2011
Predictive habitat models are increasingly being used by conservationists, researchers and governmental bodies to identify vulnerable ecosystems and species’ distributions in areas that have not been sampled. However, in the deep sea, several limitations have restricted the widespread utilisation of this approach. These range from issues with the accuracy of species presences, the lack of reliable absence data and the limited spatial resolution of environmental factors known or thought to control deep-sea species’ distributions.
The Seychelles is an archipelago consisting of 115 granite and coral islands that occupy a land area of 445 sq. km within an Exclusive Economic Zone (EEZ) of 1.3 million sq. km in the South Western Indian Ocean between 4 and 9 degrees south of the equator.
The country’s population is currently estimated at around 87,300 (2010).1 Approximately 90% of the population and infrastructure is located on the main island of Mahe. The country has a per capita income of around US$ 7,000. Tourism, fisheries and a growing industrial sector dominate the economy of the country.
This dataset shows the global distribution of mangrove forests, derived from earth observation satellite imagery. The dataset was created using Global Land Survey (GLS) data and the Landsat archive. Approximately 1,000 Landsat scenes were interpreted using hybrid supervised and unsupervised digital image classification techniques. See Giri et al.
The Republic of Mauritius consists of a group of islands situated in the South West Indian Ocean at latitude of 20.17°S and a longitude of 57.33°E. It comprises mainland Mauritius, Rodrigues and Saint Brandon, Agalega and several outer islands. It enjoys a subtropical climate.
Part A of this report examines the full range of regional environmental and resource management organizations in the Western Indian Ocean, outlining their competences and main areas of operation. It then looks at the range of environmental and resource management projects which are being, or have recently been, funded in the region, assessing their key objectives and outputs.
Reefs at Risk Revisited brings together data on the world’s coral reefs in a global analysis designed to quantify threats and to map where reefs are at greatest risk of degradation or loss. We incorporated more than 50 data sources into the analysis—including data on bathymetry (ocean depth), land cover, population distribution and growth rate, observations of coral bleaching, and location of human infrastructure.
The overall objective of this project is to develop specific spatial data products at regional scale, for the coastal and/or marine areas of all the western Indian Ocean countries, including South Africa, Mozambique, Tanzania, Kenya, Somalia, Comoros, Seychelles, Madagascar, Mauritius and France. This report summarizes the data products, which have been prepared, on the basis of their relevance to the Large Marine Ecosystems (LME’s). The preparation of these data products involved retrieval from various sources, spatial analysis and modelling, and scaling.
Recent revisions to the satellite-derived vertical gravity gradient (VGG) data reveal more detail of the ocean bottom and have allowed us to develop a non-linear inversion method to detect seamounts in VGG data. We approximate VGG anomalies over seamounts as sums of individual, partially overlapping, elliptical polynomial functions, which allows us to form a non-linear inverse problem by fitting the polynomial model to the observations.
Restoring, maintaining and conserving the ecological integrity of the Agulhas Somali Current Large Marine Ecosystem (Figure 1) while ensuring optimal and sustainable utilization of the resources has been identified as a priority (Obura et al., 2012), especially with regard to the development of policy for the establishment of transboundary Marine Protected Areas (MPAs). This task requires knowledge of the spatial distribution of the physical and biological patterns and processes than sustain marine biodiversity in the region (Lombard et al. 2007; Sink and Attwood 2008).