STS Decisions in 2007
     
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    Table of PPA characteristics in 2007

State  Area Distance   N     Max   Max   Backgr. Priority Delimit   Action    Action Comment
PPA ID sqkm    km    traps  catch before  moths    index   index  recommended taken
MN-101    8  132.1     30      1     0   0.029      1.14    3.52      -      delimit
MN-102   11  131.2     90      1     0   0.030      1.14   10.76      -      delimit
MN-104    9  126.3      7      2     0   0.042      1.59    0.94      -      delimit
MN-106   15  121.2     20      1     1   0.048      1.28    2.12      -      delimit
MN-108    8  115.9     10      2     1   0.051      1.78    1.27      -      delimit
MN-113    9  111.9     25      1     1   0.056      1.25    3.35      -      delimit
MN-115    8  107.6      3      2     0   0.059      1.53    0.54      -      delimit
MN-116   11  103.9      4      1     0   0.063      1.04    0.48      -      delimit
MN-121   49   96.1     25      2     0   0.050      1.52    1.92      -      delimit
MN-126   12   89.1      3      2     0   0.067      1.48    0.34      -      delimit
MN-128    5   86.4     16      1     1   0.073      1.18    2.39      -      delimit
MN-129   12   84.4      4      1     0   0.072      1.00    0.46      -      delimit
MN-130   19   82.3     17      1     1   0.075      1.17    1.71      -      delimit

ID = Identification number of the potential problem area (see the map).
Distance =
 
Distance from the beginning of the STS action zone, km. Negative distance means that the PPA is in the monitoring zone.
N traps = Number of traps in the problem area.
Max catch = Maximum number of moths captured within the problem area.
Max before = Maximum number of moths captured in a 3-km neighborhood in the previous year.
Priority index = Shows the priority for STS action (either treatment or delimiting).
Delimit index = Shows how well the area was delimited in the previous year.
Backgr. moths = Mean log moth counts in a 25-km neighborhood.

See details in: Decision algorithm explained.

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    Maintained By Jiang Wu