published_or_final_version / Ecology and Biodiversity / Doctoral / Doctor of Philosophy

Manguezais são ecossistemas que se distribuem ao redor do globo e desempenham funções ecológicas que são fundamentais para os ambientes costeiros adjacentes. Além de fornecerem uma ampla variedade de organismos para o subsídio humano, são áreas de produção e exportação de matéria orgânica, e também fornecem abrigo, alimentação e local para a reprodução de diversos animais marinhos. Apesar de sua enorme importância, esse ambiente encontra-se fortemente ameaçado e sob risco de desaparecer. As árvores de mangue são o seu componente mais básico e visível, sendo consideradas bastante produtivas, com grande parte do carbono orgânico encontrado nesse ambiente sendo proveniente da liteira e raízes das plantas. Micro-organismos são extremamente diversos, dinâmicos e estão distribuídos por todo o planeta, desempenhando importantes funções ecológicas, e dentro dos manguezais, eles são responsáveis pela maioria das transformações de nutrientes. Diversos estudos apontam a importância da comunidade bacteriana para uma equilibrada manutenção dos processos que ocorrem nesse ambiente, sendo um destes a decomposição de serapilheira. Dessa forma, este trabalho teve como objetivo estudar a dinâmica da comunidade bacteriana durante o processo de degradação de material vegetal em sedimentos de manguezais do Estado de São Paulo. Para isso, foi realizado o sequenciamento em larga escala do gene rRNA 16S do domínio Bacteria presente sobre a superfície do material vegetal. Paralelamente a isto, foi feita a quantificação da emissão CO2, N2O e CH4, durante os diferentes estágios de degradação foliar e também a análise da dinâmica e estrutura das comunidades. Os resultados obtidos por meio de sequenciamento em larga escala do gene rRNA 16S apontam o filo Proteobacteria como o grupo dominante nos três manguezais estudados, independente da espécie de planta. A classe Gammaproteobacteria apresentou uma maior abundância nos estágios iniciais de decomposição do material vegetal (7 e 15 dias), sofrendo um decaimento bem acentuado nas fases mais avançadas (60 dias). A alfa diversidade sofreu um aumento ao longo do tempo, com todas as amostras exibindo valores maiores nos estágios finais do processo de decomposição foliar. Em relação à emissão de gases de efeito estufa (GGE) em tratamentos em microcosmo, para N2O e CO2 tempo e local foram significativamente importantes. Já para CH4 somente o local foi relevante nas taxas de emissão. Foi possível identificar grupos de bactérias predominantes nas fases de decomposição foliar ao longo do período estudado. Gammaproteobacteria foi uma classe que se apresentou em maior quantidade nos estágios iniciais (7 e 15 dias), enquanto Alphaproteobacteria foi um grupo mais expressivo nas fases mais avançados de degradação (30 e 60 dias). / Mangroves are ecosystems that are distributed around the globe and perform ecological functions that are critical to the adjacent coastal environments. In addition to providing a wide variety of organisms for human activities, they have high productivity and are known as areas of production and export of organic matter, and also provide shelter, food and location for reproduction of various marine animals. Despite its enormous importance, this environment is at risk of disappearing. Mangrove trees are its most basic and visible component, being considered quite productive, with most of the organic carbon found in this environment being from the litter and plant roots. Micro-organisms are extremely diverse, dynamic and are spread across the globe, contributing with important environmental roles, and within the mangroves, they are responsible for most of nutrient transformations. Several studies point to the importance of the bacterial community for the balanced maintenance of the processes that are occurring in this environment. One of the most crucial of them is the decomposition of leaf-litter. Thus, this study aimed to evaluate the dynamics of bacterial communities during the degradation of plant material on sediments of São Paulo State mangroves by high throughput sequencing of 16S rRNA gene of Bacteria present on the surface of the plant material. In parallel, we quantified the emissions of CO2, N2O and CH4 during the different stages of leaf degradation and also the analysis of the dynamics and structure of communities. The results obtained by the sequencing of the 16S rRNA gene indicate the phylum Proteobacteria as the dominant group in the three mangroves, regardless of plant species. The Gammaproteobacteria class showed a greater abundance in the early stages of decomposition of the plant material (7 and 15 days), suffering a very sharp decay in the later stages (60 days). Alpha diversity has increased over time, with all the samples showing higher values in the final stages of the leaf decomposition process. It was possible to identify groups of bacteria predominant in the stages of decomposition of plant material throughout the study period. Regarding greenhouse gas emissions (GHG) in treatments in microcosm, time and location were significantly important factors to N2O and CO2 emissions. As for CH4 only the site was relevant in emission rates. It was possible to identify groups of bacteria predominant in the stages of leaf decomposition during the studied period. Gammaproteobacteria was a class that contains the main amount in the early stages (7 and 15 days), while Alphaproteobacteria was a more significant group in the most advanced stages of degradation (30 and 60 days).

