J W and X Yuan. 1998. Computer- assisted photointerpretation aids to forest inventory mapping: some possible approaches. In: D.G.L. D.A. Hill (Editor), International forum: automated interpretation of high spatial resolution digital imagery for forestry, Proceedings: Symposium. Canadian Forest Service, Pacific Forestry Centre, Victoria, British Columbia, pp. 335-343.
Leckie D. G, Gougeon F.A., David Hill, Rick Quinn, Lynne Armstrong and Roger Shreenan., 2003a. Combined high-density lidar and multispectral imagery for individual tree crown analysis. Canadian Journal of Remote Sensing, 29(5): 633-649.
273 Leckie, D. G. et al., 1999. Forest parameter extraction through computer-based analysis of high resolution imagery. In: A.A. ERIM International Inc., MI. (Editor), Proceedings of the Fourth International Airborne Remote Sensing Conference and Exhibition /21st Canadian Symposium on Remote Sensing. Canadian Forest Service, Ottawa, Canada, pp. 205-213.
Leckie, D.G., Gougeon, F.A. and Dennis, W.N.A.P., 2003b. Stand delineation and composition estimation using semi-automated individual tree crown analysis. Remote Sensing of Environment, 85(3): 355-369.
Leckie., D.G. and Gougeon., F.A., 1998b. As assessment of both visual and automated tree counting and species identification with high spatial resolution multispectral imagery. In: D.G.L. D.A. Hill (Editor), International forum: automated interpretation of high spatial resolution digital imagery for forestry, Proceedings: Symposium. Canadian Forest Service, Pacific Forestry Centre, Victoria, British Columbia, pp. 141-152.
Lederman, S.J., Jones, B. and Thorne, G., 1986. Perception of texture by vision and touch: multidimensionality and intersensory integration. Journal of Exp. Psychol. Hum. Perception. Performance, 12(2): 169-180.
Leica, G., 2002. Erdas Imagine.
Lewis, S.L., Phillips, O. L., Baker, T. R., Lloyd, J., Malhi, Y., Almeida, S., Higuchi, N., Laurance, W. F., Neill, D., Silva, N., Terborgh, J., Torres- Lezama, A., Brown, S., Chave, J., Kuebler, C., Nunez, P., Vasquez, M. R. & Vinceti, B. 2004. Concerted changes in tropical forest structure and dynamics: evidence from 50 South American long term plots. Philosophical Transactions of the Royal Society London, 359: 421-436.
Lieberman, D., Lieberman, N., Hartshorn, G.S. and Peralta, R., 1985. Growth rates and age–size relationships of tropical wet forest trees in Costa Rica. Journal of Tropical Ecology, 1(2): 97-109.
Liesner, R., 2000. Field techniques used by Missouri Botanical Garden, Missouri.
Link, A. and Di Fiore, A., 2006. Seed dispersal by spider monkeys and its importance in the maintenance of neotropical rain-forest diversity. Journal of Tropical Ecology, 22: 235-246.
Link, A. and Stevenson, P.R., 2004. Fruit dispersal sindromes in animal disseminated plants in Tinigua National Park, Colombia. Revista Chilena de Historia Natural, 79: 319-334.
Loiselle, B.A., Eric Ribbens and Vargas, O., 1996. Spatial and Temporal Variation of Seed Rain in a Tropical Lowland Wet Forest. Biotropica, 28(1): 82-95.
274 Lorenzi, H., 1992. Arvores Brasileiras, Manual de Identificacao e Cultivo de Plantas arboreas nativas do Brasil.
Lovejoy, T.E., 1997. 'Biodiversity: what is it?' In: D.E.W.a.W.O.W. M. L Reaka- Kudla (Editor), Biodiversity II: Understanding and protecting our Biological Resources. Joseph Henry Press, Washington DC.
Lowman, M.D., 1986. Light interception and its relation to structural differences in three Australian Rainforest canopies Australian Journal of Ecology, 11: 163-170.
Maas, P.J.M., Lubbert Y. Th. Westra and Aljos Farjon, 1993. Neotropical Plant Families: a concise guide to families of vascular plants in the Neotropics. Koeltz Scientific books, Germany, 289 pp.
Mack, A.L., 1997. Spatial Distribution, Fruit Production and Seed Removal of a Rare, Dioecious Canopy Tree Species (Aglaia aff. Flavida Merr. et Perr.) in Papua New Guinea Journal of Tropical Ecology, 13(3): 305-316.
Madrinan, S., 2004. Lauraceae. In: N. Smith (Editor), Flowering plants of the neotropics. Princeton University Press, New Jersey, pp. 204-205.
McEuen, A. B and Curran Lisa M. 2004. Seed dispersal and recruitment limitation across spatial scales in temperate forest fragments. Ecology, Vol. 85, No.2, pp 507-518
Maecha, G., 1990. Dendrologia de los bosques del Pacifico, Cali.
Malhi Y and Phillips, O.L., 2004. Tropical forests and global atmospheric change: a synthesis. Philosophical Transactions of the Royal Society London, 359: 549-555.
Masaki, T., Kominami Y and T. Nakashizuka. 1994. Spatial and seasonal patterns of seed dissemination of Cornus controversa in a temperate forest. Ecology, 75: 1903-1910
Medellin, R.A., 1994. Seed Dispersal of Cecropia obtusifolia by Two Species of Opossums in the Selva Lacandona, Chiapas, Mexico. Biotropica, 26(4): 400-407.
Morie, S., 1999. Guide to the vascular plants of Central French Guiana. New York Botanical Garden, New York.
Mulligan, M., 1999. A simple hydrological model for tropical mountain cloud forests. Department of Geography Occasional paper(48).
Myers, B.J., 1982. Guide to the identification of some tropical rain forest species from large-scale colour aerial photographs. Australian Forestry, 45(1): 28-41.
275 Myers, B.J., 1986. 'Tropical Deforestation and a Mega-extinctions Spams'. In: M. Soule (Editor), Conservation Biology: The science and scarcity of diversity. Sinauer Associates, Inc, Sunderland, Massachusetts.
Myers, B.J. and Benson, M.L., 1981. Rainforest species on large scale colour photos. Photogrammetric Eng. Remote Sensing, 41: 505-513.
Myers, N., Mittermeier, R.A., Mittermeier, C.G., da Fonseca, G.A. and Kent, J., 2000. Biodiversity hotspots for conservation priorities. Nature, 403: 853- 858.
Newstrom., L.E., Frankie G. V, Baker, H.G., 1994. A new classification for plant phenology based on flowering patterns in lowland tropical rain forest trees at La Selva, Costa Rica. Biotropica, 26(2): 141-159.
Noos, R.F. and Cooperrider, A.Y., 1994. Saving nature's legacy. Island Press, Washington, DC.
Normand, S., Jaana, Vormisto J, Jens-Christian Svenning J, Cesar Grandez and Henrik Balslev, 2006. Geographical and environmental controls of palm beta diversity in paleo-riverine terrace forests in Amazonian Peru. Plant Ecology, 186: 161-176.
Oliveira, A.C.M. and Ferrari, S.F., 2000. Seed dispersal by black-handed tamarins, Saguinus midas niger (Callitrichinae, Primates): implications for the regeneration of degraded forest habitats in eastern Amazonia. Journal of Tropical Ecology, 16: 709-716.
Olmos, F., Renata Pardini, Ricardo L. P. Boulhosa, Roberto Burgi and Morsello, C., 1999. Do Tapirs Steal Food from Palm Seed Predators or Give Them a Lift? Biotropica, 31(2): 375-379.
Oskins, W., 1982. Collecting plant specimens (an outline with appendices). Erigenia, 1: 9-21.
Palmer, M. and Philip , M.D., 1990. Small-scale environmental heterogeneity and the analysis of species distributions along gradients. Journal of Vegetation Science, 1(1): 57-67.
Pennington, T.D., 1990. Sapotaceae. Flora Neotropica, 52: 1-771.
Pennington, T.D., 2004. Meliaceae. In: N. Smith (Editor), Flowering plants of the neotropics. Princeton University Press, New Jersey, pp. 243.
Phillips O. L., Nunez Vargas P, Monteagudo A. L, Pena C.A, Chuspe M. E, Galiano S. W, Yli-Halla M and Rose S. 2003a. Habitat association among Amazonian tree species: a landscape scale approach. Journal of Ecology, 91: 757-775.
276 Phillips O. L, Vásquez M. R, Arroyo L, Baker T. R., Killeen T, Lewis S. L, Malhi Y, Mendoza A. M, Neill D, Núñez Vargas P, Alexiades M, Cerón C, Di Fiore A, Erwin T, Jardim A, Palacios W, Saldias M and Vinceti B. 2002. Increasing dominance of large lianas in Amazonian forests. Nature, 418: 770-774.
Phillips O. L, Vasques Martinez, Monteagudo Mendoza, Baker T. R and Vargas, N. 2005. Large lianas as hyperdinamic elements of the tropical forest canopy. Ecology, 86: 1250-1258.
Phillips, O. L. and Baker, T., 2002. RAINFOR: field manual for plot establishment and remeasurement. University of Leeds.
Phillips, O.L. and Raven, P.H., 1997. A strategy for sampling neotropical forest. In: G.A. C (Editor), Neotropical Biodiversity and Conservation. Mildred E Mathias Botanical Garden, Los Angeles, pp. 141-165.
Phillips, O.L., Martinez V. R, Nunez vargas P, Monteagudo L. A, Chuspe M. E, Galiano W, Cruz P. A, Timana M, Yli-Halla M and Rose S. 2003b. Efficient plot-based floristic assessment of tropical forest. Journal of Tropical Ecology, 19: 629-645.
Pinto J. R., Oliveira-Filho, A.T. and Hay, J.D.V., 2005. Influence of soil and topography on the composition of a tree community in a central Brazilian valley forest. Edinburgh Journal of Botany, 62: 69-90.
Pitman, N., 2000. A large-scale inventory of two Amazonian tree communities Duke University, 240 pp.
Pitman, N., Jhon W. Terborgh, Miles R. Silman, Percy V. Nunez, David A. Neill, Carlos E. Ceron, Walter A. Palacios and Milton Aulestia, 2001. Dominance and distribution of tree species in upper Amazonian Terra firme Forests. Ecology, 82(8): 2101-2117.
Pitman, N., John Terborgh, Miles R. Silman and Nunez, P.V., 1999. Tree species distribution in an Upper Amazonian forest. Ecology, 80(8): 2651-2661.
Plotkin, B.J., Jerome Chave and Ashton, P., 2002. Cluster Analysis of spatial patterns in Malaysian tree species. The American Naturalist, 160(5): 629- 643.
Plotkin, B.J., Matthew D, P., Nandi Leslie, N, M. and James, L.P.A., 2000. Species-area curves, spatial aggregation, and habitat specialization in tropical forest. Journal of theor Biology, 207: 81-99.
Porter, C.L., 1967. Taxonomy of Flowering Plants. W.H. Freeman and Company, San Francisco.
Pouliot D. A., D.J.K., F. W. Bell and D. G. Pitt. 2002. Automated tree crown detection and delineation in high-resolution digital camera imagery of
277 coniferous forest regeneration. Remote Sensing of Environment, 82: 322- 334.
Prance, G.T., 1982. Forest refuges: evidences from woody angiosperms In: G.T. Prance (Editor), Biological diversification in the tropics. Columbia University Press, New York, pp. 137-158.
