Reference Document 6-2
Appendix 6
Assign the plant data to the cores • Use a spatial join to join the dissolved plant EO polygon feature class created above to the intact habitat cores. A one-to-many join makes a copy of each core polygon for each unique species, which lets us see which species has been observed in each core. This provides helpful reference data, but this information is sensitive and cannot be shared publicly. • Use another spatial join to join the dissolved plant EO polygon feature class created above to intact habitat cores. A one-to-one join just makes one row for each core, and has a join_count field that counts number of unique species. So, in the resulting layer, the join_count field has the number of unique plants that meet our criteria. • Make a copy of the plant join_count field. Since we will ultimately add the plant and animal counts together, we need to retain each separately with meaningful field names. Prepare the animal Heritage data • Start the animal analysis using the Source Features points and lines. • Convert the points to polygons we can combine them in one feature layer with the lines. Buffer the resulting polygons by 90 m to account for possible spatial accuracy issues. • Make a copy of the above output to use for the append step below. • Convert the lines to polygons so we can combine them in one feature layer with the points. Buffer the resulting polygons by 90 m to account for possible spatial accuracy issues. • Combine the polygonised points and lines using the append function. • Start narrowing down the polygonised animal data by retaining only records that are animals or that have an accuracy of medium or better. o Use a select and the following query: ACCURACY IN ('1-Very High', '2-High', '3- Medium') And NAME_CATGY = 'Animal' • Further narrow down the polygonised animal data by retaining only records that have the values S1, S2, or S3 in the S_Rank field or that have the values G1, G2, G3, T1, T2, or T3 in the G_Rank field. o Use a select and the following query: S_RANK LIKE '%S1%' Or S_RANK LIKE '%S2%' Or S_RANK LIKE '%S3%' Or G_RANK LIKE '%G1%' Or G_RANK LIKE '%G2%' Or G_RANK LIKE '%G3%' Or G_RANK LIKE '%T1%' Or G_RANK LIKE '%T2%' Or G_RANK LIKE '%T3%' • Continue to narrow down the polygonised animal data by retaining only records with a last observed date of the year 2000 or more recent by making a new numeric field from the DESCR field. This is a text field, but for the records that we want to keep, the text field always starts with the year. By calculating this new numeric field from the
2025 NC Wildlife Action Plan
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