Investigation of a novel approach for aquaculture site selection.

This study investigated the potential use of two "species distribution models" (SDMs), Mahalanobis Typicality and Maxent, for aquaculture site selection. SDMs are used in ecological studies to predict the spatial distribution of species based on analysis of conditions at locations of known presence or absence. Here the input points are aquaculture sites, rather than species occurrence, thus the models evaluate the parameters at the sites and identify similar areas across the rest of the study area. This is a novel approach that avoids the need for data reclassification and weighting which can be a source of conflict and uncertainty within the commonly used multi-criteria evaluation (MCE) technique. Using pangasius culture in the Mekong Delta, Vietnam, as a case study, Mahalanobis Typicality and Maxent SDMs were evaluated against two models developed using the MCE approach. Mahalanobis Typicality and Maxent assess suitability based on similarity to existing farms, while the MCE approach assesses suitability using optimal values for culture. Mahalanobis Typicality considers the variables to have equal importance whereas Maxent analyses the variables to determine those which influence the distribution of the input data. All of the models indicate there are suitable areas for culture along the two main channels of the Mekong River which are currently used to farm pangasius and also inland in the north and east of the study area. The results show the Mahalanobis Typicality model had more high scoring areas and greater overall similarity than Maxent to the MCE outputs, suggesting, for this case study, it was the most appropriate SDM for aquaculture site selection. With suitable input data, a combined SDM and MCE model would overcome limitations of the individual approaches, allowing more robust planning and management decisions for aquaculture, other stakeholders and the environment.

Modelling the nitrogen loadings from large yellow croaker (Larimichthys crocea) cage aquaculture.

Large yellow croaker (LYC) cage farming is a rapidly developing industry in the coastal areas of the East China Sea. However, little is known about the environmental nutrient loadings resulting from the current aquaculture practices for this species. In this study, a nitrogenous waste model was developed for LYC based on thermal growth and bioenergetic theories. The growth model produced a good fit with the measured data of the growth trajectory of the fish. The total, dissolved and particulate nitrogen outputs were estimated to be 133, 51 and 82 kg N tonne(-1) of fish production, respectively, with daily dissolved and particulate nitrogen outputs varying from 69 to 104 and 106 to 181 mg N fish(-1), respectively, during the 2012 operational cycle. Greater than 80 % of the nitrogen input from feed was predicted to be lost to the environment, resulting in low nitrogen retention (<20 %) in the fish tissues. Ammonia contributed the greatest proportion (>85 %) of the dissolved nitrogen generated from cage farming. This nitrogen loading assessment model is the first to address nitrogenous output from LYC farming and could be a valuable tool to examine the effects of management and feeding practices on waste from cage farming. The application of this model could help improve the scientific understanding of offshore fish farming systems. Furthermore, the model predicts that a 63 % reduction in nitrogenous waste production could be achieved by switching from the use of trash fish for feed to the use of pelleted feed.

Spatial modeling of environmental vulnerability of marine finfish aquaculture using GIS-based neuro-fuzzy techniques.

Combining GIS with neuro-fuzzy modeling has the advantage that expert scientific knowledge in coastal aquaculture activities can be incorporated into a geospatial model to classify areas particularly vulnerable to pollutants. Data on the physical environment and its suitability for aquaculture in an Irish fjard, which is host to a number of different aquaculture activities, were derived from a three-dimensional hydrodynamic and GIS models. Subsequent incorporation into environmental vulnerability models, based on neuro-fuzzy techniques, highlighted localities particularly vulnerable to aquaculture development. The models produced an overall classification accuracy of 85.71%, with a Kappa coefficient of agreement of 81%, and were sensitive to different input parameters. A statistical comparison between vulnerability scores and nitrogen concentrations in sediment associated with salmon cages showed good correlation. Neuro-fuzzy techniques within GIS modeling classify vulnerability of coastal regions appropriately and have a role in policy decisions for aquaculture site selection.