Mangrove forests are woody vegetation located amongst the coastal wetlands of the tropics that hold importance for local populations, carbon sequestration and biodiversity. Despite this, mangrove forests are in decline as a consequence of both direct and indirect anthropogenic activities, such as aquaculture practices, coastal development and climate change. Through the interpretation of time-series colour composite radar imagery, mangrove forests were observed to have been anthropogenically disturbed and to have experienced changes in extent caused by natural processes. This revealed that as much as 40% of the world's mangrove forests occur within a region at risk of further loss and degradation. In light of this observation, this study has developed a method for automatically mapping and monitoring mangrove extent using time-series Japanese (JAXA) ALOS PALSAR (2007-2010) and JERS-1 (1996) radar imagery at a variety of locations across the tropics. Random Forests was used to classify mangrove forest extent for the year 2010 at 16 study sites across the tropics, representing a broad range of mangrove habitats and forest types. Existing mangrove extent maps were re ned using a Bayesian Maximum Likelihood approach and were used to generate training data for the Random Forests algorithm. Changes in mangrove forest extent were detected using a novel map-to-image approach developed in this study. The technique detected change in mangrove extent in an automated fashion for the period 2007-2010 using ALOS PALSAR imagery and 1996-2010 using JERS-1 imagery. An area in excess of 2.5 million ha of mangrove forest extent was classi ed with the baseline and changes in extent mapped with an accuracy >90%. Limitations pertaining to image registration, classi cation error and the size of the minimum mapping unit were identi ed as sources of error in the baseline and change detection mapping. The results of this work are applicable at the global level and can be scaled to update the existing map of global mangrove forest extent and implement a mangrove monitoring system.

Mangroves are widely recognised as being one of the most valuable of coastal ecosystems. On a local scale they provide coastal protection, habitat for fish and shellfish, and control water quality. Globally, they are key to mitigate climate change given their considerable capacity to sequester carbon. In Latin America, especially in the Tropical Eastern Pacific region, mangrove forests are abundant, and linked to millions of coastal livelihoods. This thesis investigates the conservation status of mangroves on the Tropical Eastern Pacific coasts of Costa Rica, Colombia, Ecuador, and Panamá, by examining historical trends of mangrove loss, modern rates of deforestation (2000-2012), and the effectiveness of the protected area management in place. The contribution of mangrove-associated species to fisheries is evaluated using two artisanal fishing communities as case studies: Northern Chocó, Colombia and the Gulf of Montijo, Panamá. To do this, I investigate historical ecology, perform spatial analysis of proximate drivers of land use and land cover change adjacent to mangroves, and analyse small-scale fisheries landings. Results show that mangrove dependent species are important for small-scale fisheries in the Gulf of Montijo, Panamá. In Northern Chocó, territorial use rights in fisheries promote offshore fishing, thereby reducing fishing pressure on mangrove-associated species, but simultaneously may have displaced fishing effort from industrial trawlers into neighbouring areas. Regionally, mangrove area declined by almost 50% in the 20th century, but deforestation was virtually zero between 2000 and 2012, showing that protected areas are highly effective at conserving mangroves. Given that the success of mangrove conservation depends on government capacity to integrate multi-sectorial interests over mangroves, this thesis represents an important step to inform management strategies that involve a better understanding of human-mangrove interactions in Latin America.

No description available.

No description available.

Mangroves are a group of woody plants that occur in the dynamic tropical and subtropical intertidal zones. Mangrove forests offer numerous ecosystem services (e.g. nutrient cycling, coastal protection and fisheries production) and support costal livelihoods worldwide. Rapid environmental changes and historical anthropogenic pressures have turned mangrove forests into one of the most threatened and rapidly vanishing habitats on Earth. Yet, we have a restricted understanding of how these pressures have influenced mangrove abundance, composition and functions, mostly due to limited availability of mangrove field data. Such knowledge gaps have obstructed mangrove conservation programs across the tropics. This thesis focuses on the plants of Earth’s largest continuous mangrove forest — the Sundarbans — which is under serious threat from historical and future habitat degradation, human exploitation and sea level rise. Using species, environmental, and functional trait data that I collected from a network of 110 permanent sample plots (PSPs), this thesis aims to understand habitat preferences of threatened mangroves, to explore spatial and temporal dynamics and the key drivers of mangrove diversity and composition, and to develop an integrated approach for predicting functional trait responses of plants under current and potential future environmental scenarios. I found serious detrimental effects of increasing soil salinity and historical tree harvesting on the abundance of the climax species Heritiera fomes. All species showed clear habitat preferences along the downstream-upstream gradient. The magnitude of species abundance responses to nutrients, elevation, and stem density varied between species. Species-specific density maps suggest that the existing protected area network (PAN) does not cover the density hotspots of any of the threatened mangrove species. Using tree data collected from different salinity zones in the Sundarbans (hypo-, meso-, and hypersaline) at four historical time points: 1986, 1994, 1999 and 2014, I found that the hyposaline mangrove communities were the most diverse and heterogeneous in species composition in all historical time points while the hypersaline communities were the least diverse and most homogeneous. I detected a clear trend of declining compositional heterogeneity in all ecological zones since 1986, suggesting ecosystem-wide biotic homogenization. Over the 28 years, the hypersaline communities have experienced radical shifts in species composition due to population increase and range expansion of the disturbance specialist Ceriops decandra and local extinction or range contraction of many endemics including the globally endangered H. fomes. Applying habitat-based biodiversity modelling approach, I found historical tree harvesting, siltation, disease and soil alkalinity as the key stressors that negatively influenced the diversity and distinctness of the mangrove communities. In contrast, species diversity increased along the downstream – upstream, and riverbank — forest interior gradients, suggesting late successional upstream and forest interior communities were more diverse than the early successional downstream and riverbank communities. Like the species density hotspots, the existing PAN does not cover the remaining biodiversity hotspots. Using a novel integrated Bayesian modelling approach, I was able to generate trait-based predictions through simultaneously modelling trait-environment correlations (for multiple traits such as tree canopy height, specific leaf area, wood density and leaf succulence for multiple species, and multiple environmental drivers) and trait-trait trade-offs at organismal, community and ecosystem levels, thus proposing a resolution to the ‘fourth-corner problem’ in community ecology. Applying this approach to the Sundarbans, I found substantial intraspecific trade-offs among the functional traits in many tree species, detrimental effects of increasing salinity, siltation and soil alkalinity on growth related traits and parallel plastic enhancement of traits related to stress tolerance. My model predicts an ecosystem-wide drop in total biomass productivity under all anticipated stress scenarios while the worst stress scenario (a 50% rise in salinity and siltation) is predicted to push the ecosystem to lose 30% of its current total productivity by 2050. Finally, I present an overview of the key results across the work, the study’s limitations and proposals for future work.