生長於潮間帶的紅樹林是熱帶和亞熱帶地區最具生產力的生態系統之一。香港擁有十個紅樹品種，其覆蓋面積約共三百五十公頃。位於香港西北面的米埔是現時香港最大的紅樹林區。這片紅樹林及其鄰近濕地於一九九五年被列為拉姆薩爾重要的濕地。隨著經濟的迅速發展、污染及一些不可持續的開發，全球紅樹林的面積不斷地萎縮。而香港的紅樹也正面對城市發展及基建的直接威脅。因此，了解及監測紅樹林的生長狀況、覆蓋面積的轉變是紅樹林保育的基礎。遙感是具有成本效益和能提供及時數據的技術，在紅樹林的生態保育及監測上發揮著重要功能。 / 是次研究選擇位於米埔的紅樹林區。通過結合高光譜和雷達數據以及實地磡測，以達到三個目的。第一，利用模式辨認分析找出可提高品種辨識度的光譜帶及雷達數據。第二，把挑選出來的光譜帶及雷達數據組合，利用不同的分類法包括最大概似法、决策樹 C5.0演算法、類神經網路及支持向量機進行紅樹林的品種分類，並籍此測試各分類法的精度。第三，利用植被指數及雷達數據中取得的參數為獨立變量，而在野外點測的葉面積指數 (LAI) 為因變量，通過迴歸分析以估算整片紅樹林的葉面積指數，籍此了解紅樹林現時的生物物理狀況。 / 根據特徵選擇的結果，位於高光譜數據中的綠波段 (570nm, 580nm, 591nm及601nm)、紅波段 (702nm)、紅邊位 (713nm)、近紅外波段 (764nm及774nm)、 短波紅外波段 (1276nm, 1316nm及1629nm) 以及在不同季節取得的過濾後向散射數據是最能辨識品種差異。 / 據品種分類的結果顯示，單用多時後向散射特徵數據存在很大誤差。而在大多的情況下，單用光譜數據比起混合光譜及後向散射數據的分類表現為佳。但對於某些品種來說，後向散射數據能給予比較準確的預測。另外，在同數據組合下，分類法在訓練精度上沒有多大的分別。除了類神經網路分類法以外，其他分類法的測試精度總比其訓練精度低。這說明類神經網路模型比起其他分類法的模型要為穩定，而决策樹模型則被過度訓練。根據生產者及使用者精度分析，因為缺乏足夠的訓練樣本，桐花樹及海桑屬的精度較其他品種為低。 / 據不同植被指數的簡單線性迴歸模型顯示，利用三角植被指數 (TVI)及修正葉綠素吸納比例指數一 (MCARI 1) 對於葉面積指數的估算是最準確。相反地，葉面積指數與從雷達數據中取得的參數關係則比較弱。這表示單用雷達參數不能對葉面積指數進行準確的估算。在結合植被指數及雷達參數的多元逐步迴歸分析下，三角植被指數及在灰度共生矩陣下得出的角二階矩參數能減低葉面積指數估算的誤差。總結以上兩項分析，光譜及雷達數據在紅樹林的品種分類及葉面積指數估算上有互補的作用。 / Mangrove is one of the most productive ecosystems flourished in the intertidal zone of tropical and subtropical regions. Hong Kong has ten true mangrove species covering an approximate area of 350 hectares. Mai Po locating in the northwestern part of Hong Kong nourishes the largest mangrove stand and it was listed as a Wetland of Importance under the Ramsar Convention in 1995. Over the years, areas of mangrove have been shrinking globally due to development, pollution, and other unsustainable exploitation and Hong Kong was no exception. In Hong Kong, mangroves are usually sacrificed for urban development and infrastructure construction. Therefore, it is crucial to monitor their growth conditions, change of extent and possible unsustainable practices threatening their existence. Remote sensing being a cost-effective and timely tool for vegetation conservation is most suitable for such purpose. / Taking Mai Po as study area, this study acquired satellite-borne hyperspectral and radar data supplemented with in situ field survey to achieve three purposes. First, features from the remotely-sensed data that are significant to species discrimination were identified through pattern recognition. Second, selected features grouped into different subsets were used to delineate the boundary of mangrove species through supervised classification. In the meantime, classifiers including maximum likelihood (ML), decision tree C5.0 (DT), artificial neural network (ANN) and support vector machines (SVM) were tested for their accuracy performance. The third purpose is to understand the current biophysical condition of mangrove through leaf area index (LAI) modeling by regressing field-measured LAI against vegetation indices, backscatter and textural measures. / Results from feature selection revealed that hyperspectral narrowbands locating in green at 570nm, 580nm, 591nm, 601nm; red at 702nm; red-edge at 713nm; near infrared at 764nm and 774nm and shortwave infrared at 1276nm, 1316nm and 1629nm as well as the multi-temporal filtered backscatter captured in different seasons have high sensitivity to species difference. / Species-based classification using multi-temporal backscatter features alone do not provide a satisfactory accuracy. Comparatively, results from pure spectral bands have better overall accuracy than that from combining spectral and radar features. However, radar backscatter does improve accuracy of some species. Besides, all classifiers had similar variations of training accuracy under the same feature subset. However, the testing accuracy is much lower with the exception of ANN. Performance of ANN was more stable and robust than other classifiers while serious overtraining occurs for the DT classifier. Moreover, most species were mapped accurately as revealed by the producer’s and user’s accuracy with the exception of A. corniculatum and Sonneratia spp. due to deficiency of training samples. / Simple linear regression model with VIs revealed that triangular vegetation index (TVI) and modified chlorophyll absorption ratio index 1 (MCARI1) had the best relationship with LAI. However, weak relationship was found between field- measured LAI and radar parameters suggesting that radar parameters cannot be used as single predictor for LAI. Results from stepwise multiple regression suggested that TVI combined with GLCM-derived angular second moment (ASM) can reduce the estimation error of LAI. To conclude, the study has demonstrated spectral and radar data are complementarity for accurate species discrimination and LAI mapping. / Detailed summary in vernacular field only. / Detailed summary in vernacular field only. / Detailed summary in vernacular field only. / Detailed summary in vernacular field only. / Detailed summary in vernacular field only. / Wong, Kwan Kit. / Thesis (Ph.D.)