Prance, G.T., 1994. A comparison of the efficacy of higher taxa and species numbers in the assessment of biodiversity in the neotropics. Philosophical Transactions of the Royal Society of London, 345(1311): 89-99.
Prance, G.T., Beentje H, Dransfield J and Jhons R, 2000. The tropical flora remains undercollected. Annals of Missouri Botanical Garden, 87: 67-71.
Prance, G.T., Rodriguez, W.A. and Da Silva, M.F., 1976. Inventario florestal de um hectare de mata de terra firme km 30. da estrada Manaus-Itacoatiara. Acta Amazonica, 6: 9-35.
Qin, W., Gerstl Siegfried A. W, Deering Donald W and Goel, Narendra S., 2002. Characterizing leaf geometry for grass and crop canopies from hotspot observations: A simulation study. Remote Sensing of Environment, 80(1): 100-113.
Raven, P.H., 1988. Tropical floristic tomorrow. Taxon, 37: 549-560.
Ravishankar Rao, A., and Gerald L. Lohse, 1996. Towards a Texture Namimg System: Identifying relevant dimensions of texture. Vision Research, 36(11): 1649-1669.
Ribeiro, J.E., Hopkins Michael, et al., 1999. Flora da Reserva Ducke: Guia de identificacao das plantas vasculares de uma floresta de tierra-firme na Amazonia central. INPA, Manaus, 816 pp.
Ripley, B.D., 1976. The second-order analysis of stationary point processes. Journal of Applied Probability, 13: 255-266.
Romolerux., K., 1997. Yasuni Forest Dymanics Plot: initial taxonomic results. Newsletter of Centre for Tropical Forest Science.
Russo, S. E., and Carol, K.A., 2004. Aggregated seed dispersal by spider monkeys limits recruitment to clumped patterns in Virola calophylla. Ecology Letters, 7(11): 1058-1064.
Salm, R., 2005. Arborescent palm seed morphology and seedling distribution Brazilian Journal of Biology, 65(4): 1-10.
Saville, D. 1962. Collection and care of botanical specimens. Canad. Dept. Agric, Publ. Res. Branch 1113: 1-124.
278 Sayn - Wittgenstein., De Milde and Inglis, C. J., 1978. Identification of tropical trees on aerial photographs FMR-X-113, Forest Management Institute, Canada.
Schimper, A.F.W., 1903. Plant geography upon a physiological basis. Oxford University Press, Oxford.
Seigler, D.S., 2004. Fabaceae. In: P.U. Prees (Editor), Flowering Plants of the Neotropics. Princeton University, New Jersey, pp. 151-152.
Stevens, W.D., C. Ulloa., A. Pool and O. M. Montiel, 2001. Flora de Nicaragua. Monographs in Systematic Botany from the Missouri Botanical Garden, 85: 1-2666.
Stevenson, P.R., 2000. Seed dispersal by woolly monkeys (Lagothrix lagothricha) at Tinigua National Park, Colombia: Dispersal distance, germination rates, and dispersal quantity. American Journal of Primatology, 50(4): 275-289.
Steyermark, J., Paul, B., Kay, Y. and Bruce, H., 1995. Flora of the Venezuelan Guayana. Missouri Botanical Garden.
Suarez, S., 2003. Spatio-temporal foraging skills of white-bellied spider monkeys (Ateles belzebuth belzebuth) in the Yasuni National park, Ecuador, Stoony Brook University, New York.
Suarez, S., 2006. Diet and travel costs for Spider monkeys in a nonseasonal, hyperdiverse environment. International Journal of primatology, 27(2): 411-436.
Svenning, J. C. and Wright, S.J., 2005. Seed limitation in a Panamanian forest. Journal of Ecology, 93(5): 853-862.
Svenning, J. C., 1999. Microhabitat specialization in a species-rich palm community in Amazonian Ecuador. Journal of Ecology, 87: 55-65.
Tabarelli, M. and Peres, C.A., 2002. Abiotic and vertebral seed dispersal in the Brazilian Atlantic forest: implications for forest regeneration. Biological Conservation, 106: 165-176.
Terborgh, J. and Andresen, E., 1998. The composition of Amazonian forest: patterns at local and regional scales. Journal of Tropical Ecology, 14: 645-664.
Thomas, D. W., Cloutier D, Provencher Marc and Houle, C. 1988. The shape of Bird-generated seed shadows around a Tropical fruiting tree. Biotropica, Vol 20, No 4. pp 347-348
Thorne, R.F., 1992. Classiffication and Geography of the flowering plants. The Botanical Review, 58: 225-348.
279
Trichon, V., 2001. Crown typology and the identification of rain forest trees on large-scale aerial photographs. Plant Ecology, 153: 301-312.
Trichon, V. and Julien, M.P., 2006. Tree species identification on large-scale aerial photographs in a tropical rain forest, French Guiana- application for management and conservation. Forest Ecology and Management, 225(1- 3): 51-61.
Tuomisto, H., 1998. What Satellite Imagery and Large-Scale Field Studies can Tell About Biodiversity Patterns in Amazonian Forest. Annals of the Missouri Botanical Garden, 85(1): 48-62.
Tuomisto, H., Kalle Ruokolainen, Melchor Aguilar and Sarmiento, A., 2003. Floristic patterns along a 43-km long transect in an Amazonian Rain Forest. Journal of Ecology, 91: 743-756.
Turner, W., S. Spector, N. Gardiner, M. Fladeland, E. Sterling and M. Steininger., 2003. Remote sensing for biodiversity science and conservation. Trends in Ecology and Evolution, 18(6): 306-314.
Utsugi, H., 1999. Angle distribution of foliage in individual Chamaecyparis obtusa canopies and effect of angle on diffuse light penetration. Trees, 14: 1-9.
Valencia, R., Balslev, H. and Paz y Mino, C., 1994. High tree alpha-diversity in Amazonian Ecuador. Biodiversity and Conservation, 3: 21-28.
Valencia, R., Foster R, Villa G, Condit R, Svenning J-C, Hernandez C, Romoleroux K, Losos E, Margard E and Balslev H, 2004. Tree species distributions and local habitat variation in the Amazon: large forest plot in eastern Ecuador. Journal of Ecology, 92: 214-229.
Vasquez, M.R. and Phillips, O.L., 2000. Floristics and ecology of a high- diversity forest Allpahuayo, Amazonian Peru Annals of Missouri Botanical Garden, 87: 499-527.
Vasquez, R., 1997. Florula de las reservas biologicas de Iquitos, Peru. Missouri Botanical Garden.
Verheyden, A., Farid D. Guebas, Katreien Thomaes, William De Genst, Sanath Hettiarachchi and Nico Koedam, 2002. High-resolution vegetation data for Mangrove research as obtained from Aerial Photography. Environment, Development and Sustainability, 4(2): 113-133.
Vezga, F., 1971. La Expedicion Botanica. Carvajal & Compania, Cali, 263 pp.
Vimos-Calle, P., 2007. Characterising forest structure in a rain forest of the Ecuadorian Amazon, PhD, King's College London, London.
280 Vormisto, J., Tuomisto H & Oksanen J, 2004. Palm distribution patterns in Amazonian rainforest: what is the role of topographic variation? Journal of Vegetatio Science, 15: 485-494.
Wagstaffe, R. and Fidler, J.H., 1968. The preservation of natural history specimens, Botany. H. F. and G. Witherby Ltd, London, pp. 404.
Warner, A.T. and Jong, Y. L., 1998. Delineation and identification of individual trees in the eastern deciduous forest. In: D.A.a.D.G.L. Hill (Editor), Automated Interpretation of High Spatial Resolution Digital Imagery for Forestry. Pacific Forest Centre, Victoria, British Columbia, Victoria, pp. 81-91.
Watt, A.S., 1924. On the ecology of the British beecwoods with special reference to their regeneration. Part II: The develpment and structure of beech communities on the Sussex downs. Journal of Ecology, 12: 10-202.
Webb, E.L., Stanfield, B.J. and Jensen, M.L., 1999. Effects of topography on rainforest tree community structure and diversity in American Samoa, and implications for frugivore and nectarivore populations Journal of Biogeography, 26: 887-897.
Wehncke, E.V., Hubbell, S.P., R. Foster and Dalling, J.W., 2003. Seed dispersal patterns produced by white-faced monkeys: implications for the dispersal limitation of Neotropical tree species. Journal of Ecology, 91(4): 677- 701.
Weier, E.C., Ralph, S. and Michael, G.B., 1974. Botany: An introduction to plant biology. John Wiley & Sons, Davis, California.
Wenny, D.G., 1999. Two-stage dispersal of Guarea glabra and G. kunthiana (Meliaceae) in Monteverde, Costa Rica. Journal of Tropical Ecology, 15: 481-496.
Williams, P.H., 1995. Comparing character diversity using biological surrogates. In: C.J. Humphries, C.R. Margules, R.L. Pressey and R.I. Vane-Wright (Editors), Priority Areas Analysis: Systematic methods for Conserving Biodiversity. Oxford University Press, Oxford.
Williams, P.H. and Gaston, K.J., 1994. Measuring more biodiversity: can higher- taxon richness predict wholescale species richness? Biological Conservation, 67: 211-217.
Williams, P.H., Gaston, K.J. and Humphries, C.J., 1997. Mapping biodiversity value worldwide: combining higher-taxon richness from different groups. Proceedings of the Royal Society, 264: 141-148.
Wilson, T.K., 2004. Myristicaceae, Flowering plants of the neotropics. Princeton University Press, New Jersey, pp. 261.
281 Willson, M. F. 1992. The ecology of seed dispersal. Pages 61-85 in M. Fenner, editor. Seeds: the ecology of regeneration in plant communities. CAB International Press, Cambridge, UK.
Wulder, M. A., 1998. Optical remote-sensing techniques for the assessment of forest inventory and biophysical parameters. Progress in Physical Geography, 22(4): 449-476.
Wulder, M.A., White J.C., Nelson K.O. and Nelson T, 2004., Comparison of airborne and satellite hight spatial resolution data for the identification of individual trees with local maxima filtering. International Journal of Remote Sensing, 25(11): 2225-2232.
Zereja J. C., Wendy L. Clement and Shannon, L.D.a.G., D. Weiblen, 2005. Biogeography and divergence times in the mulberry family. Molecular Phylogenetics and Evolution 37: 402-416.
282 APPENDIX 1
Aerial imagery at TBS (Ground data collection)
All the images below are equivalent to the collection sectors (blue circles and rectangles above)
Sector 1
283 Sector 2 and 3
Sector 4 and 5
284
Sector 6
285 Sector 6
Sector 7
286 Sector 8
Sector 9-10
287
Sector 12
Sector 13
288 Sector 13
Sector 14
289
Sector 15
Sector 15
290
Sector 16
Sector 16
291 Sector 17
Sector 19
292
Sector 20 and 21
293
Sector 22
294 Sector 23
Sector 24
295
Sector 25
296
APPENDIX 2
Summary of the main tree families at Tiputini Biodiversity Station (TBS)
By
Carlos E Gonzalez
Ground floristic composition studies at the plot scale usually find varied results at different sites, as has been the case at Yasuni National Park, Ecuador. Pitman
(2000) argues “Fabaceae, Lauraceae and Meliaceae are represented by a large number of species in tropical tree communities generally but by only an average number of common species at Yasuni (Ecuador) and Manu (Peru) while
Violaceae and Cecropiaceae are important families in the oligarchies at our sites but relatively species-poor across tropical tree communities in general”. This is a general description for the main botanical families at TBS.
Fabaceae Lindl.
