An exploratory ecosystem model of the Bay of Bengal Large Marine Ecosystem

The study's aim was to develop and ecosystem model of the Bay of Bengal built with Ecopath and Ecoism software.The Ecopath model was built to represent 1978 and synthesise available population dynamics and fisheries data. A preliminary Ecoism was set up to explore interactions between functional groups and the impact of fishing.

Report of the MPA Atlas and the interactive online database portal of the Bay of Bengal Large Marine Ecosystem

This report describes the process and details of developing an interactive online database portal for the BOBLME region.The MPA (Marine Protected Area) Atlas website, created by WorldFish was designed to provide public access to the latest information relevant to marine scientists, managers and conservationists. The main features include; BOBLME MPA database;interactive geospatial maps;and information about important habitats such as coral reefs,BOBLME boundaries and bathymetry.

Understanding nutrient loading and sources in the Bay of Bengal Large Marine Ecosystem

Inputs of nitrogen, phosphorous and dissolved silica from watersheds draining into the Bay of Bengal Large Marine Ecosystem are calculated for the present day and predictions made for 2030 and 2050 are presented. The major sources are identified and the Indicator of Coastal Eutrophication (ICEP) is calculated.

Report of the workshop on assessing governance in the Bay of Bengal Large Marine Ecosystem, Bangkok, Thailand, 28-30 October, 2014

The objective of the workshop was to begin a structured discussion on regional governance in the BOBLME, drawing on lessons from a region with similar issues, the Carribean. Conclusions were made about principles, regional governance arrangements, national-regional interface and national science-policy interfaces. Future work was also planned.

Assessing, demonstrating and capturing the economic value of marine & coastal ecosystem services in the Bay of Bengal Large Marine Ecosystem

The objective of the study was to assess the economic value of ecosystem services in the Bay of Bengal.The manin aim was to support the development of a Strategic Action Plan (SAP). Findings included: economic consequences of ecosystem change; potential economic instruments to strengthen sustainable management; and recommendations on next steps in using economic valuation.

ICM for the Bay of Bengal: a review on best practices and lessons learned from Indonesia, Malaysia, Myanmar and Thailand

Country overviews of Integrated Coastal Management (ICM) for Indonesia, Malaysia, Myanmar and Thailand. Policies, scale and practices - what works and what does not work. Conclusions, limitations and suggestions

An ecosystem characterisation of the Bay of Bengal

This study summarises the high level drivers on ecological systems of the BOBLME. The ecological characterisation resulted in the identification of 29 subsystems. The report recommends the development of fully integrated approaches that considers human needs and the ecological system, involving stakeholders in a transparent way.

Report of the training course on integrated coastal management in the Bay of Bengal, Chonburi, Thailand, 20-24 October, 2014

Participants consisted of 25 middle and junior level personnel from BOBLME countries. Modules included: Integrated Coastal Management (ICM) concept and principles; ICM development and implementation; indicators of good practice; and action planning.

Report of the SocMon capacity development workshop for the Bay of Bengal Large Marine Ecosystem countries in South Asia, St Martin's Island, Bangladesh, 2-11 January, 2015

Socio-economic Monitoring (SocMon) is an approach and set of tools for conducting socio-economic monitoring of changes in coastal communities. Planned outputs of the workshop included: training of local staff i SocMon methodologies; draft a SocMon report for St. Martin's Island; a workplan for implementing the SocMon; a communication strategy; and key inputs to a regional SocMon strategy

Report of the SocMon capacity development workshop for the Bay of Bengal Large Marine Ecosystem countries in South Asia, Vidathaltivu, Northern Province, Sri Lanka, 3-11 March, 2015

This Socioeconomic Monitoring (SocMon) training workshop was coordinated by the Small Fisher Federation of Lanka (SFFL). Planned outputs included: participants from Mannar trained in SocMon methodologies; draft SocMon reports fro Vidathaltivu; a workplan for Mannar; a communication strategy for Vidathaltivu/ Mannar; and key inputs to a regional SocMon strategy

Key considerations for a regional SocMon strategy for the Bay of Bengal Large Marine Ecosystem countries in South Asia

Socio-economic Monitoring (SocMon) is an approach and set of tools for conducting socio-economic monitoring of changes in coastal communities. Key considerations included: importance of local partnerships; government and civil society partnerships; emphasis of adapting SocMon to local needs and priorities; capacity building; engaging with local stakeholders; inter and intra-regional collaboration; importance of language; and importance of language.