--Chinese University of Hong Kong, 2012. / Includes bibliographical references (leaves 434-472). / Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Abstract also in Chinese. / ACKNOWLEDGEMENTS --- p.II / ABSTRACT --- p.IV / 論文摘要 --- p.VI / TABLE OF CONTENTS --- p.VIII / LIST OF ABBREVIATIONS --- p.XIII / LIST OF TABLES --- p.XV / LIST OF FIGURES --- p.XVIII / Chapter CHAPTER 1 --- INTRODUCTION --- p.1 / Chapter 1.1 --- BACKGROUND TO THE STUDY --- p.1 / Chapter 1.1.1 --- Mangrove Mapping and Monitoring --- p.1 / Chapter 1.1.2 --- Mangrove Mapping and Monitoring --- p.3 / Chapter 1.1.3 --- Role of Remote Sensing in Mangrove Study --- p.4 / Chapter 1.2 --- OBJECTIVES OF THE STUDY --- p.6 / Chapter 1.3 --- SIGNIFICANCE OF THE STUDY --- p.7 / Chapter 1.4 --- ORGANIZATION OF THE THESIS --- p.8 / Chapter CHAPTER 2 --- LITERATURE REVIEW --- p.10 / Chapter 2.1 --- INTRODUCTION --- p.10 / Chapter 2.2 --- FACTORS AFFECTING VEGETATION REFLECTANCE --- p.11 / Chapter 2.2.1 --- Foliar structure and principal constituents --- p.12 / Chapter 2.2.2 --- Foliar optical properties --- p.14 / Chapter 2.2.2.1 --- The visible region (400-700nm) --- p.14 / Chapter 2.2.2.2 --- The red edge (690-740nm) --- p.15 / Chapter 2.2.2.3 --- The near-infrared region (700-1300nm) --- p.16 / Chapter 2.2.2.4 --- The short-wave infrared region (1300-2500nm) --- p.17 / Chapter 2.2.3 --- Canopy architecture --- p.18 / Chapter 2.2.4 --- Background reflectance --- p.19 / Chapter 2.2.5 --- Atmospheric perturbation --- p.20 / Chapter 2.2.6 --- Sun-sensor relationship --- p.22 / Chapter 2.3 --- HYPERSPECTRAL IMAGING AND VEGETATION CLASSIFICATION --- p.23 / Chapter 2.4 --- RADAR IMAGING AND VEGETATION CLASSIFICATION --- p.31 / Chapter 2.5 --- PATTERN RECOGNITION FOR VEGETATION CLASSIFICATION --- p.39 / Chapter 2.5.1 --- The Hughes Phenomenon and Dimensionality Reduction --- p.39 / Chapter 2.5.2 --- Statistical Pattern Recognition and Feature Selection --- p.44 / Chapter 2.5.2.1 --- Search Method --- p.47 / Chapter 2.5.2.1.1 --- Exhaustive search --- p.48 / Chapter 2.5.2.1.2 --- Branch and bound --- p.49 / Chapter 2.5.2.1.3 --- Sequential forward/ backward selection --- p.55 / Chapter 2.5.2.1.4 --- Sequential Floating search --- p.57 / Chapter 2.5.2.1.5 --- Oscillating Search --- p.61 / Chapter 2.5.2.1.6 --- Genetic algorithm --- p.64 / Chapter 2.5.2.2 --- Evaluation criteria --- p.66 / Chapter 2.5.2.2.1 --- Distance measure --- p.67 / Chapter 2.5.2.2.2 --- Information measure --- p.68 / Chapter 2.5.2.2.3 --- Classification error --- p.71 / Chapter 2.5.2.3 --- Feature Selection Stability --- p.72 / Chapter 2.5.3 --- Feature extraction --- p.75 / Chapter 2.6 --- BIOPHYSICAL PARAMETERS MEASUREMENT AND ESTIMATION --- p.77 / Chapter 2.6.1 --- Leaf Area Index (LAI) --- p.78 / Chapter 2.6.2 --- Fraction of Absorbed Photosynthetically Active Radiation (fAPAR) --- p.79 / Chapter 2.6.3 --- In-situ Leaf Area Index Measurement --- p.81 / Chapter 2.6.3.1 --- Direct and Indirect Methods --- p.81 / Chapter 2.6.3.2 --- LAI Estimation through Gap Fraction Inversion --- p.85 / Chapter 2.6.3.3 --- Gap Fraction Ground Measurement --- p.89 / Chapter 2.6.3.3.1 --- LAI-2000 Plant Canopy Analyzer --- p.89 / Chapter 2.6.3.3.2 --- Hemispherical Photography --- p.92 / Chapter 2.6.3.4 --- Correction of Indirect LAI Measurement --- p.99 / Chapter 2.6.3.4.1 --- Clumping --- p.100 / Chapter 2.6.3.4.2 --- Mixture of Green and Non-green Elements --- p.101 / Chapter 2.6.4 --- Empirical Relationship with Spectral Vegetation Indices --- p.102 / Chapter 2.6.4.1 --- Traditional Vegetation Indices --- p.103 / Chapter 2.6.4.2 --- Leaf Area Index Estimation from Hyperspectral and Radar Images --- p.106 / Chapter 2.6.5 --- Physically-based Canopy Reflectance Model Inversion --- p.111 / Chapter 2.6.5.1 --- Canopy Reflectance Model --- p.111 / Chapter 2.6.5.2 --- Model Inversion and Biophysical Parameters Extraction --- p.115 / Chapter 2.7 --- SUMMARY --- p.118 / Chapter CHAPTER 3 --- METHODOLOGY --- p.120 / Chapter 3.1 --- INTRODUCTION --- p.120 / Chapter 3.2 --- STUDY AREA DESCRIPTION --- p.120 / Chapter 3.3 --- METHODOLOGICAL FLOW --- p.124 / Chapter 3.4 --- REMOTE SENSING DATA ACQUISITION AND PROCESSING --- p.127 / Chapter 3.4.1 --- Hyperion - EO-1 --- p.127 / Chapter 3.4.1.1 --- Radiometric correction --- p.127 / Chapter 3.4.1.1.1 --- Vertical strips removal --- p.128 / Chapter 3.4.1.1.2 --- Atmospheric correction --- p.129 / Chapter 3.4.1.1.3 --- Wavelength recalibration --- p.135 / Chapter 3.4.1.1.4 --- SNR enhancement through MNF --- p.137 / Chapter 3.4.1.2 --- Geometric correction --- p.139 / Chapter 3.4.1.3 --- Atmospheric correction algorithms comparison --- p.140 / Chapter 3.4.2 --- ASAR - ENVISAT --- p.141 / Chapter 3.4.2.1 --- Data Acquisition --- p.141 / Chapter 3.4.2.2 --- Data Processing --- p.143 / Chapter 3.4.2.2.1 --- Radiometric and Geometric Correction --- p.145 / Chapter 3.4.2.2.2 --- Speckle Filtering --- p.146 / Chapter 3.5 --- FIELD MEASUREMENTS AND DATA PROCESSING --- p.149 / Chapter 3.5.1 --- Species Distribution --- p.149 / Chapter 3.5.2 --- Leaf Spectra Measurement --- p.151 / Chapter 3.5.2.1 --- Leaf Collection and Handling --- p.152 / Chapter 3.5.2.2 --- ASD FieldSpec 3 Setup --- p.154 / Chapter 3.5.2.3 --- Laboratory setup --- p.156 / Chapter 3.5.2.4 --- Spectra Measurement --- p.158 / Chapter 3.5.2.5 --- Spectral similarity and variability --- p.159 / Chapter 3.5.3 --- In situ Leaf Area Index Measurement --- p.161 / Chapter 3.5.3.1 --- The optical instrument --- p.161 / Chapter 3.5.3.2 --- The LAI survey campaign p163 / Chapter 3.5.3.3 --- Data processing and canopy analysis --- p.166 / Chapter 3.5.3.4 --- Canopy parameter computation gap fraction, LAI, clumping index, mean inclination angle --- p.170 / Chapter 3.5.3.5 --- Field LAI and Their Correlation with Reflectance and Backscattering Coefficient Data Exploration --- p.175 / Chapter 3.6 --- FEATURE SELECTION --- p.175 / Chapter 3.6.1 --- Data Preprocessing and Preparation --- p.178 / Chapter 3.6.2 --- Data Format and Split --- p.183 / Chapter 3.6.3 --- Wrapper-based Approach --- p.185 / Chapter 3.6.4 --- Search Algorithm --- p.187 / Chapter 3.6.5 --- Stability Evaluation --- p.187 / Chapter 3.6.6 --- Feature Frequency analysis --- p.188 / Chapter 3.7 --- MANGROVE SPECIES CLASSIFICATION --- p.189 / Chapter 3.7.1 --- Species Separability --- p.193 / Chapter 3.7.2 --- Gaussian Maximum Likelihood Classifier --- p.193 / Chapter 3.7.3 --- Decision Tree Classifier --- p.194 / Chapter 3.7.4 --- Artificial Neural Network Classifier --- p.197 / Chapter 3.7.5 --- Support Vector Machines Classifier --- p.199 / Chapter 3.7.6 --- Accuracy Assessment --- p.204 / Chapter 3.8 --- LEAF AREA INDEX MODELING --- p.206 / Chapter 3.8.1 --- Preliminary Exploration of Relationship between Hyperspectral bands and LAI --- p.206 / Chapter 3.8.2 --- Vegetation Index Derived from Hyperspectral Data. --- p.206 / Chapter 3.8.3 --- Radar Backscatter and Derived Textural Parameters --- p.208 / Chapter 3.8.4 --- Regression Analysis --- p.211 / Chapter 3.8.5 --- Error Estimation --- p.217 / Chapter 3.9 --- SUMMARY --- p.218 / Chapter CHAPTER 4 --- RESULTS AND DISCUSSION (I) FEATURE SELECTION AND MANGROVE SPECIES CLASSIFICATION --- p.221 / Chapter 4.1 --- INTRODUCTION --- p.221 / Chapter 4.2 --- DATA PROCESSING AND EXPLORATION --- p.221 / Chapter 4.2.1 --- Atmospheric correction algorithms comparison --- p.222 / Chapter 4.2.2 --- Radar Data Speckle Reduction --- p.227 / Chapter 4.2.3 --- Statistical Discrimination of Mangrove Spectral Class --- p.230 / Chapter 4.3 --- FEATURE SELECTION --- p.249 / Chapter 4.3.1 --- Sequential Forward Selection (SFS) --- p.250 / Chapter 4.3.2 --- Sequential Floating Forward Selection (SFFS). --- p.256 / Chapter 4.3.3 --- Oscillating Search (OS) --- p.262 / Chapter 4.3.4 --- Search Algorithms comparison --- p.268 / Chapter 4.3.5 --- Final Subset Selection --- p.270 / Chapter 4.3.6 --- Correlation Analysis --- p.280 / Chapter 4.4 --- IMAGE CLASSIFICATION --- p.283 / Chapter 4.4.1 --- Mangrove Spectral Class Separability --- p.284 / Chapter 4.4.2 --- Gaussian Maximum Likelihood (ML) --- p.288 / Chapter 4.4.3 --- Decision Tree (DT) --- p.297 / Chapter 4.4.4 --- Artificial Neural Network (ANN) --- p.304 / Chapter 4.4.5 --- Support Vector Machines (SVM) --- p.312 / Chapter 4.4.6 --- Algorithm Comparison --- p.321 / Chapter 4.5 --- DISCUSSION AND IMPLICATION --- p.325 / Chapter 4.5.1 --- Feature Selection --- p.325 / Chapter 4.5.2 --- Mangrove Classification --- p.342 / Chapter 4.6 --- SUMMARY --- p.351 / Chapter CHAPTER 5 --- RESULTS AND DISCUSSION (II) - LEAF AREA INDEX MODELING --- p.353 / Chapter 5.1 --- INTRODUCTION --- p.353 / Chapter 5.2 --- DATA EXPLORATION --- p.353 / Chapter 5.2.1 --- Dependent Variable: Field measured LAI --- p.353 / Chapter 5.2.2 --- Independent Variables: Vegetation Index and texture measure --- p.355 / Chapter 5.2.3 --- Hyperspectral bands and