Type - specimen(s) T: Faba Mill. - The Gardeners Dictionary. Abridged,
Fourth Edition, 1754 in (Stevens, 2001)
Geographical distribution
Fabaceae (or Leguminosae) is the most dominant neotropical tree family
(Gentry, 1990b). It has 3 sub-groups, which are Mimosaideae, Caesalpinoidea,
297 and Papilionoidea sensu stricto. The family is numerous and distributed worldwide; Fabaceae “comprises 65-700 genera and about 1800 species; in the western hemisphere, there are about 272 native genera and 1205 species”
(Seigler, 2004).
Floral taxonomy
Flowers are medium to small size, generally grouped into spikes, racemes or panicles, bisexual, actinomorphic or strongly zygomorphic, hypogynous or somewhat perigynous; perianth of 5-merous whorls, differentiated into a calyx and corolla, sepals free to connate, petals free or often partially connate or all petals basally connate; stamens 5-10, often connate (Maas, 1993).
Field characteristics
The largest trees in the rainforest belong to the Fabaceae family. This family is very diverse in terms of morphological features, but the most distinguishing characteristic of the family is its compound leaf arrangement. There is no single way of recognising Fabaceae trees. The trunk is generally rounded; external bark can be whitish, reddish, grey or dark; inner bark layers are extremely varied in colour but it could be said that red is the dominant colour. The base of the trunk is usually straight. Leaves are compound-alternate and some opposite; leaflets can be either odd or pair-pinnate.
298 Main genera
Of the many genera belonging to the Fabaceae family in the Amazon, two representative taxa were chosen for their floristic characteristics and composition as part of the landscape distribution analysis for this thesis. These genera are
Parkia and Inga.
Inga has 350 species, and is one of the largest and most common neotropical genera (Gentry, 1993). Gentry describe Inga as follows: “the typical cup-shaped glands between each leaflet pair of the paripinnate leaves, frequently with a winged rachis, are completely distinctive. Flowers always of the typical mimosoid form but the inflorescence varies from umbellate to capitate to spicate.
Fruits never dehiscent always elongated” (Gentry, 1993).
Parkia is one of the most common emergent trees in TBS; the genus comprises
40 species. Gentry characterised Parkia as “usually emergent spreading- crowned trees; vegetatively distinctive in the flat glandular area of the petiole; large-leafleted species tend to have very characteristic narrowly oblong, slightly curved, coriaceous leaflets and may have opposite leaves. The technical characters include calyx imbricate in bud and the very characteristic, capitate, bat-pollinated, usually long pendent infloresce and fruits linear and woody, long stipitate, borne hanging in groups from the pendent inflorescence” (Gentry,
1993).
299 Figure 1 shows an example of the geographical distribution of a common Parkia species: Parkia multijuga Benth.
Figure 1. Sites at which Parkia multijuga Benth has been found (source: http://mobot1.mobot.org/website/map_post.asp)
General uses
The fruits are generally abundant and of a large size. Fabaceae fruits are a food source for mammals, principally for monkeys, but also less frequently for peccaries and some species of bat.
300 Moraceae Link.
Type - specimen(s) Morus L. - Species Plantarum 2: 986. 1753 (Stevens,
2001)
Geographical distribution
The Moraceae family is mainly found in the Old World tropics, particularly in
Asia and the Indo-Pacific islands, but also in tropical America. Biogeographical research has suggested that the family has Gondwanan origins (Zereja J. C. et al., 2005). In tropical America, there are 19 genera and 270 species. It is one of the top tree families in species diversity and abundance of individuals in ecological studies, especially in western Amazonia (Berg, 2004).
Floral taxonomy
Flowers are unisexual, plants are monoecious or dioecious; the perianth is variable, often 4-merous; stamens 1-2(4), sometimes inflexed in bud, then springing back at anthesis and releasing pollen in an explosive way (Maas,
1993).
Field characteristics
Moraceae is a laticiferous family, with characteristic milky sap found in most of the species. In some cases the sap is brown. Moraceae can be confused with
Sapotaceae, Euphorbiaceae and Apocynaceae because of the presence of
301 exudates in the trunk. The combination of sap and terminal stipules makes
Moraceae different from the other families. Another defining feature is the presence of large buttresses, with a smooth outer trunk. The inner bark layer is sandy and pinkish-white in colour, with a grainy brittle texture. Leaves are simple, alternate and have stipules; leaf texture tends to be leathery.
Main genera
The Moraceae family comprises 37 genera and approximately 1050 species
(Berg, 2004); Ficus, Clarisia and Olmedia are some of the most common genus belonging to the family. These three genera have simple leaves, but other members of the family have palmate leaves. Cecropia and Pourouma are examples of such genera, which are included within the family but placed in a different sub-group. The genera Cecropia and Pourouma were chosen for a more detailed landscape mapping within TBS.
The Cecropia genus comprises 100 species, and it is “one of the predominant genera of early second growth; very distinctive in the palmately lobed, peltate leaves, hollow internodes inhabited by ants, and the characteristic pulvinar are at the base of the petiole with glycogen-containing food bodies” (Gentry, 1993).
The Pourouma genus is made up of 25 species, and it is “usually palmately 3-5 lobed and distinct; when deeply multilobed it can be told from superficially similar Cecropia species by the cordate rather than peltate base; brown latex is usually present in the young branches” (Gentry, 1993).
302 General uses
Trees have edible berries, which are mainly eaten by bats, peccaries and birds, and some monkeys. Humans use the fruits to make sweet drinks. The exudates are occasionally poisonous.
Sapotaceae Juss.
Type - specimen(s) T: Sapota Mill. - The Gardeners Dictionary Abridged
Fourth Edition 3: Sapota. 1754. (Stevens, 2001)
Geographical distribution
The Sapotaceae family consists of 53 genera and about 1100 species; in tropical
America, there are 11 genera and approximately 455 species (Pennington,
2004b). Sapotaceae is distributed from the southern United States, throughout
Mexico, Central America and the West Indies, and through South America to
Paraguay, Uruguay and Chile (Pennington, 1990).
Floral taxonomy
The floral taxonomy has been studied by T. D. Pennington since 1960. Flowers are small and bisexual or some-times unisexual; sepals 4-12, petals 4-12, forming a gamopetalus corolla; stamens in 2-3 whorls, free or basally connate (Maas,
1993).
303 Field characteristics
Sapotaceae is usually a canopy and sub-canopy tree family. Sometimes they have buttresses and the trunk used to have a textured external bark. The tree architecture has open foliage and branches are horizontally arranged. Leaves are simple, alternate, with or without stipules. The majority of the species have indumentums on the leaves and branches, with reddish or grey colours. The leaves are thick and the shape of the petiole is variable, usually convex or rounded. Leaf venation has parallel and pinnately secondary veins. Sapotaceae could be confused with Moraceae. The differences are the presence of sticky milky sap. The inner bark in Sapotaceae can also be distinguished because of the reddish-pink colour, while Moraceae’s inner bark is whitish. In Sapotaceae sap is exuded in a dot pattern, but the sap is not as abundant as in Moraceae family.
Main genera
It is a family from the lowland rain forest, which is highly diverse in the
Amazonian region. According to Pennington (1990), 50 species can be found in a single location in the Amazon forest. Eleven genera are recognised in the neotropics: Sideroxylon, Chromolucuma, Sarcaulus, Elaeoluma, Eclinussa,
Pradosia and Diploon. The main genera in the southern parts of South America are Pouteria, Manilkara, Chrysophyllum and Micropholis. Most of them are trees adapted to terra-firme habitats. However, some species can be found in flooded environments.
304 The only genus chosen for the analysis regarding this thesis was Pouteria. This genus has around 188 species. Gentry (1993) describes their taxonomical characters as follows: “all species have spirally arranged leaves and their characteristic pop-bottle-shaped petiole; most have reticulate tertiary venation; the usually urceolate flowers are characterised by having as many staminodes as corolla lobes”.
Figure 2 shows an example of one of the most common Sapotaceae edible tree species representing the geographical distribution over the whole Amazonian region.
Figure 2. Sites at which Caimito has been found (source: http://mobot1.mobot.org/website/map_post.asp)
305 General uses
Sapotacae fruits are edible berries, which are a good source of food for mammals. Latex can also be extracted from the trunks and used to produce rubber.
Myristicaceae R. Br
Type - specimen(s) T: Myristica Gronov. - Flora orientalis 141. 1755.
(Stevens, 2001)
Geographical distribution
Myristicaceae has a worldwide distribution containing 19 genera and about 400 species. In tropical America, there are 5 genera and approximately 84 species
(Wilson, 2004). It is distributed in the neotropics from Brazil to Central America.
Most of the species occur in the lowland Amazonian forest.
Floral taxonomy
The flowers are unisexual and small; petals are 3(-5) and basally connate; it usually has two stamens and filaments united into a solid column (Maas, 1993).
306 Field characteristics
This family is relatively easy to recognise in the field because of the combination of red sap, reddish outer bark and fibrous inner bark. Leaves are simple, alternate and have reddish pubescence underneath. When leaves have fallen to the ground, they turn grey, providing a reliable means of forest identification. Fruits have a reddish inner colour with a white edible part. Most leaves are puberulous with stars and a T shaped pubescence.
Main genera
Myristicaceae have five endemic genera for America, with a total of 80 species; the most common genera are Virola, Iryanthera, Compsoneura, Osteophloeum and Otoba (Ribeiro et al., 1999a) . Otoba is the only genus chosen for analysis in this research. The taxonomy is well described by Gentry (1993) as “leaves very distinctive in the smooth glaucous or tannish undersurface and the barely prominulous secondary veins which usually fade out well before the margin; venation lines paralleling the midvein are often conspicuous”.
Figure 3 shows the distribution of one of the most common trees found in the study area belonging to Myristicaceae.
307
http://mobot1.mobot.org/website/map_post.asp
Figure 3. South American geographical distribution of Otoba glycycarpa
General uses
This family provides a food supply for toucans, fruit-eating birds and some monkeys. Indigenous people use the bark and sap to make hallucinogenic drinks and hunting poison, and occasionally, the wood is used for building roofs.
Meliaceae Juss.
Type - specimen(s) T: Melia L. - Species Plantarum 1: 384-385. 1753.
Geographical distribution
308 Meliaceae is distributed worldwide; there are about 50 genera and 550 species. In tropical America, there are 8 genera and about 130 species (Pennington, 2004a).
Floral taxonomy
Inflorescences are paniculate or racemose; there are 4-6 small sepals, which are free or partially connate and some times caducus; there are 4-6 petals, which are free or basally connate and a disc generally present. Stamens are 8-12, often connate into a tube (Maas, 1993).
Field characteristics
This family has a particular odour when a cut on the trunk is made, which is like old woody wine barrels. Indigenous people in the Amazon have named the family ‘apple tree’. Most of the trunks have whitish and pink inner bark; the old trees have very flaky outer bark. Meliaceae can be distinguished from the very closely related Sapindaceae families because of the lack of glands and compound leaves. It could be confused with Burseraceae. The sap dries white in
Burseraceae while Meliaceae does not have exudates and the leaves have indeterminate growth.
Main genera
Meliaceae is mainly distributed in tropical areas. Most of the genera are distributed in Amazonian or Andean environments. They usually have pinnate
309 leaves and the individual leaflets have a high morphological variability. Guarea and Trichilia are the most common genera in the rainforest while Cedrela and
Swietenia are dominant in the lowland forest. Within Meliaceae, the genus
Guarea was the taxa chosen for this thesis.
