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Use of prior knowledge to inform restoration projects in estuaries of GOM - Supplement

date: July 28, 2017 autosize: true css: oss.css transition: none

# randomize author order
aut <- c('Marcus Beck', 'Kirsten Dorans', 'Jessica Renee Henkel', 'Kathryn Ireland', 'Ed Sherwood', 'Patricia Varela') %>% 
  sample %>% 
  paste(collapse = ', ')

By Kathryn Ireland, Ed Sherwood, Kirsten Dorans, Jessica Renee Henkel, Marcus Beck, Patricia Varela

Developing Restoration Dataset

Drawing

Developing Restoration Dataset

[1] "HABITAT_ENHANCEMENT"   "HABITAT_ESTABLISHMENT" "HABITAT_PROTECTION"   
[4] "NONPOINT_SOURCE"       "POINT_SOURCE"

Developing Restoration Dataset

======= Use of prior knowledge to inform restoration projects in estuaries of GOM - Supplement

Use of prior knowledge to inform restoration projects in estuaries of GOM - Supplement

July 28, 2017

# randomize author order
aut <- c('Marcus Beck', 'Kirsten Dorans', 'Jessica Renee Henkel', 'Kathryn Ireland', 'Ed Sherwood', 'Patricia Varela') %>% 
  sample %>% 
  paste(collapse = ', ')

By Marcus Beck, Jessica Renee Henkel, Ed Sherwood, Kirsten Dorans, Patricia Varela, Kathryn Ireland

Developing Restoration Dataset

Drawing

Developing Restoration Dataset

  • Merge the WT Projects with the Habitat Restoration Projects
    • filter on lat/lon, separate into tables of activity location vs tables of activity & technology, combine WT/Habitat location tables, combine WT/Habitat descriptive tables
    • All Restoration Activities
    • 5 types of project activities
[1] "HABITAT_ENHANCEMENT"   "HABITAT_ESTABLISHMENT" "HABITAT_PROTECTION"   
[4] "NONPOINT_SOURCE"       "POINT_SOURCE"

Developing Restoration Dataset

  • 28 types of project technologies
>>>>>>> 2087b1c56be0452739237e0dfbbb2dbcc1dfdb53 
 [1] "HYDROLOGIC_RESTORATION" "EXOTIC_CONTROL"        
 [3] "FW_WETLANDS"            "MANGROVES"             
 [5] "SALTMARSH"              "PROTECTION_MANAGEMENT" 
 [7] "SEAGRASS_HABITAT"       "OYSTER_HABITAT"        
 [9] "ACQUISITION"            "BMP_WETLAND_TREATMENT" 
[11] "BMP_BAFFLE_BOX"         "BMP_STORMWATER_POND"   
[13] "BMP_MANAGEMENT"         "BMP_ON_SITE"           
[15] "PS_TREATMENT"           "SEND_TO_WWTP"          
[17] "BMP_ALUM_TREATMENT"     "BMP_TREATMENT_TRAIN"   
[19] "UPLANDS"                "DREDGING"              
[21] "EDUCATION"              "INCREASE_REUSE"        
[23] "MANAGEMENT"             "ATMOSPHERIC_DEPOSITION"
[25] "REGULATION"             "BMP_CDS_UNIT"          
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[27] "BMP_AGRICULTURAL"       "STREET_SWEEPING"

Bayesian Networks

library(bnlearn)
net = model2network("[X1][X2][Xn][X_Child|X1:X2:Xn]")

Training Conditional Probability Tables

======= [27] "BMP_AGRICULTURAL" "STREET_SWEEPING"

Bayesian Networks

library(bnlearn)
net = model2network("[X1][X2][Xn][X_Child|X1:X2:Xn]")

Training Conditional Probability Tables

>>>>>>> 2087b1c56be0452739237e0dfbbb2dbcc1dfdb53