LAI --- p.356 / Chapter 5.2.4 --- Normality testing --- p.359 / Chapter 5.2.5 --- Linearity testing --- p.363 / Chapter 5.2.6 --- Outliner detection --- p.365 / Chapter 5.3 --- SIMPLE LINEAR REGRESSION ANALYSIS --- p.366 / Chapter 5.3.1 --- LAI2000 Generalized method --- p.369 / Chapter 5.4 --- STEPWISE MULTIPLE REGRESSION ANALYSIS --- p.381 / Chapter 5.4.1 --- LAI2000 Generalized method --- p.384 / Chapter 5.5 --- DISCUSSION AND IMPLICATION --- p.391 / Chapter 5.5.1 --- LAI model comparison --- p.391 / Chapter 5.5.2 --- Species composition and LAI --- p.393 / Chapter 5.5.3 --- Hyperspectral Bands, Vegetation Indices and LAI --- p.397 / Chapter 5.5.4 --- Backscatter, texture measures and LAI --- p.407 / Chapter 5.5.5 --- Complementarity of Vegetation Index and Radar Parameters --- p.414 / Chapter 5.6 --- SUMMARY --- p.421 / Chapter CHAPTER 6 --- CONCLUSION --- p.423 / Chapter 6.1 --- SUMMARY OF THE STUDY --- p.423 / Chapter 6.2 --- LIMITATION OF THE STUDY --- p.427 / Chapter 6.3 --- RECOMMENDATION --- p.431 / Chapter REFERENCE --- p.434 / Chapter APPENDIX A --- GEOMETRIC CORRECTION OF HYPERSPECTRAL DATA --- p.473 / Chapter APPENDIX B --- SCRIPTS DERIVED FROM FEATURE SELECTION TOOLBOX (FST) FOR FEATURE SELECTION --- p.475 / Chapter APPENDIX C --- PREDICTED LAI(BON) AND LAI(2000) FROM SIMPLE LINEAR REGRESSION MODELS --- p.513 / Chapter APPENDIX D --- PREDICTED LAI(BON) AND LAI(2000) FROM MULTIPLE STEPWISE REGRESSION MODELS --- p.524

Mangrove forests are an integral part of coastal wetlands in temperate and tropical regions of the world, including New Zealand. These coastal plants act as a shelter,feeding and breeding grounds for marine and terrestrial organisms. Many overseas studies have investigated the importance of mangrove and seagrass habitats in sustaining coastal food chains. In New Zealand, however, only a few studies have addressed the ecology and food web dynamics of these temperate ecosystems.As a first step to investigate the nutrient dynamics of estuarine food webs in temperate estuaries, this study aimed to quantify the nutrient concentrations in the catchment and the estuary of Matapouri, northern New Zealand. Field studies involved the collection of surface fresh and estuarine water (during low and high tides). Plant material (mangrove and seagrass), and sediment samples were collected at various sites within the estuary. Chemical analyses were carried out to determine the concentration of C, N, P and Si macronutrients and Fe and Zn micronutrients during different seasonal rainfall events.The results suggest that mangrove habitats may act as a source of POC, but not DOC for the adjacent aquatic habitats (i.e., seagrass, sand flats, channels), while seasgrass beds contribute more N to the estuarine system than the mangrove forests. The concentrations of N and P nutrients are strongly influenced by both the freshwater inputs and the bio-chemical processes within the estuary. The results obtained point to the freshwater streams as the main source of Si and Fe in the estuary. However, Zn was higher in the estuarine water compared to the catchment freshwater. NO3 -, NH4 +, Fe and Zn concentrations showed strong responses to the higher rainfall months reaching their highest level during the winter and early spring seasons. Conversely, P concentrations showed a negative seasonal pattern, which was linked to monthly rainfall events.Mangrove sediments may operate as a sink for the heavy metal Zn in Matapouri estuary. Iron concentration in seagrass leaves exceeded that in mangrove leaves by 65 orders of magnitude. The study suggests that seagrass plants could be used as a biological indicator of iron concentration in the estuary. The complex dynamics of bio-chemical cycles in Matapouri indicate that each habitat within the estuary has specific nutrient contributions to the estuarine food web system. However, the catchment and oceanic influences must also be considered in the nutrient balance of these coastal environments.