The Guarea genus consists of 35 species. Its taxonomical characteristics are described by Gentry (1993) as the “understory to canopy trees, vegetatively very characteristic with even-pinnate leaves with a kind of terminal bud between the terminal leaflet pair; of ten with very large fruits; flowers with anthers inserted and very conspicuous staminal tube”.
Cedrela fissilis is a woody species that has been badly impacted by timber extraction in the Amazon. The main distribution centre for one of the most popular timber species is mainly located in South America (Figure 4).
http://mobot1.mobot.org/website/map_post.asp
Figure 4. Sites which Cedrela fissilis has been found in South America
310
General uses
Meliaceae is a famous family because of the international timber trade market.
Extracts from the bark of some species of Cedrela, Guarea and Trichilia are used for medicines due to their anti mosquito properties, and to make natural paints.
Most of the Guarea fruits are eaten by many species of monkey in the lowland forest (Ribeiro et al., 1999a).
Lauraceae Juss.
Type - specimen(s) T: Laurus L. - Species Plantarum 1: 369. 1753.
(Stevens, 2001)
Lauraceae is a relatively simple family to recognise but at the same time it is a complicated group in terms of taxonomy. There are many unknown species all over the neotropics, and it could even be said that, after orchids, Lauraceae is one of the most unexplored taxa. Lauraceae trees can be recognised to the family level without flowers, but even with the reproductive organs, species identification is demanding. This is the reason why identification for Lauraceae was only done to the family level for this research.
311 Geographical distribution
Lauraceae includes 52 genera and 2750 species worldwide. In tropical America, there are 27 genera and 1000 species (Madrinan, 2004). According to Ribeiro
(1999) there are 29 genera and 900 species in North America.
Floral taxonomy
The flowers are bisexual or unisexual; it has mostly 6 sepals in two whorls, those are connate and often have an aromatic smell. There are between 2 or 4 stamens spatially organised in whorls (Maas, 1993).
Main genera
The largest and commonest genera are Nectandra and Ocotea. The taxonomical differences are based on the position of the valves in the anthers. Identifying these subtly different genera can be difficult and requires a certain amount of botanical expertise. Although floral differences are not fully known for most of the species, it is likely that entities can be recognised. Figure 5 illustrates their geographical distribution.
312
Figure 5. Example of Lauraceae species location in South America (Source: http://mobot1.mobot.org/website/map_post.asp
General uses
Lauraceae is another family that is considered very valuable due to the quality of its wood. The bark of many species is used for food flavouring, as they give off a strong odour when dried in the sun. Some of the woody species are cut down by local tribes to make canoes and bases for houses. The fruits are eaten by birds and small mammals.
Arecaceae Schultz Sch.
Type - specimen(s) T: Areca L. - Species Plantarum 2: 1189. 1753.
313 Palm trees are a typical component of the lowland canopy landscape. The areas historically settled by indigenous people frequently have a high level of palms amongst the tree population. The short review for this family mainly focuses on two dominant palm species: Iriartea deltoidea and Astrocarium chambira.
Geographical distribution
This is one of the most speciose families in the world, with 200 genera and 1500 species. The distribution of Arecaceae is centred in the tropics, however some groups are located in subtropical regions (Ribeiro et al., 1999a).
Main genera and species
This thesis includes just two well-known species of palms, which are reliably identifiable using aerial photography: Iriartea deltoidea and Astrocarium chambira. Both of these palm trees have been extensively studied in the Amazon, with special emphasis on Iriartea deltoidea (Figure 8). I deltoidea is distributed mainly in the Andean mountains and the Amazonian lowland forest. It is called the ‘copa palm’ because the trunk has a swollen shape. A chambira has a spiny and dark surface with a larger crown than I deltoidea; it is also distributed mainly in lowland rainforest.
I deltoidea is considered a distinctive species in the Amazon lowland forest.
Gentry (1993) describes it thus: a “large palm with close-together non spiny stilt roots; trunk often with swollen middle; leaflets irregularly split and held in
314 different planes, the basal segments wider; inflorescence terete curved and pendent in bud, resembling a longhorn’s horn and flowers with 12-15 stamens”.
Figure 6 shows the species distribution.
Figure 6. The sites which “Panbil” palm has been found in South America.
(Source: http://mobot1.mobot.org/website/map_post.asp)
A chambira is a less common palm but it is still representative of the mid-canopy in the lowland forest. It has a well known geographical distribution across the lowland rainforest (Figure 7). It is described by Gentry (1993) as “large usually solitary spiny palms with acute-tipped leaflets; large, hard fruit; essentially a large version of Bactris except inflorescence rachis thick and well developed with the male flowers densely clustered at ends of inflorescence branches and somewhat immersed”
315
Figure 7. Sites that the spiny palm has been found in South America (Source: http://mobot1.mobot.org/website/map_post.asp)
316
APPENDIX 3
PLANTS OF TIPUTINI BIODIVERSIY STATION I. DICOTILEDONOUS. A PRELIMINARY LIST
By
Carlos E. Gonzalez and Andrew Jarvis
This is a preliminary list of tree plot collections at Tiputini Biodiversity Station.
The collection was made in ten 25m x 25m distributed evenly around TBS, and one 1-Ha plot which was adapted to the HERB tree plot methodology after
Pitman (2002) established the plot.
The collections can be found in the University San Francisco of Quito herbarium, and copies in the National Herbarium, Quito and the herbarium of the
Universidad Catolica. Photos have been taken f most individuals, and are available from the authors upon request.
The tree database includes 2375 individuals, which have been marked, measured and identified. Of these, some 603 species have been separated, and this list reports these findings. The database is in a continual state of improvement, and it is stressed that the identifications made here are preliminary.
Taxon No Elevatio Collecti Photo individu n on Gallery als (m) Anacardiaceae Astronium cf 1 219 CG 3672 - Spondias cf mombin L. 1 261 CG 4413 1516A/B Spondias cf venulosa (Endl.) Endl 1 CG 4550 - Spondias cf 1 199-200 CG 3604 55A Tapiria cf guianensis 1 261 CG 4423 1502A/B Anacardium cf (sp novnigelperu) 1 227 CG 4252 1172A/B Indet 1 227 CG 4112 697A/B
317 Annonaceae Crematosperma cauliflorum aff 1 - CG 4442 1648A/B Duguettia spixiana Mart. 6 227 CG 4204 916A/B Duguettia 3 200-210 CG 4197 938A/B Duguettia 1294A/B Guatteria cargadero cf 3 220 CG 4225 988A/B Guatteria 1 227 CG 4093 641A/B Guatteria cf 1 219 CG 3679 - Guatteria cf 1 261 CG 4436 - Guatteria 1 238 CG 4493 1652A/B Annona cf 1 223 CG 4302 1754A/B Rollinia 1 227 CG 4172 850AA/BB Xylopia 1 261 CG 4424 1503A/B Indet 1 196 CG 3753 240A/B Pseudomolmea 1 217 CG 3759 268A/B Indet 1 200 CG 3805 342A Indet 1 227 CG 4372 - Indet 1 227 CG 4070 672A Indet 2 227 CG 4158 744A/B Indet 1 227 CG 4142 778A/B Indet 3 227 CG 4178 857A/B Indet 1 227 CG 4323 1319A/B Indet (Guatteria?) 1 217 CG 3777 235A Indet 1 227 CG 4220 970A/B Indet 1 CG 3596 22A/B Apocynaceae Aspidosperma 1 219 CG 4362 1463A/B Aspidosperma CG 3644 40A/B Couma 1 261 CG 4419 - Couma 1 227 CG 4046 605A/B Couma 1 227 CG 4177 864A Himathantus 1 199 CG 3606 - Lachmellea 1 227 CG 4199 918A/B Lachmellea 1 227 CG 4245 1037A/B Aquifoliaceae Ilex 1 217 CG 3795 - Ilex cf inundata 1 220 CG 3891 518A/B Ilex 1 220 CG 3926 532A/B Araliaceae Dendropanax arboreus (L.) Dec & Pl 4 227 CG 4067 673A/B Dendropanax caucanus (Harm.) Har 3 219-261 CG 3676 2098/99 Dendropanax 1 220 CG 3882 3409/11 Arecaceae Astrocarium chambira Burret 6 217-220 CG 3796 290A/B Astrocarium murumuru Mart. 1 200 CG 3849 394A Euterpe precatorae 4 219-261 CG 4348 - Geonoma maxima cf 17 227 CG 4057 - Iriarthea deltoidea Ruiz & Pavon 25 199-227 CG 3625 49A/B Oenocarpus bataua Mart. 1 CG 4552 - Socratea exorrhiza (Mart.) H. Wendl. 2 200-227 CG 3847 360A/B Wettinia maynensis Spruce 1 217 CG 3904 256A/B Indet sp1 10 199-21 CG 3699 151A/B Bignoniaceae Jacaranda copaia (Aublet) D. Don 4 219-261 CG 4278 1482A/B Memora cladothica Sandwith 11 227 CG 4208 655A/B Tabebuia serratifolia (Vahl.) G. Nich CG 4468 1593A/B Tabebuia cf - 219 CG 4337 1434A/B Indet - 261 CG 4367 1524A/B Bombacaceae Ceiba 1 219 CG 3651 187A/B Matisia cordata Bonpl 3 220 CG 3714 102A/B Matisia cf longiflora Gleason 13 217-227 CG 4087 - Matisia cf bracteolosa Ducke 2 227 CG 4247 1198A/B Matisia sp2 19 199-261 CG 3608 644A/B Ochroma pyramidale (Cav. ex Lam.) U. - 100 - - Pachira acuatica Aubl. 1 227 CG 4161 - Quararibea 1 261 CG 4395 1547A/B
