the biomass calculation. After the grids are selected a Field Statistics function can be
used to calculate the biomass. For example, Fig. 8 illustrates how the biomass was
calculated for Big Barasway. Note that the sum value, in the Statistics for BB_biomass
field, is 236345 kilograms. This number represents the predicted biomass for Big
Barasway.
The 100 x 100 grids are used for descriptive analysis such as the calculation of
densities. The polygons can also be used for mapping attributes. Fig. 9 (A) illustrates
the use of the grids to map the distribution of mature clams densities/m sq. (e.g. clams
>= 25 mm). Mature clam densities of 161 to 269/m sq. are acceptable for good growth
while densities higher than 269/m sq. can impede growth because of competition for
food and space (Newell and Hidu, 1994). From an aquaculture management perspective
areas with excessive mature clam densities, depending on the size class distribution,
need to be culled or harvested. Thus mapping of the mature clam densities can identify
areas that require culling or harvesting as well as areas of low densities that require
seeding. A possible solution to the excessive density problem is to move the culled
juvenile or pre-recruit clams to areas of low densities.
Mapping clam densities with vector polygon data will reveal trends, but if the
data are to be used in any prescriptive analysis the spatial data must be converted to a
raster format. The visualization and analysis of clam densities in a GIS can be applied to
a raster version of the original grid. However, the clam density distribution is a
continuous spatial variable and should be mapped as a continuous surface where
possible. Continuous surfaces are created in a raster GIS by using spatial interpolation
algorithms. ArcView™ Spatial Analyst provides an inverse distance squared (IDW) and
a spline interpolation routine. The process of converting the clam density data from
polygon to point coordinates for interpolation involved several steps. Firstly, stratified
random samples of coordinates were generated for each of the three areas. The
sampling density ensured that 25m spacing occurred between the points. Secondly, a
point-in-polygon procedure was used to attach the grid density size class data to the
points database. An inverse distance squared algorithm
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GIS and Aquaculture: Soft-Shell Clam Site Assessment
used to calculate the biomass. For example, Fig. 8 illustrates how the biomass was
calculated for Big Barasway. Note that the sum value, in the Statistics for BB_biomass
field, is 236345 kilograms. This number represents the predicted biomass for Big
Barasway.
The 100 x 100 grids are used for descriptive analysis such as the calculation of
densities. The polygons can also be used for mapping attributes. Fig. 9 (A) illustrates
the use of the grids to map the distribution of mature clams densities/m sq. (e.g. clams
>= 25 mm). Mature clam densities of 161 to 269/m sq. are acceptable for good growth
while densities higher than 269/m sq. can impede growth because of competition for
food and space (Newell and Hidu, 1994). From an aquaculture management perspective
areas with excessive mature clam densities, depending on the size class distribution,
need to be culled or harvested. Thus mapping of the mature clam densities can identify
areas that require culling or harvesting as well as areas of low densities that require
seeding. A possible solution to the excessive density problem is to move the culled
juvenile or pre-recruit clams to areas of low densities.
Mapping clam densities with vector polygon data will reveal trends, but if the
data are to be used in any prescriptive analysis the spatial data must be converted to a
raster format. The visualization and analysis of clam densities in a GIS can be applied to
a raster version of the original grid. However, the clam density distribution is a
continuous spatial variable and should be mapped as a continuous surface where
possible. Continuous surfaces are created in a raster GIS by using spatial interpolation
algorithms. ArcView™ Spatial Analyst provides an inverse distance squared (IDW) and
a spline interpolation routine. The process of converting the clam density data from
polygon to point coordinates for interpolation involved several steps. Firstly, stratified
random samples of coordinates were generated for each of the three areas. The
sampling density ensured that 25m spacing occurred between the points. Secondly, a
point-in-polygon procedure was used to attach the grid density size class data to the
points database. An inverse distance squared algorithm
287
GIS and Aquaculture: Soft-Shell Clam Site Assessment