Three questions are considered in the context of the possible effects of seedassociated mycoflora, typified by Fusarium moniliforme, during hydrated storage of recalcitrant seeds of the tropical species, Avicennia marina. These are: 1) whether fungal infection reduces storage lifespan; 2) whether seeds become more susceptible to fungal attack during storage and whether they posses defence mechanisms that might suppress fungal proliferation in hydrated storage (production of antifungal compounds and 13-1,3-glucanase (EC 3.2.1.39) and chitinase (EC 3.2.1.14)] and 3) whether it is possible to discriminate ultrastructurally between inherent deteriorative changes and those that are fungally-induced. 1) The data indicate unequivocally that if fungal activity is curtailed, then the hydrated storage lifespan of A. marina seeds can be considerably extended. 2) When inoculated immediately with F. moniliforme, newly harvested seeds were extremely susceptible to the adverse effects of the fungus, while seeds that had been wet-stored for 4 days showed a considerably heightened resilience to the effects of the fungus prior to inoculation. The enhanced resilience, although declining, persisted in seeds stored hydrated for up to 10 days prior to inoculation, being lost after 12 days. This finding was supported by significant increase in 13-1,3-glucanase and chitinase and in antifungal compound production during 10 days of wet storage. After 14 days of wetstorage, seeds become more susceptible to the effects of fungusthanthose in the newly harvested condition. 3) The resilience of seeds that had been stored in the short-term was associated with ultrastructural changes indicative of enhanced metabolic activity associated with the onset of germination (e.g. increase in vacuolation, well-developed mitochondria and endomembrane system [ER and Golgi bodies]). However, with sustained stress associated with wet-storage IV conditions, the seeds became increasingly badly affected by the fungus, showing some ultrastructural fungally-induced abnormalities (e.g. nuclear lobing, presence of lipid bodies and prevalence of Golgi bodies that had many associated vesicles) and a decrease in 13-1,3-glucanase and chitinase activity. It is suggested that the decreased susceptibility of A. marina seeds during short-term storage relies on the ability to create an antifungal environment prior to infection (through synthesis and accumulation of pre-formed and induced antifungal compounds and antifungal enzymes), which would also be an effective strategy during germination in the natural environment. / Thesis (Ph.D.)-University of KwaZulu-Natal, Durban, 2004

In Richards Bay Harbour, the mangrove Avicennia marina exhibits a distinct natural productivity gradient. The fringe site, which is regularly inundated twice daily by tides, supports luxuriant adult A. marina trees that are 6-10 m tall and which form a dense, well-developed canopy. The landward site which is only inundated during high spring tides, supports diminutive or dwarf A. marina that are less than 1.5 m in height. In this study we compared leaves from fringe and dwarf sites with respect to morphology, ultrastructure and ecophysiology. Alterations in leaf morphology, ultrastructure and physiology of A. marina were compared at the fringe site (35 ‰) and dwarf site (60 ‰) using morphometric measurements, light (LM), transmission (TEM) and scanning microscopy (SEM). SEM and light microscopy revealed that multicellular salt glands were located on the thick, cutinised adaxial surface from leaves of both sites. The glands appeared to be scattered and protruding from individual crypts in fringe mangrove leaves whilst they appeared sunken and occluded by cuticular material in dwarf mangrove leaves. The salt glands on the abaxial surface were not sunken but obscured by the indumentum of peltate trichomes. Ultrastructural changes observed in dwarf mangrove leaves were associated with cuticle, cell walls, chloroplasts, mitochondria of mesophyll tissue and salt glands. Fringe mangrove leaves had chloroplasts with typical well-developed grana and stroma. Ultrastructural changes of chloroplasts were evident in dwarf mangrove leaves and included swelling and separation of thylakoids, disintegration of granal stacking and integranal lamellae, as well as loss of the integrity of the chloroplast envelope. Multivesicular structures were commonly found in vacuoles and associated with chloroplasts and mitochondria in both leaf types. In fringe mangrove leaves, mitochondria appeared spherical to tubular with a relatively smooth outer membrane and a highly convoluted inner membrane. Swelling and vacuolation of mitochondrial membranes, cristae and mitochondrial clustering in the cytoplasm around the chloroplasts were evident in dwarf mangrove leaves. Extensive lipid accumulation in the form of large, dense plastoglobuli occurred in the chloroplasts of dwarf mangrove leaves. There were characteristic differences in salt gland morphology of fringe and dwarf mangrove leaves, namely in the cell walls, vacuoles, and vesicle formation. In salt glands of dwarf mangrove leaves, a distinct withdrawal of the cytoplasm from the cell wall was observed. This feature was not observed