318 Quararibea 1 238 CG 4454 1646B Quararibea 2 223 CG 4515 1695B Indet 1 CG 1690A/B Boraginaceae Cordia 1 199 CG 3621 42A Cordia 1 227 CG 4163 898A/B Indet 1 228 CG 4482 1628A/B Burseraceae Crepidospermum rhoifolium Bent (T& Pl) 1 199 CG 4075 665A/B Crepidospermum - 238 CG 4457 1636A/B Protium ecuadorense Benoist 2 199-200 CG 3839 370A/B Protium cf fimbriatum Swart 1 220 CG 3916 570A/B Protium glabrescens Swart 1 220 CG 3818 399A/B Protium polybotrium (Turcz.) Engl 1 217 CG 3731 212A/B Protium cf robustum (Swart.) DM Porter 1 227 CG 4053 595B Protium trifoliolatum Engl. 1 261 CG 4418 1497A/B Tetragastris panamensis (Engl.) Kuntz 1 238 CG 4481 1583A/B Trattinikia 261 CG 4368 1513A/B Protium 5 219-227 CG 4157 751B Protium 2 238-261 CG 4386 1533A/B Protium 1 223 CG 4531 - Protium 1 227 CG 4040 - Protium 1 227 CG 4159 743A/B Protium - 219 CG 4356 1459A Protium - 227 CG 4157 887A/B Protium - 238 CG 4439 1665A/B Indet - 227 CG 4036 619A Indet - 227 CG 4184 835A/B Protium 1704A/B Capparaceae Capparis 1 261 CG 4405 1558A/B Caricaceae Jacaratia cf digitata (Poepp & Endl.) Solm 5 219-227 CG 4553 625A/B Cecropiaceae Cecropia tomentosa 4 217-227 CG 3718 293A/B Cecropia sciadophylla Mart. 4 219-261 CG 4345 1422A/B Cecropia ficifolia 3 219-227 CG 3721 158A/B Cecropia 3 220-227 CG 4276 - Cecropia 1 227 CG 4301 - Cecropia 1 CG 3885A - Cecropia - 227 CG 4121 690A/B Coussapoa cf villosa Poepp & Endl. 2 200 CG 3833 369A/B Pourouma bicolor Mart. 2 199 CG 3600 50A/B Pourouma ferruginea cf 1 219 CG 3681 200B Pourouma guianensis Aubl. ? ? CG 3710 - Pourouma napoensis C. Berg. 4 223-238 CG 3781 243A/B Pourouma cf ovata 1 223 CG 4526 - Pourouma cf villosa ? ? CG 4474 - Pourouma sp1 2 199-227 CG 3609 886A/B Pourouma sp? 3 199-261 CG 3599 - Pourouma 3 219-227 CG 4377 - Pourouma - 227 CG 4271 1291A/02 Celastraceae Maytenus 1 210 CG 3867 447A/B Maytenus 1 238 CG 4444 1642A/B Maytenus 1 227 CG 4265 1122B Chrysobalanaceae Couepia 1 217 CG 3732 258A/B Couepia 2 261 CG 4378 1523A/B Couepia 1 227 CG 4320 1320A/B Couepia 1 227 CG 4305 1388A/B Licania cf elliptica Standl - 227 CG 4197 938AA/B Licania cf caudate Prance - 219 CG 3695 166A Licania glablanca 1 CG 4341 1418A/B Licania 1 CG 4524 1727A/B Licania 1 199 CG 3581 - Licania 1 217 CG 3769A 277A/B
319 Licania 2 200 CG 3801 344A/B Licania 1 210 CG 3861 423A/B Licania 1 238 CG 4451 - Licania 1 227 CG 4211 1008A/B Licania 1 227 CG 4244 1036A/B Licania 1 227 CG 4269 1104A/B Licania 1 227 CG 4081 Parinari cf 1 227 CG 4216 1000A/B Indet 1 219 CG 3887 - Indet 2 227 CG 4102 - Clusiaceae Chrysoclamis cf membranacea Pl & Tr 1 223 CG 3589 - Chrysoclamis 1 199 CG 4510 1683A/B Tovomita cf amazonica 2 217 CG 3750 - Vismia cf sprucei Sprage 217 CG 3756? 265A/B Vismia 2 227-261 CG 4127 812A/B Indet 219 CG 3712? 105A/B Combretaceae Buchenavia 1 227 CG 4090 632A/B Dichapetalaceae Dichapetalum cf rugosum - 217 CG 3730 222A Tapura - 261 CG 4387 1537A Elaeocarpaceae Sloanea 1 199 CG 3628 72A/B Sloanea 1 261 CG 4404 1571A/B Sloanea 4 217-219 CG 3762 275A/B Sloanea - 217 CG 3780 283A/B Sloanea - 200 CG 3825 331A/B Sloanea 1 238 CG 4499 - Sloanea 1 217 CG 4097 735A/B Sloanea 1 227 CG 4203 - Sloanea 1 199 CG 4499 1603A/B Euphorbiaceae Pausandra trianae (Muell. Arg.) Baill. 7 219-227 CG 3698 1003A/B Acidoton nicaraguensis CG 3782 232A/B Hasseltia floribunda - 227 CG 4171 1071A/B Alchornea triplinervia (Sprengel) Muell. Arg 1 199 CG 3624 51A/B Conceveiba 1 227 CG 4288 1223A/B Alchornea latifolia Sw 3 227 CG 4171 877A/B Hyeronima alchorneiodes Allemao - 227 CG 4084 648A/B Mabea “comun” 2 217-219 CG 3768 280A/B Mabea superbrondu 6 220-223 CG 3807 339A/B Mabea occidentalis Benth. - 223 CG 4546 1678A/B Mabea 1 227 CG 4232 1081A/B Phyllanthus cf urinaria L. 1 227 CG 4105` 710A/B Richeria racemosa 1 227 CG 4240 1068A/B Alchornea triplinervia 1 261 CG 4433 1509A/B Acalypha cf 5 227 CG 4218 993A/B Acalypha cf cuneata Poepp & Endl. 1 220 CG 3906 585A/B Sapium cf ovobatum K ex Müll. Arg 1 261 CG 4390 1554A/B Croton 1 227 CG 4230 1094A/B Nealchornea cf 1 220 CG 3927 547A/B Pera cf 1 227 CG 4318 1367A/B Pera duguet 1 227 CG 4250 1185A/B Hyeronima 1 227 CG 3601 - Hyeronima cf 1 227 CG 4496 - Acalypha cf 5 227 CG 4218 993 Indet - - CG 4404 - Richeria racemosa 1 217 CG 3774 274A/B Indet 1 217 CG 3787 - Indet 1 220 CG 3923 - Indet 2 238 CG 4475 - Indet - - 758 - Indet 1 227 CG 4262 - Fabaceae Inga auristellae Harms - 200 CG 4035 - Inga 1 199 CG 3583 10A/B
320 Inga 1 238 CG 4484 1618AA/B Inga velutina Willd 1 199 CG 3588 16A/B Inga rusbyi Pittier - 219 CG 3662 - Inga brachyrachis/capitata 1 219 CG 3665 - Inga aff umbratica CG 4455 1668A/B Inga 1 219 CG 3667 - Inga 1 219 CG 3682 186A/B Inga cordatoalata Ducke 1 219 CG 3653 193A/B Inga umbratica Poepp 1 219 CG 3655 196A/B Inga 1 217 CG 3778 - Inga acreana Harms 2 217 CG 3741 220A/B Inga 1 217 CG 3740 - Inga - 217 CG 3913 - Inga multijuga o ruiziana 7 217-227 CG 3745 252A/B Inga cf microcoma Harms - 200 CG 3798 343A/B Inga sarayacuensis T.D.Penn 2 200 CG 3763 285A/B Inga - 210 CG 3873 452A/B Inga alata - 210 CG 3856 461A/B Inga 1 210 CG 3855 - Inga 1 210 CG 3872 - Inga tenuistepula Ducke - 220 CG 4141 - Inga cf splendens Willd. - 219 CG 4350 1430A/B Inga 1 219 CG 4338 1437A/B Inga - 261 CG 4311 - Inga - 261 CG 4523 - Inga umbellifera (Vahl) Steud ex DC. 2 261 CG 4432 - Inga cf stenoptera Benth. 1 261 CG 4434 1501A/B Inga spectabilis (vahl) Willd. 2 261-223 CG 4426 1724A/B Inga capitata Desvaux CG 4473 1602A/B Inga 1 261 CG 4394 1539A/B Inga 2 227-261 CG 4408 1541A/B Inga cf umbelliferae (Vahl) Steud. ex DC. - 261 CG 4556 1760A/B Inga velutina Willd. 2 261 CG 4428 1550A/B Inga 1 261 CG 4403 1556A/B Inga 1 238 CG 4473 1602A/B Inga - 238 CG 4513 - Inga 1 223 CG 4540 - Inga cf suaveolens Ducke 9 223-238 CG 4513 1691A/B Inga spectabilis Vahl. Willd 1 227 CG 4111 703A/B Inga aff setulifera T.D. Penn CG 4523 1763A/B Inga 1 227 CG 4099 716A/B Inga 1 227 CG 4215 1001A/B Inga punctata 5 227 CG 4213 1005A/B Inga tessmannii Harms 1 227 CG 4289 1221A/B Inga CG 3789 250A/B Inga 1 227 CG 4285 1235A/B Inga marginata CG 4374 1481A/B Inga aff heterophylla Willd 1 227 CG 4272 1282A/B Inga CG 3794 296A/B Abarema jupunba 1 210 CG 3871 492A/B Lecointea peruviana 1 217 CG 3769 272A/B Bauhinia brachycalyx 1 227-238 CG 4452 1669A/B Dalbergia frutescens (Vell.) Britton CG 3822 381A/B Macrolobium angustifolium (Benth.) Cowman CG 3636 78A/B Macrolobium gracile Spruce & benth CG 3709 128A/B Macrolobium archeri R.S.Cown CG 3744 251A/B Macrolobium colombianum aff CG 3686 132A/B Zygia heteroneura Barneby & Crimes 6 227 CG 4086 645A/B Hymenae oblongifolia 3 227 CG 4088 643A/B Zygia aff latifolia (L.) Fawcett & Rendle 1 227 CG 4476 1602A/B Zygia schultzeana 1 277 CG 3589 1555A Zygia? 1 CG 4505 1732A/B Indet 1 219 CG 4338 1437A/B Brownea macrophylla Hort ex Mast 3 277 CG 4189 937A/B Calliandra trinervia 1 277 CG 4293 1204A/B Marmaroxillon CG 4410 1532A/B Indet 1 277 CG 4100 734A/B
321 Dalbergia 1 277 CG 4188 831A/B Dussia 1 277 CG 4479 1592A/B Zygia 2 261 CG 4374 1481A/B Browneopsis ucayalina Huber 78 223-238 CG 4328 1450A/B Macrolobium 5 217 CG 3728 940A/B Swartzia multujuga Brownwea grandiceps CG 3622 32A/B Flacourtiaceae Casearia arborea (Rich.)Urb. 1 277 CG 4296 Casearia 7 277 CG 4257 Casearia cf javitensis Kunth 277 CG 432A/B Casearia nigricans Sleumer 277 CG 4257 813A/B Casearia CG 4414 1511A/B Carpotroche cf 1 199 CG 3642 69A/B Lozania 1 227 CG 4060 593A/B Ryania cf speciosa Vahl 277 CG 4166 866A/B Tetratylacium macrophyllum Poepp 217-277 CG 3754 301A/B Casearia cf prunifolia Kunth( Ryania en list) 277 CG 4096 738A/B Casearia 277 CG 4450 843A/B Hasseltia floribunda Sw 277 CG 4115 701A/B Casearia CG 4257 1148A/B Neosprucea cf 277 CG 4535 1714A/B Lacistema cf 277 CG 4132 803A/B Casearia 261 CG 4431 1505A/B Casearia 238 CG 4450 1660A/B Casearia 277 CG 4221 980A/B Hippocrateaceae Salacia cf spectabilis Ac. Smith CG 4226 921A/B Salacia CG 4077 661A/B Humiriaceae Vantanea CG 3770 277A/B Ventanea 219 CG 4343 1412A/B Lauraceae Cinnamomun triplinervia 277 CG 4174 873A/B Endlicheria 277 CG 4168 881A/B Edlicheria 3 238 CG 4463 1587A/B Nectandra 1 199 CG 8 Nectandra 1 261 CG 4421 1530A/B Nectandra 2 261 CG 4398 1564A/B Nectandra 1 238-277 CG 4449 1657A/B Ocotea? 1 199 CG 3585 6 Ocotea? 2 199 CG 3617 38 Ocotea? 1 199-217 CG 3602 57A/B Ocotea 1 261 CG 4420 1488A/B Ocotea 2 219 CG 4101 730A/B Persea 1 227 CG 4180 854A/B Mezilaurus 1 222-219 CG 3712 105A/B Rhodostemonodaphne 3 217 CG 3790 297A/B Indet 1 CG 3817 307A/B Indet. 1 199-227 CG 3611 24A/B Indet 2 199 CG 3634 76A/B Indet 3 222-227 CG 3717 107A/B Indet 1 199-200 CG 3671 177A/B Indet 3 222 CG 3652 194A/B Indet 2 217-277 CG 3775 233A/B Indet 1 217-238 CG 3776 234A/B Indet 1 217 CG 3760 267A/B Indet 2 217 CG 3770 297A/B Indet 1 200 CG 3799 377A/B Indet 1 200 CG 3869 413A/B Indet 2 200 CG 3875 432A/B Indet 3 200-261 CG 3874 442A/B Indet 1 200-222 CG 3859 489A/B Indet 1 220 CG 3920 500A/B Indet 1 261 CG 4393 1542A/B Indet 1 223 CG 4527 1686A/B Indet 3 223 CG 4514 1715A/B