Workshop on assessing governance in the Bay of Bengal Large Marine Ecosystem, Bangkok, Thailand, October 28-30, 2014: workshop summary and messages for Bay of Bengal marine resource policy-makers and other stakeholders

This brochure suggests casting a wider net when dealing with governance assessment to include other players.

Temporal Coherence of Chlorophyll a during a Spring Phytoplankton Bloom in Xiangxi Bay of Three-Gorges Reservoir, China

Algal bloom phenomenon was defined as "the rapid growth of one or more phytoplankton species which leads to a rapid increase in the biomass of phytoplankton", yet most estimates of temporal coherence are based on yearly or monthly sampling frequencies and little is known of how synchrony varies among phytoplankton or of the causes of temporal coherence during spring algal bloom. In this study, data of chlorophyll a and related environmental parameters were weekly gathered at 15 sampling sites in Xiangxi Bay of Three-Gorges Reservoir (TGR, China) to evaluate patterns of temporal coherence for phytoplankton during spring bloom and test if spatial heterogeneity of nutrient and inorganic suspended particles within a single ecosystem influences synchrony of spring phytoplankton dynamics. There is a clear spatial and temporal variation in chlorophyll a across Xiangxi Bay. The degree of temporal coherence for chlorophyll a between pairs of sites located in Xiangxi Bay ranged from -0.367 to 0.952 with mean and median values of 0.349 and 0.321, respectively. Low levels of temporal coherence were often detected among the three stretches of the bay (Down reach, middle reach and upper reach), while high levels of temporal coherence were often found within the same reach of the bay. The relative difference of DIN between pair sites was the strong predictor of temporal coherence for chlorophyll a in down and middle reach of the bay, while the relative difference in Anorganic Suspended Solids was the important factor regulating temporal coherence in middle and upper reach. Contrary to many studies, these results illustrate that, in a small geographic area (a single reservoir bay of approximately 25 km), spatial heterogeneity influence synchrony of phytoplankton dynamics during spring bloom and local processes may override the effects of regional processes or dispersal.

Forecasting Daily Chlorophyll a Concentration during the Spring Phytoplankton Bloom Period in Xiangxi Bay of the Three-Gorges Reservoir by Means of a Recurrent Artificial Neural Network

A recurrent artificial neural network was used for 0-and 7-days-ahead forecasting of daily spring phytoplankton bloom dynamics in Xiangxi Bay of Three-Gorges Reservoir with meteorological, hydrological, and limnological parameters as input variables. Daily data from the depth of 0.5 m was used to train the model, and data from the depth of 2.0 m was used to validate the calibrated model. The trained model achieved reasonable accuracy in predicting the daily dynamics of chlorophyll a both in 0-and 7-days-ahead forecasting. In 0-day-ahead forecasting, the R-2 values of observed and predicted data were 0.85 for training and 0.89 for validating. In 7-days-ahead forecasting, the R-2 values of training and validating were 0.68 and 0.66, respectively. Sensitivity analysis indicated that most ecological relationships between chlorophyll a and input environmental variables in 0-and 7-days-ahead models were reasonable. In the 0-day model, Secchi depth, water temperature, and dissolved silicate were the most important factors influencing the daily dynamics of chlorophyll a. And in 7-days-ahead predicting model, chlorophyll a was sensitive to most environmental variables except water level, DO, and NH3N.

Temporal and Spatial Distributions of Rotifers in Xiangxi Bay of the Three Gorges Reservoir, China

From July 2003 to June 2005, investigations of rotifer temporal and spatial distributions were carried out in a bay of the Three Gorges Reservoir, Xiangxi Bay, which is the downstream segment of the Xiangxi River and the nearest bay to the Three Gorges Reservoir dam in Hubei Province, China. Thirteen sampling sites were selected. The results revealed a high species diversity, with 76 species, and 14 dominant species; i.e., Polyarthra vulgaris, Keratella cochlearis, Keratella valga, Synchaeta tremula, Synchaeta stylata, Trichocerca lophoessa, Trichocerca pusilla, Brachionus angularis, Brachionus calyciflorus, Brachionus forficula forficula, Ascomorpha ovalis, Conochilus unicornis, Ploesoma truncatum and Anuraeopsis fissa. After the first year of the reservoir impoundment, the rotifer community was dominated by ten species; one year later it was dominated by eight species. The community in 2003/2004 was dissimilar to that in 2004/2005, which resulted from the succession of the dominant species. The rotifer community exhibited a patchy distribution, with significant heterogeneity observed along the longitudinal axis. All rotifer communities could be divided into three groups, corresponding to the riverine, the transition and the lacustrine zone, respectively.