in salt glands of fringe mangrove leaves. Numerous large vacuoles were observed in the secretory cells of glands of dwarf mangrove leaves compared to those of fringe plants. Multivesicular structures, vesicles and mitochondria were common features in both leaf types. Physiological studies involved a comparison of osmotic and ionic relations as well as whole plant responses in fringe and dwarf mangrove leaves. Relative leaf water content decreased by 7.8 % and specific leaf area by 17 % in dwarf compared to those of fringe mangroves. Dwarf mangrove leaves were 27.6 % thicker and leaf cuticle thickness 37.4 % higher than those from fringe mangroves. Fringe mangrove leaves displayed higher total chlorophyll contents by 27 %, with chlorophylls a and b being 22 % and 39.6 % higher, respectively than those of dwarf mangroves. Salt gland frequencies were higher in the apex, mid-lamina and base of fringe than dwarf mangrove leaves by 36 %, 45 % and 51 %, respectively. The concentration of glycinebetaine, a compatible, N-containing osmolyte was significantly higher by 40 % in dwarf than in fringe mangrove leaves. Concentrations of proline were 27 % lower in dwarf than in fringe mangrove leaves. The predominant inorganic ion detected in mature leaves was Na+, which was 19 % higher in dwarf than fringe mangrove leaves. Phosphorus was an element that appeared deficient in dwarf mangrove leaves, being 50 % lower compared to fringe mangrove leaves. The results of this investigation indicated that there were cytomorphological alterations as well as differences in physiological responses in leaves of A. marina at fringe and dwarf sites. / Thesis (M.Sc.)-University of KwaZulu-Natal, Westville, 2007.

Seeds remain the most convenient and successful way for storing the genetic diversity of plant species and for producing new plants routinely for agriculture and horticulture. The importance of seed storage and the ability to predict seed longevity must therefore not be underestimated. To be successful, storage conditions must maintain seed vigour and viability and ensure that normal seedlings are subsequently established under field conditions. Seed quality is best retained when deteriorative events are minimised, which is achieved by storage of low moisture-content seeds under cool to cold, or even sub-zero, temperatures. Such conditions are employed for 'orthodox' seeds, which are desiccation tolerant and able to survive at sub-zero temperatures in the dehydrated state for extended periods. It is seeds referred to as 'recalcitrant' that cannot be dehydrated and often not stored at low temperatures because they are desiccation sensitive and may not tolerate chilling. According to almost anecdotal records chilling temperatures for such seeds are those below 15°C down to 0°C, depending on the species. The limited storage lifespan of recalcitrant seeds presents a problem even for short-term storage, and as most research on chilling sensitivity has been conducted on vegetative tissue, relatively little data exist for seeds, especially recalcitrant types. The purpose of this study was to gain an understanding of the chilling response of recalcitrant seeds, as reduced temperature could have the potential to extend, rather than curtail, storage lifespan, depending on the species. Selected physiological, biochemical and ultrastructural responses of recalcitrant seeds of Avicennia marina and Ekebergia capensis were characterised. Seeds of the two species were stored at 25, 16 and 6°C. Germination, water content (determined gravimetrically), respiration (measured as CO2 production) and leachate conductivity (tissue electrolyte leakage over time) were assessed at regular intervals. Chilling response at the subcellular level was examined using transmission electron microscopy (TEM). Changes in sugar metabolism and activities of the antioxidant enzymes superoxide dismutase (SOD), catalase (CAT) and glutathione reductase (GR) were assessed for A. marina seeds, which were severely affected by the chilling temperature of 6°C, losing viability after 1 week. In contrast, the seeds of E. capensis retained viability after 12 weeks of storage at 6°C, indicating the marked difference in chilling response between seeds of the two recalcitrant species, despite their common tropical provenance. However, when E. capensis seeds were stored at 3°C viability decreased significantly after 8 weeks, thus indicating how critically temperature must be controlled if such conditions are to be profitably employed. Ultrastructural studies revealed that in both E. capensis and A. marina seeds vacuole