322 Indet 1 223-277 CG 4528 1723A/B Indet 1 223 CG 4561 1738A/B Indet 1 277 CG 4164 888A/B Indet 1 277 CG 4198 949A/B Indet 1 277 CG 4207 1026A/B Indet 3 277 CG 4236 1067A/B Indet 1 200-277 CG 4228 1095A/B Indet 1 277 CG 4227 1098A/B Indet 1 277 CG 4268 1107A/B Indet 1 277 CG 4260 1142A/B Indet 277 CG 4290 1218A/B Indet 2 cf 1266 Indet 1 277 CG 4273 1276A/B Indet 1 277 CG 4279 1278A/B Indet 1 277 CG 4085 647A/B Indet 1 277 CG 4125 819A/B Indet 1 277 CG 4185 840A/B Indet 277 CG 4280 1240A/B Indet 1706A/B Lecythidaceae Eschweilera andina 8 222-277 CG 3702 154A/B Eschweilera cf rufifolia O tessma S.A. Mori 2 223-261 CG 4416 1577A/B Eschweilera 1 199 CG 3590 7A/B Eschweilera 4 261 CG 4370 1477A/B Eschweilera 1 238 CG 4469 1594A/B Grias neuberthii J.F. Macbr 2 222 CG 3700 115A/B Gustavia longifolia Poepp ex berg 8 220-277 CG 3632 68A/B Lecythis 1 200 CG 3865 478A/B Lecythis 2 223-238 CG 4462 1581A/B Indet 2 277 CG 4154 ? Malpighiaceae Byrsonima cf 1 238 CG 4472 4A/B Melastomataceae Blakea 2 223-261 CG 4567 1747A/B Miconia 1 217 CG 3784 ? Miconia 1 261 CG 4425 1492A/B Miconia 5 223-261 CG 4506 1701A/B Miconia 1 277 CG 4044 610? Miconia 1 277 CG 4150 765A/B Indet 1 217 CG 3771 279? Indet 1 200 CG 3803 318A/B Indet 1 200 CG 3826 403A/B Indet 1 200 CG 3868 494A/B Indet 1 223 CG 4539 1725? Indet 1 277 CG 4104 712A/B Mouriri 1 223 CG 4533 1716A/B Mouriri 1 277 CG 4267 1111? Indet 1707A/B Meliaceae Cedrela cf 1 261 CG 4417 1476A/B Guarea cf goma Pulle 1 219 CG 4357 1448A/B Guarea pterorachis Harms 7 200-220 CG 3736 242A/B Guarea purusana cf 5 277 CG 4063 589A/B Trichillia septentrionalis CG 3650 195A/B Guarea 1 222 CG 3715 122? Guarea 1 217 CG 3772 236? Guarea “gomma” 6 220-277 CG 3928 527A/B Guarea 5 220-238 CG 3912 542A/B Cabralea cangeriana 1 261 CG 4435 1508A/B Guarea silvatica 2 261-277 CG 4415 1517A/B Guarea guentheriana 12 220-277 CG 4466 1579A/B Guarea 3 238-220 CG 4448 1650A/B Trichilia 1 223 CG 4545 1687A/B Guarea 7 277 CG 4176 847A/B Guarea 1 277 CG 4217 992A/B Guarea 1 277 CG 4306 1392? Trichilia 16 222-277 CG 3684 150?
323 Trichilia 7 238-261 CG 4368 1479A/B Indet 2 277 CG 4210 1009? Indet 1 277 CG 4316 1325A/B Menispermaceae Abuta cf 1 219 CG 4363 1464 Monimiaceae Mollinedia 1 277 CG 4147 774A/B Siparuna 1 277 CG 4258 1147A/B Siparuna 1 277 CG 4324 1317A/B Moraceae Brosimum 2 200 CG 3832 357A/B Brosimum 1 277 CG 4275 1269A/B Castilla cf 3 217-261 CG 3751 246? Castilla 4 238-277 CG 4478 1627? Clarisia 4 199-277 CG 3603 54A/B Clarisia 2 261-277 CG 4382 1572A/B Ficus 2 222 CG 3720 97 Ficus 1 238 CG 4497 1624A/B Ficus 2 223 CG 4518 1731A/B Ficus 1 277 CG 4287 1229A/B Ficus 1 277 CG 4283 1258? Ficus 2 199 CG 3703 93? Helicostylis 2 277 CG 4095 737A/B Maquira 1 261 CG 4411 1535A/B Naucleopsis 1 277 CG 4195 924A/B Naucleopsis cf 2 222-217 CG 3737 214A/B Naucleopsis CG 3841 383A/B Perebea 6 220-222 CG 3673 180? Poulcenia armata 1 219 CG 4344 1424A/B Pseudolmedia 1 238 CG 4438 1670A/B Sorocea 2 199 CG 3689 143A/B Sorocea 56 200-222 CG 3800 337A/B Batocarpus “ramaroja” 1 220 CG 3884 516A/B Sorocea 2 219 CG 4349 1432A/B Sorocea 1 277 CG 4291 1215A/B Sorocea? 3 217 CG 3691 112? Indet 2 199-238 CG 3626 18? Indet 2 222-217 CG 3725 125A/B Indet 1 217 CG 3669 147A/B Indet 1 222 CG 3677 189? Indet 2 222 CG 3739 244A/B Indet 1 217-22 CG 3758 261A/B Indet 1 200 CG 3863 486A/B Indet 5 200-277 CG 3911 582? Indet 9 238 CG 4487 1597? Indet 2 277 CG 4069 675A/B Indet 1 277 CG 4325 1306A/B Myristicaceae Otoba parvifolia cf CG 4456 1647A/B Compsoneura CG 4128 816A/B Compsoneura CG 4446 1640A/B Virola duckei A.C. Smith CG 4340 1411A/B Virola mollis (AC. DC) Warb CG 3619 44? Virola multinervia CG 4234 1072A/B Virola obovata CG 3866 417? Virola flexuosa CG 4536 1712A/B Virola dixonii CG 3791 205A/B Virola CG 4461 1634? Virola CG 4091 634A/B Virola CG 4264 1128A/B Virola CG 4091 634A/B Virola CG 3845 367A/B Virola CG 3680 174A/B Virola pavoni CG 1009A/B Iryanthera ulei CG 718? Iryanthera CG 4308 1384A/B Otoba CG 4559 1749?
324 Iryanthera CG 3917 550A/B Otoba glycicarpa CG 3685 135A/B Otoba 1702A/B Myrsinaceae Ardisia CG 3795 303A/B Ardisia “semiovada” CG 4455 1641A/B Cybianthus CG 4130 808A/B Stylogyne CG 4335 1436A/B Myrtaceae Eugenia CG 3645 43A/B Eugenia CG 3899 502A/B Eugenia CG 4052 598? Eugenia CG 4183 836A/B Eugenia aff stipitata CG 4292 1213A/B Eugenia CG 4358 1475A/B Eugenia feijoi CG 4360 1460A/B Calophyllum CG 4134 796A/B Plinia CG 4229 1024A/B Plinia CG 3910 538A/B Indet CG 3706 98? Indet CG 3761 288? Indet CG 3804 341A/B Indet CG 3838 396A/B Indet CG 4360 1460A/B Eugenia CG 4422 1504A/B Indet CG 4483 1584A/B Indet CG 4465 1588A Indet CG 4555 1762A/B Indet CG 4549 1767? Indet CG 4551 1768? Indet CG 4037 618A/B Indet CG 4119 691A/B Indet CG 4152 762A/B Indet CG 86A/B Indet CG 4175 861A/B Indet CG 3901 575A/B Indet CG 4352 1454A/B Indet CG 4392 1543A/B Plinia CG 4512 1680A/B Indet CG 4542 1693A/B Indet CG 4520 1722A/B Nyctaginaceae Neea “supercrasa” CG 3877 485A/B Neea CG 4488 1672A/B Neea “altomina” CG 4502 1728A/B Neea “popular” CG 4117 695A/B Ochnaceae Ouratea “flaquita” CG 4467 1622A/B Olacaceae Heisteria CG 4315 1333A/B Heisteria CG 4266 1109A/B Heisteria CG 3769 269A/B Indet CG 4076 658? Indet CG 4544 1692? Piperaceae Piper CG 1459A/B Polygonaceae Triplaris Americana CG 3815 1429A/B Coccoloba fallax CG 4110 704A/B Coccoloba densifrons CG 3919 543A Indet CG 4133 800? Proteaceae Roupala montana CG 59A/B Rhamnaceae Colubrina arborescens (Mill.) Sarq. 1607 Rubiaceae Borojoa CG 4193 925A/B
325 Duroia hirsuta cf CG 4353 1449A/B Faramea glandulosa Poepp CG 4565 1748A/B Psychotria CG 4445 1644A/B Pentagonia spathicalyx CG 3713 104A/B Pentagonia CG 4329 1439A/B Posoqueria latifolia CG 4108 688A/B Posoqueria? CG 4169 880A/B Posoqueria? CG 3816 321A/B Cousarea brevicaulis Krause CG 3812 335A/B Cousarea cephaloides Taylor CG 4277 1262A/B Randia CG 3659 208A/B Randia CG 4253 1178A/B Simira cordifolia CG 4107 700A/B Simira wurdackii Steyerm. CG 4492 1643A/B Wittmackanthus stanleyanus cf CG 4071 671A/B Psychotria stenostachya CG 3616 788A/B Randia armata (Sw.) DC. CG 4355 1458A/B Indet CG 3584 12? Indet CG 3593 19? Cousarea CG 3711 94A/B Simira CG 3663 209A/B Alseis CG 3729 228A/B Cousarea cf CG 3813 317A/B Indet CG 3876 483A/B Ladenbergia CG 3925 526? Indet CG 4492 1643A/B Indet CG 4534 1711A/B Cousarea macrophylla aff CG 4530 1719A/B Indet CG 4558 1742? Indet CG 4522 1750A/B Indet CG 4554 1765? Alibertia CG 4223 983A/B Indet CG 4322 1321A/B Indet CG 4298 1403A/B Sabiaceae Discophora guianensis CG 4079 657A/B Sapindaceae Allophylus CG 4347 1425A/B Allophylus CG 4409 1544A/B Allophylus CG 4146 770A/B Talisia CG 4330 1446A/B Indet CG 4326 1305? Sapotaceae Chromolucuma cf CG 3705 134A/B Chrysophyllum CG 4508 1684A/B Sarcaulus CG 4205 915A/B Micropholis venulosum CG 3828A 374A/B Micropholis “molihoniana” CG 3757 1614A/B Pouteria bilocularis CG 4034 623A/B Pouteria CG 3765 80A/B Pouteria CG 3620 48A/B Pouteria CG 3704 88A/B Pouteria CG 3708 100A/B Pouteria CG 3724 108A/B Pouteria CG 3690 118A/B Pouteria CG 3701 124A/B Chrysophyllum venezuelanense (Pierre)Penn CG 3693 157A/B Pouteria vernicosa T.D. Penn CG 215A/B Pouteria quianensis Aubl. CG 4224 986A/B Pouteria CG 3657 188A/B Pouteria multiflora (A.DC) Eyme CG 3746 238A/B Pouteria platiphylla cf CG 1068? Pouteria CG 3654 183A/B Pouteria CG 3860 443A/B Pouteria CG 4489 1655? Pouteria CG 4504 1733A/B Micropholis 1619A/B
326 Simaroubaceae Picramnia CG 4373 1522A/B Simaba “smed” CG 4156 752A/B Simaruba CG 4196 939A/B Solanaceae Cestrum CG 4300 1398A/B Solanum CG 4192 957A/B Staphyleaceae Turpinia occidentalis CG 4051 599A/B Tapura CG 4162 894A/B Sterculiaceae Herrania CG 4179 856A/B Theobroma subincanum cf CG 3914 551A/B Theobroma speciosum cf CG 4443 1651A/B Styracaceae Styrax argenteus CG 3648 60A/B Theophrastaceae Clavija CG 3905 565A/B Tiliaceae Apeiba membranacea CG 4047 606A/B Apeiba aspera CG 4041 612A/B Apeiba CG 3900 524A/B Mollia gracilis CG 4209 1014A/B Ulmaceae Celtis schippii Standl. CG 512A/B Celtis CG 4491 1617A/B Trema micrantha CG 121A/B Urticaceae Urera CG 4098 714? Urera cf CG 4256 1156 Violaceae Gloesospermum ecuadorensis CG 3892 506A/B Leonia crassa CG 4136 791A/B Leonia CG 1438A/B Rinorea apiculata Hekking CG 4045 607A/B Rinorea lindeniana (Tul.) Kuntze CG 4282 1252A/B Rinorea viridifolia Rusby CG 1435A/B Vochysiaceae Qualea paraensis CG 3842 348A/B Vochysia CG 4511 1689A/B Collected PLOT 1-9 and problems PITMAN Indet H1708 Indet 1710A/B Annonaceae-Guatteria 1713A/B Indet 1726A/B Indet 1735A/B Indet 1743A/B Fabaceae 1752B Fabaceae 26A/B Indet 161A/B Moraceae 00A/B Indet 218A/B Lauraceae 231A/B Indet 255A/B Meliaceae? 262A/B Fabaceae? 531A/B Fabaceae/Inga 325A/B Indet 332A/B Annonaceae 372A/B Indet 000A/B Sterculiaceae-Theobroma 429A/B Indet 531A/B Rubiaceae? 541A/B Annonaceae cf 564A/B Theaceae? 1413A/B Indet 1423A/B Indet 1468A/B
327 Indet 1607A/B Indet 1632A/B Anacardiaceae/Burseraceae 1667A/B Euphorbiaceae- Mabea 273A/B Fabaceae? 1759A/B Indet 273A/B Indet 8A/B INdet 1769A/B Indet 1551A/B Indet 1755A/B Indet 298A/B Indet 438A/B Indet 1736A/B Indet 450A/B Indet 90A/B Indet 528A/B Indet H86A/B Indet PPP1083 Indet 1273A/B Indet 1295A/B Indet 1301A/B Indet 1329A/B Indet 1336A/B Indet 1369A/B Indet 882A/B
328 APPENDIX 4
The morphological basis for tree identification
The traditional classification process involves the following steps. First, it must be decided where the specimen may be taxonomically located. This step is technically known as identifying the supra taxa, and is carried out through observation of morphological features like leaf arrangement, stipules etc. This process is typically performed during fieldwork, by applying the dendrology technique. The specimen is subsequently analysed and scientifically named in the herbarium using traditional Linnaean taxonomy based on flower characteristics.
