The giant gourami is an air-breathing fish, native to this region, and grown commercially as a food fish as well as for the aquarium market throughout SE Asia (Lefevre et al., 2014). \u00a0The fish inhabits freshwater, brackish, benthopelagic environments in swamps, lakes, and rivers among vegetation, found in medium to large rivers and stagnant water bodies. \u00a0People around the world rely on fish as a primary source of protein and income, and the growing aquaculture industry provides roughly half of the global fish supply (FAO, 2014). \u00a0However, to meet the demands of a rapidly growing population (exceeding 7 billion by 2020), a rising middle class, and an increasingly urban population (65% by 2020), protein consumption is expected to increase to 45kg per capita by 2020, a 25% increase from 1997\u2014the fish consumption rate is no outlier. <\/span><\/p>\n <\/p>\n A description of the dataset.<\/b><\/p>\n Hypotheses: I expect that the potential habitat for the giant gourami has increased over time and with increased human impacts do to the physiological resilience of the species. \u00a0This fish inhabits regions characterized by fresh to brackish water and in slow-moving areas like swamps, lakes, and large rivers. \u00a0Given its unique ability to breather air, this fish can survive in poorly oxygenated water to anoxic areas. \u00a0I expect that with climate change, increased urbanization, and the changing hydrologic profile of the system due to potential dams that this fish may become more suitable than others for its ability to live in \u2018poorer\u2019 environmental conditions. <\/span><\/b><\/p>\n Approaches:\u00a0<\/b>I hope to use python or modelbuilder to iterate through the available datasets to assess the changing habitat based on a habitat suitability index for the giant gourami. \u00a0There is also a time-series tool in Arc that I would like to explore. <\/span><\/p>\n Expected outcome: I hope to develop a habitat suitability index for the giant gourami and compare habitat suitability models for the potential habitat based on the changing physical landscape and increasing human impacts. \u00a0If the data are available, I hope to create a simple time-series animation for each model.<\/span><\/b><\/p>\n Significance:<\/b>\u00a0Fish production from aquaculture is poised to absorb an increasing amount of this demand for meat, offering techniques that offset some of the environmental costs of production. \u00a0Depending on the species and farming conditions, fish production can achieve some of the lowest feed-conversion ratios of any type of terrestrial animal meat production. \u00a0If farmed responsibly, some species of the diverse group of air-breathing fish such as the giant gourami present an advantage in aquaculture for their unique ability to breathe air. \u00a0However, it is critical to understand the impact of increased production levels on the natural range of the species order to mitigate the unwanted invasions or overloading of the natural environment. \u00a0A study to assess the spatio-temporal patterns of the habitat suitability of potential aquaculture species will allow for managers to make informed decisions about aquaculture siting and resource allocation.<\/span><\/p>\n Your level of preparation:\u00a0<\/b>In terms of my experience with the tools available for this type of analysis, I am starting to develop my comfort with ArcInfo, ModelBuilder, and Python for GIS. \u00a0However, am no expert. \u00a0I have also been exposed to some statistical applications of R, but am again not an expert.<\/span><\/p>\n <\/p>\n FAO. (2014). The State of World Fisheries and Aquaculture 2014<\/em>. Rome, Italy.<\/p>\n Lefevre, S., Wang, T., Jensen, a., Cong, N. V., Huong, D. T. T., Phuong, N. T., & Bayley, M. (2014). Air-breathing fishes in aquaculture. What can we learn from physiology? Journal of Fish Biology<\/em>, 84<\/em>, 705\u2013731. doi:10.1111\/jfb.12302<\/p>\n","protected":false},"excerpt":{"rendered":" My spatial problem A description of the research question that you are exploring. \u00a0Global change is occurring from the continuing variability in the climate, the ecological responses to the climate drivers, and the socioeconomic and political response. \u00a0These changes alter the landscape in predictable and unforeseen ways, simultaneously causing modifications in interactions between the landscape… Continue reading ![\"gourami\"](\"http:\/\/blogs.oregonstate.edu\/geo599spatialstatistics\/files\/2016\/04\/gourami.png\")
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