formation was initiated more rapidly at lower temperatures than at higher temperatures, indicating that this was a response specific to the chilling stress imposed. Once again, 'lower temperatures' differed relative to the species concerned. In the E. capensis seeds, nucleolar morphology was affected and the extent of chromatin patches in the nuclei increased as the storage temperature was reduced. Other ultrastructural findings could not be linked specifically to the chilling stress imposed on the E. capensis and A. marina seeds. Activity of the antioxidant enzymes SOD and GR was detected in the A. marina seeds. No measurable CAT activity was detected. Glutathione reductase activity increased in response to chilling stress, the rate of the increase depending upon the severity of the chilling stress imposed. Other than when the A. marina seeds were placed directly at 6°C, there were no notable increases in SOD activity. Interestingly, SOD and GR activity was not the same in the axes as in the cotyledons. Superoxide dismutase activity was found to be higher in the axes and GR activity higher in the cotyledons. It would have been beneficial to determine the extent of antioxidant enzyme activity in the E. capensis seeds as well if this had been possible. Generally, chilling of recalcitrant seeds seems to evoke a response similar to that of dehydration below a critical water content. This could lead to the conclusion that recalcitrant seeds do not possess the genetic ability to cope with dehydration or chilling stress, if it were not for the existence of recalcitrant seed species that are more chilling tolerant. / Thesis (M.Sc.)-University of Natal, Durban, 2002.

Heavy metal contamination of mangroves is of critical concern due to its accumulative and adverse effects in aquatic ecosystems. This study was undertaken to investigate the effects of mercury (Hg ), lead (Pb ), copper (Cu ) and zinc (Zn ) on plant responses, specifically growth and productivity, in Avicennia marina (Forssk.) Vierh. A. marina plants were grown for twelve months in pots contaminated with Hg +, Pb +, Cu2+ and Zn2+ at concentrations of 0, 40, 80, 120 and 160 ppm (1 ppm = 1 (agmf1). Accumulation and distribution of the heavy metals in shoot and root tissues were determined using atomic absorption spectroscopy (Perkin-Elmer Model 303) while secretion of the heavy metals from leaves was studied using scanning electron microscopy and energy dispersive X-ray microanalysis. I hypothesized that heavy metals have deleterious effects on plant growth and that they are absorbed by roots and secreted from salt glands present on the leaves. SEM X-ray microanalyses confirmed secretion of Cu + and Zn + ions as well as salt (NaCl) from glandular structures on both the adaxial and abaxial surfaces of leaves; however Hg2+ and Pb2+ were not detected in the secretion. Ion concentrations were significantly higher in plant roots than in shoots, particularly at 160 ligml"1 for all heavy metals. In addition, toxic levels of Hg and Pb were detected in the shoot tissue; however, Cu2+ and Zn2+ were within the normal ion concentration in the shoots. Plant height, number of leaves, biomass accumulation and chlorophyll content were significantly lower at 160 ugml" than the control values for all heavy metals. Carbon dioxide exchange, transpiration and leaf conductance generally decreased with increasing metal concentration. CO2 exchange at a concentration of 160 (J-gmf1 was significantly lower than the control for all metals. CO2 exchange at 160 ugml"1 for Hg2+, Pb2+, Cu2+ and Zn were 49.6 %, 55 %, 47.6 % and 63.6 % respectively lower than the control values. Photosystem II (PS II) quantum yield, photochemical efficiency of PSII (Fv/Fm) and electron transport rate (ETR) through PS II generally decreased with increasing concentration for all heavy metals. XV This study has shown that A. marina experiences dose-dependent stress responses to Cu2+, Zn2+, Hg2+ and Pb2+ in shoot and root tissue at a concentration of 160 lagmi"1, evidenced by decreases in growth and photosynthetic performance. The results also ~)A- "7-1- 9-1- "J-\-indicate that CuZT, Znz\ HgZT and PbZT are taken up by roots and transported to shoots. In addition, only Cu and Zn are secreted via the glands while Hg and Pb accumulate within the shoots. / Thesis (M.Sc.)-University of KwaZulu-Natal, 2008.

Thesis (MAppSc) -- Auckland University of Technology, 2004. / Also held in print (214 leaves, 30 cm.) in Wellesley Theses Collection (T 577.698 SOL)

Thesis (Ph.D.) -- James Cook University, 2003. / Typescript (photocopy) Bibliography: leaves 214-238.