If the taxon has not been discovered and described in the scientific literature, it will be necessary to describe the plant as a new species. The following sections give more detail on how the whole process is carried out. The basic procedures are generally summarized in five stages: collecting, sorting, preserving, drying and classifying.
Tree taxonomy is based on a series of paths, which are used in order to achieve any identification. The details are taken in order moving from general to detailed characteristics. The process begins with a description of the trunk, then moves on to branches and finally to the stipules. The most important features to look for in identifying a tree are tree form, trunk, branches, leaves, twigs and stems, flowers and fruits. A magnifying glass often has to be used especially for tiny parts such as anthers or for any pubescence. Figure 1 below shows the different plant parts used at each identification step. (a) tree architecture (b) trunk (c) bark (d) stem
329 (e) leaf parts (f) leaf type (g) leaf arrangement (h) leaf shape (i) leaf apex (j) leaf base.
How to identify trees?
TRUNK
External Form Architecture
Trunk features Bark types
Inner bark Outer bark
BRANCHES
Twigs Stems
LEAVES
Parts Types
Arrangement Shape
COMPLEMENTARY CHARACTERS
Stipules Exudates
Colours Smell
Implementation and use of identification keys
Figure 1 Field identification diagram
330 Tree architecture
Information on tree architecture may be used to explain crown property variation.
This thesis will not include this kind of aspect because architectural measurements are complicated and time consuming to make for even one taxon.
The term ‘tree architecture’ is defined as “a visible morphological expression of the genetic blueprint of a tree at any one time” (Halle, 1978) as observed in
Figure 2. In 1970, Halle and Oldeman published an essay about the architecture and growth dynamics of tropical trees. Following this, a series of 23 types of architectural models based on the branching system, was published (Halle,
1978).
Figure 2 Brazilian pepper tree Schinus terebithifolious and the equivalent Attim architectural model. (Photos from University of Florida 2004)
Binoculars may be used to obtain ground information such as the branch angle, stem shapes, leaf arrangement, leaf shape, etc. From this information a pre- identification can be inferred before sample collection.
331
The trunk
It is almost impossible to observe the woody section of the tree using aerial images. Sometimes only the larger branches are visible in the canopy of emergent trees. On the contrary, from the ground, the trunk is usually the most obvious feature. Nevertheless, a description of the trunk and its bark are necessary because they contain key information about diagnostic characteristics.
Experience is the best way to learn to identify trees from their bark characteristics, although it is not an easy skill to acquire. The shape, texture, thickness and colour of the bark are the best characteristics to observe (Figure 3).
The only disadvantage of using bark is that there are differences in the bark between young and mature trees of the same species.
332
Figure 3 Differences in outer bark types in four rainforest trees. (Photos from
Gutierrez et al., (1996).
Bark types are not always reliable for identification, as some species within the same family have very similar bark, for example, trees from the Eucalyptus family have a papery texture, a long shape, white colour and aromatic smell. The inner layers of bark, which can be accessed by cutting a slice of bark with a machete, also have distinctive features that are useful for identification. The colour of the bark and the type of outer fibres should be noted, as in the case of
Annonaceae of which 99.9 % of species have fibres criss-crossing each other.
333 Other complementary inner bark characteristics include: (a) number of layers (b) type of layer and its composition formed by outer, mid and inner bark (c) heart- wood with its hardness and colour (d) presence of buttresses, aerial roots etc. (e) type of latex, judged by colour but not by the flavour, as there is a high degree of risk of poisoning Figure 4.
Figure 4 Tropical tree inner bark characteristics (Photos from Gutierrez et al.,
(1996).
Twigs and stems
Several key features of twigs, including buds, leaf scars, lenticels and pith can help identify the tree (Figure 5). Others such as thorns, spines, colour, flavour,
334 and odour of stems can also be taken into consideration in the identification process. In many cases, these parts are specific to certain families. For example,
Moraceae has terminal buds, conspicuous scars are present in Flacourtiaceae, lenticels are frequent in Fagaceae, long pith is common in Acanthaceae, thorns are always present in Saxifragaceae and spines in Arecaceae. However, the stem is also important as it bears the branch and the leaves. It also has nodes, which are the point of the stem through which one or more leaves are inserted
(illustrated as lateral bud in Figure 5).
Figure 5 Twig and stem. (From http://forestry.about.com)
Leaf parts
After the leaf type and arrangement, leaf parts are the next important characteristic to identify trees; these parts include the blade, petiole and stipules.
The lamina is the blade part of the leaf; these leaves are attached to the stems by the petiole. The petiole may be either short or long, and grows in a variety of different shapes, and indeed may not exist in some trees. Between them there are structures named stipules that are one or a pair of small, scaly or leaf-like organs
335 (Figure 6). Stipules can have a broad variety of shapes and sizes, and often they are not visible without using a magnifying glass.
Figure 6 Part of a simple leaf (From http://forestry.about.com)
Leaf type
Leaf type is the main feature used for separating families correctly and refers to how the leaves are displayed on the branch. There are two different leaf types: compound and simple leaves. A simple leaf has a single blade, and a compound leaf has two or more blades that are called leaflets. There are several types of compound leaves, which are described in Figure 7. The stalk to which the blades are attached is called a rachis. The arrangement of the leaflets on the rachis determines the particular type of compound leaf.
336 Pinnately compound Bipinnately compound Even-pinnate
Odd-pinnate Palmately compound
Figure 7 Compound leaf types. (From http://forestry.about.com)
A pinnately compound leaf has leaflets arranged laterally on the rachis; examples of families with this characteristic are Meliaceae (Guarea gomma), Sapindaceae
(Tapirira guianensis) and Burseraceae (Protium glabresens). A bi-pinnately compound leaf has multiple leaflets attached to a leaf-bearing stalk from the rachis, and the most common families within this group are Bignoniaceae
(Jacaranda copaia) and Cunnoniaceae (Weimmania pubescens). A leaf with an even number of leaflets is called even-pinnate, which can be found in the family
Fabaceae (Inga thibaudiana). A leaf with an odd number of leaflets on the rachis is called an odd-pinnate leaf, as in the Fabaceae family (Brownea grandiceps).
337 Finally, palmately compound leaves have each leaflet attached to a common point, as in the common family Cecropiaceae (e.g. Cecropia sciadophylla).
Leaf arrangement
The next step in identification is examining the leaf arrangement. Simple leaves are attached directly to the stem, while in compound leaves the leaflets are attached to the rachis. The rachis can be recognised easily as its function is to hold leaflets while stems only hold petioles. Leaves are arranged in one of three ways: opposite, verticillate and alternate (Figure 8). Opposite leaves are those that are even and opposite each other on sides of the twig. Three or more leaves found at the same node, or bud on a twig are called a verticillate arrangement.
Alternate leaf arrangement occurs when one leaf is attached at each node, arranged in a spiral pattern around the twig.
Opposite Alternate Verticillate
Figure 8 Types of leaf arrangements. (From http://forestry.about.com)
Some representative examples of tree families with opposite leaf arrangement are
Rubiaceae (Warscewiczia coccinea) and Violaceae (Rinorea viridifolia. For alternate arrangements, there are many common families such as Moraceae
338 (Pseudolmedia laevis), Myristicaceae (Iryanthera juruensis). Families with whorled leaves include Vochysiaceae (Qualea amazonica).
Leaf shape
Leaf shape can be a reliable taxonomical indicator for identification, but many forms are present in the tropical forest, making the process difficult without previous training also within a species (indeed within a single crown), leaf shape may vary greatly. In compound leaves, the leaflets are of different sizes depending on whether they are located on the tip or on the side of the rachis. The most common leaf shapes are shown below Figure 9, but it should be noted that there are hundreds of variations of these samples.
Asymmetrical Elliptical
Linear Oblanceolate
Oblong Oval
Figure 9 Most common leaf shapes. Photos from Ribeiro, et al. (1999b)
339 The next three botanical characters are not crucial but give small details that are present in some uncommon families and are of strong importance for naming species.
Leaf apex and base
The tip of a blade that is farthest from the petiole, or stalk, is called the apex. The part of the blade nearest to the petiole is called the base. The common shapes of the apex and base are shown in Figure 10.
Apiculate Aristate
Caudate Cuspidate
Cunate Obtuse
340
Truncate Auriculate
Figure 10 Common shapes of leaf apices and bases. Photos from Ribeiro, et al.
(1999b)
Flowers
The following aspects are not part of the traditional sterile plant identification performed in the field. Nevertheless, botanists very often use these characters during taxonomical identification in the herbarium. The flower and fruit usually have much more species-specific characteristics than other organs, and therefore tree reproductive organs are particularly useful for taxonomical classification.
This importance is particularly clear in the traditional Linnean binomial taxonomy, which has had a strong influence on the whole field of taxonomy.
This scheme includes not only plant structure and size but also flowering sections of plants like petals, pistils, style forms and number. A complete flower has four parts: the calyx (composed of sepals), corolla (composed of petals), stamens and pistils (Figure 11).
341
Flowers attached to the trunk (Brownea Holding flowers (Brugmansia sp). sp) From www.ecuador-travel.net From www.livingrainforest.org/
Figure 11 Diagram of the parts of a flower (Weier et al., 1974) and examples of two types of tropical floral characteristics.
342
Miscellaneous characteristics
There are also other characteristics that are sometimes used to identify trees, including exudates, odours, and colours. In addition, the surface texture of the leaf is another means of identification. The leaf hair type, resin glands, waxes and scales provide valuable clues for naming a tree. The texture of the leaf may feel like leather or like paper (a function of leaf thickness). Leaf venation is also a useful indicator. Sometimes species coexist (and co-evolve) with insects and those interactions use structures that are present only in single families such as
Boraginaceae or Cecropiaceae. For example, the twigs on certain trees develop nodes that are used as ants’ nests, or cavities on the stems that form channels inhabited by insects.
There are several types of exudates that contribute to identifying species: sap, watery, glue, resin, etc. Each one has specific colours, textures and smells; notes should be taken about where exudates are emanating from, for example, after cutting into the bark, the Amazonian rubber tree exudes sap for around 24 hours, after which, the amount of exudate decreases.
343
APPENDIX 5
The process of traditional tree taxonomic identification
Historical synopsis
Plant taxonomy is a complex science which allows taxa to be identified by retrieving information on their structure and morphology using a classification system (Contreras, 2003). Carolus Linnaeus founded the field of plant taxonomy, called ‘binomial nomenclature’. The binomial nomenclature system is based on descriptions of plant reproductive organs. This methodology has been used for more than 200 years, though the techniques are unsophisticated by today's scientific and technical standards.
In 1760, the Spanish botanist Jose Celestino Mutis disembarked in the port of
Cartagena – Nueva Granada, in what is now known as Colombia (but actually incorporated modern-day Colombia, Ecuador and Venezuela). This arrival marked the advent of Linnean binomial taxonomy in South America. In 1783,
Caballero y Góngora, a lord of the Spanish Monarchy, ordered the official beginning of the Nueva Granada Botanical Expedition, led by Mutis with
Colombian naturalists such as Francisco Jose de Caldas, Eloy Valenzuela,
Francisco Matis and others (Vezga, 1971) working alongside him.
They found unexplored and diverse forest, and their aim was to write a complete floral record for Nueva Granada. Although many species were collected and described, the expedition (unsurprisingly) failed to record all the plants in
344 Colombia, Ecuador, Panama and Venezuela. However, this failure was not due to incapable naturalists, but rather because of the immense biodiversity they encountered. They used the Linnaean taxonomical nomenclature, which focuses on flowers, and though they were only able to collect around 20,000 plants, only
2000 species were described and published in over 45 years of subsequent museum work.
The historical trend in taxonomical research has been based on species diversity as the best measure of conservation value as stated by Prance (1994). This is not the only method that exists, however it is the most popular and most widely used.
There are some alternative new generation techniques which involve not only the flower but also the fruit or sterile organs such as leaves, bark etc. For example, the identification of many tropical plant families that are identify by morphological and architectural features (Keller, 2004).
Practical field guides with ample illustrations like A Field Guide to the Families and Genera of Woody Plants of Northwest South America: With Supplementary
Notes on Herbaceous Taxa by Gentry (1993) or examples from specific countries such as the Bolivian tree guide (Killeen et al., 1993) and the 350 tree species from Brazil described in Lorenzi (1992) have contributed to improving knowledge in this field, but there is still a large research gap to fill in terms of the large number of unidentified species.
The experience in Central America has been better documented; for example, with the Costa Rica flora exploration studies (Holdridge, 1972; Jimenez, 1998).
345 The contribution of this group of botanists was to popularise methodologies for identification without flowers, using sterile organs (leaves, twigs, bark, odours, tastes, exudations, buttresses, etc).
In the South American region, identification without flowers has been explored in the work on neotropical families and genera in Colombia (Arango, 1972), and also in research in the south-western Chocó region. The rainforest in this region has been explored floristically and field guides produced, which are based on identification keys with sterile terminology (Maecha, 1990) for families and some genera of the Pacific Colombian coast and forests.
Today there is still a much wider literature on tree species described by their flowers (Morie, 1999; Steyermark et al., 1995; Vasquez, 1997), even though it has been proved that the non-floral identification technique is efficient less efficient that floral one at species level (Jimenez, 1998). This shows that the tree identification technique by non-floral organs has not yet been fully accepted by traditional taxonomical authorities. Consequently, there is a lack of supporting literature and carefully planned methodologies to fulfil the biodiversity conservation needs of tropical South America.
This section includes some general ideas about what is needed for the process of identifying trees in the tropical forest using traditional techniques, in order to provide a context for the later discussion of new techniques.
346 There is a vast literature about botanical collection, however it is difficult to find key references which describe the basic steps required for identifying a botanical sample. It is essential to clarify that this technique has changed little since the first expeditions to the tropical forest in the 18th century. The most descriptive recent references are given in Table 1.
Title Reference
Collecting Plant Specimens (An Oskins, (1982) Outline with Appendices)
A Selected Bibliography of Plant Hicks & Hicks (1978) Collection and Herbarium Curation
A Handbook of Field and Jain, (1977) Herbarium Methods. Today and Tomorrow The Preservation of Natural Wagstaffe & Fidler (1968) History Specimens
Manual for Tropical Herbaria Fosberg & Sachet (1965)
Collection and Care of Botanical Saville (1962) Specimens
Table 1 Key references on plant collection
General description
The following description of the collection process is based on the instructions given by Liesner (2000), but is complemented by information from a number of other herbarium manuals. Personal experience in the field has also been taken
347 into account as the literature does not always include the specifics of collection techniques applicable in the Amazon region.
Collecting and sorting
Collection is physically demanding, time-consuming and sometimes hazardous.
The basic equipment required is a field notebook, binoculars and a specimen press. On collecting plants, botanists have to choose the best branch to cut off in order to have at least three replicas of the sample. Cutting suitable branches for tall canopy or emergent trees may require a trimmer.
The samples then have to be carefully folded using a sheet of newspaper or a similar material for storage. At this time, it is very important to assign a serial number to the item, label the newspaper and record details about the plant in the notebook. It is preferable for the sample to be collected with flowers or fruit. If the collection has to be stored for longer than a day, the newspaper must be soaked with alcohol, and put into a hermetically sealed plastic bag in order to preserve the plant tissues. If this is not properly done, the sample will be structurally damaged or attacked by fungus. If the collector has enough knowledge, it is useful to make a preliminary identification at this stage.
In the field, the botanist’s experience plays an important role as they can use many alternative techniques to identify the sample. As mentioned previously, the main technique used to recognise plants in the field is dendrology. Jimenez
(1998) has confirmed that it is an effective method for field-based preliminary identification.
348
Dendrological methods use three characteristics in the series of descriptions to typify genera and families, namely: leaf class (e.g. simple, compound), leaf arrangement (e.g. opposite, alternate, etc.), and the presence and types of small organs attached to the stem between the leaves and the petiole, named stipules, which are very significant for splitting botanical families. These three types of characteristics are combined with others such as colour and type of exudates, dots on the leaves, leaf hardness, nectars, odours, etc.
Preserving and drying
The treatment has to be continued in the herbarium under the supervision of experts. The next step is drying, which is carried out with a press and sheets of corrugated paper. The set of pressed samples have to dry in an oven as long as the type of tissue texture requires, for example, for a sample from the rain forest, the drying time is on average 18 hours at 250 ºC. When drying, it is important to keep the samples in numerical order according to the collection voucher register.
After they are totally dry, all the sheets can be organized according to families to make any future manipulation easier, especially if there are many labels to put on each newspaper sheet. Finally, mounting specimens on high quality paper in suitable filing systems is important because this guarantees successful preservation in the herbarium archives.
Classification
An alternative identification technique is based on whether the sample has flowers; the procedure would then continue with a description of details about
349 the floral structures like stamens, petal colour, and number of petals. In some cases, extra information about the branches or even fallen leaves can serve as complementary data in order to make a robust identification. All this information will help finalise the classification process in the herbarium, using a microscope to detail small parts of the flower so as to follow specific dichotomous identification keys that contain the sequential path which will lead to the plant’s scientific name.
Once the collector has a dry museum specimen, it is time to refine the classification process, either with classical floral taxonomy or sterile recognition.
Both may be used depending on the amount of information in the sample. In the tropical forest, the majority of trees produce flowers only for a short period each year, which complicates the task of collecting fertile material.
350
APPENDIX 6
ONLINE KEY
351
352
353 Key one:
354
355
356
357
358
359 Key two:
360
361
362
363
364
365
366 APPENDIX 7
PRELIMINARY KEY
CROWN HIERARCHICAL KEY START 2 1 I. CROWN SINGLE MULTIPLE TYPE Without clear division within the crown With two or more large division within the crown
1 2 1 2 DISCONTINUOUS CONTINUOUS II. FOLIAGE Foliage concentred in some parts of the PALM SHAPE CLUMPING Foliage regularly distributed within the CONTINUITY crown; branches are visible in other parts Foliage radiating in star Crowns with more than one clump h REGULAR IRREGULAR A FOLIAGE FOLIAGE SURFACE
111 2 121 122 211 212 12 III. CROWN ARCHITECTURE FLAT ROUNDED OPAQUE LIGHT STAR ROSSETE LAYERED SEGMENTED
BRANCH ELEMENTS SEVERAL LEAVES SURFACE NOTHING IS OR AN ALMOST DISTINGUISHABLE SEGMENTS CURVATU VISIBLE CLUMPS HORIZONTAL THOUGH IN RE THOUGH PARTICLES SURFACE, THE FOLIAGE LAYERS MOSTLY THE FOLIAGE
1121 1122 SHALLOW DEEP
FOLIAGE FOLIAGE CONCENTRATED VERTICALLY TO THE TOP OF DISTRIBUTED THE TREE
IV. FOLIAGE TEXTURE
1 2
CLUMPS AND CLUSTERS LEAVES
22212 22211 22221 22222 GRANULAR SMOKY DOTTED GRAINY REPETITION INDIVIDUAL REPETITION OF REPETITION OF SMALL LARGE LARGER OF LEAVES LEAVES PARTICLES LEAVES NOT COMPACTED SEPARATED
367