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

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

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

• Synthesize data in space and time to evaluate cumulative effects of restoration projects

• Develop a Bayesian Decision Network with empirical observations to evaluate likelihood of potential outcomes

• Expand to other estuaries using a flexible framework

• “Softer” Restoration -> Local ordinances (e.g. ferilizer restrictions), Education, etc.
• “Soft” Restoration -> Habitat Creation, Enhancement and Management/Protection Measures
• “Hard” Restoration -> Stormwater BMPs, Point Source Reductions through Time, Regulations

• Water Treatment Projects
  • Two Raw Datasets: http://apdb.tbeptech.org
  • Descriptions, names, location
  • dplyr: combine dataset (left_join), subset (filter) to subbasins of interest, find WT projects not listed as complete for further investigation (anti-join)

# A tibble: 5 x 19
  HeaderID                             Project_Name      Bay_Segment
     <int>                                    <chr>            <chr>
1        8 Delany Creek Wetland Restoration Project Hillsborough Bay
2       10           Cone Ranch Restoration Project Hillsborough Bay
3       11   29th or 30th Street Outfall/ Mckay Bay Hillsborough Bay
4       14          Palma Ceia Area Stormwater Pond Hillsborough Bay
5       15            North Tampa Pond Enlargements Hillsborough Bay
# ... with 16 more variables: Lead_Entity <chr>, Completion_Date <int>,
#   TP_Reduction_lbs_yr <dbl>, TN_Reduction_lbs_yr <dbl>,
#   TSS_Reduction_lbs_yr <int>, ProjectName <chr>,
#   OngoingInitiation <int>, DiscontinuedDate <int>, CompletionDate <int>,
#   ActualProjectCost <chr>, FundingSource <chr>,
#   ProjectDescriptionText <chr>, NonPointProject <int>,
#   PointProject <int>, ProjectLatitude <dbl>, ProjectLongitude <dbl>

• Water Treatment Projects

  • Manual categorization of WT projects by technique = Categorized WT Projects
  • Broad classification: 5 unique WT project activities

[1] "Nonpoint_Source"       "Habitat_Enhancement"   "Habitat_Establishment"
[4] "Habitat_Protection"    "Point_Source"         

• Water Treatment Projects
  • Finer classification: 26 unique WT project technologies

 [1] "BMP_Wetland_Treatment"  "Hydrologic_Restoration"
 [3] "BMP_Baffle_Box"         "BMP_Stormwater_Pond"   
 [5] "FW_Wetlands"            "BMP_Management"        
 [7] "Acquisition"            "BMP_On_Site"           
 [9] "PS_Treatment"           "Mangroves"             
[11] "Send_to_WWTP"           "BMP_Alum_Treatment"    
[13] "BMP_Treatment_Train"    "Uplands"               
[15] "Dredging"               "Education"             
[17] "Increase_Reuse"         "Management"            
[19] "Atmospheric_Deposition" "Protection_Management" 
[21] "Regulation"             "Exotic_Control"        
[23] "Saltmarsh"              "BMP_CDS_Unit"          
[25] "BMP_Agricultural"       "Street_Sweeping"       

• Habitat Restoration Projects
  • Raw dataset: http://http://maps.wateratlas.usf.edu/tampabay/
  • Manual categorization of Habitat Restoration projects by technique = Categorized Habitation Restoration Projects
  • Broad classification: 3 unique WT project activities

[1] "Habitat_Enhancement"   "Habitat_Establishment" "Habitat_Protection"   

• Habitat Restoration Projects
  • Finer classification: 9 unique habitat restoration technologies

[1] "Hydrologic_Restoration" "Exotic_Control"        
[3] "FW_Wetlands"            "Mangroves"             
[5] "Saltmarsh"              "Protection_Management" 
[7] "Seagrass_Habitat"       "Oyster_Habitat"        
[9] "Acquisition"           

WQ and restoration sites: Spatial match

WQ and restoration sites: Spatial match

WQ and restoration sites: Temporal match

WQ and restoration sites: Spatial match

WQ and restoration sites: Temporal match, before/after

WQ and restoration sites: Spatial match

WQ and restoration sites: Temporal match, before/after, slice

What do the data look like? For one water quality station matched to many restoration sites…

WQ and restoration sites: Temporal match, before/after, slice

# A tibble: 4 x 3
# Groups:   stat [1]
   stat     cmb     cval
  <int>   <chr>    <dbl>
1     7 hab_aft 8.255185
2     7 hab_bef 8.350187
3     7 wtr_aft 8.053273
4     7 wtr_bef 8.129733

What do the data look like? For many water quality stations matched to many restoration sites…

# A tibble: 20 x 4
    stat     hab     wtr      cval
   <int>  <fctr>  <fctr>     <dbl>
 1     6 hab_aft wtr_aft  8.903273
 2     6 hab_aft wtr_bef 11.720206
 3     6 hab_bef wtr_aft 11.902951
 4     6 hab_bef wtr_bef 14.719883
 5     7 hab_aft wtr_aft  8.154229
 6     7 hab_aft wtr_bef  8.192459
 7     7 hab_bef wtr_aft  8.201730
 8     7 hab_bef wtr_bef  8.239960
 9     8 hab_aft wtr_aft 19.867100
10     8 hab_aft wtr_bef 17.444274
11     8 hab_bef wtr_aft 17.331973
12     8 hab_bef wtr_bef 14.909147
13     9 hab_aft wtr_aft  9.030021
14     9 hab_aft wtr_bef  8.621069
15     9 hab_bef wtr_aft  8.398558
16     9 hab_bef wtr_bef  7.989606
17    11 hab_aft wtr_aft  6.576058
18    11 hab_aft wtr_bef  6.727664
19    11 hab_bef wtr_aft  8.112902
20    11 hab_bef wtr_bef  8.264508

What do the data look like? For many water quality stations matched to many restoration sites…

What do the data look like? For many water quality stations matched to many restoration sites…

What do the data look like? For many water quality stations matched to many restoration sites…

• In other words, what is the conditional distribution of chlorophyll given restoration type and before/after effect?

• Similar to a two-way ANOVA…

\[ Chl \sim\ f\left(Water \space\ treatment \times Habitat \space\ restoration\right) \]

• This can be extrapolated to additional 'treatments', or a three-way ANOVA

\[ Chl \sim\ f\left(Water \space\ treatment \times Habitat \space\ restoration \times Salinity \right) \]

Conditional distributions on two-levels:

Conditional distributions on three-levels:

Conditional distributions on three-levels:

Conditional distributions on three-levels:

• Water quality (chlorophyll) responds to restoration with varying effects by salinity

• In the frequentist framework - mean chlorophyll varies given treatment

\[ Chl \sim\ f\left(Water \space\ treatment \times Habitat \space\ restoration \times Salinity \right) \]

• In the Bayesian framework - probability of an event depends on occurrence of other events

\[ P\left(Chl \mid Event\right) = \frac{P\left(Event \mid Chl\right) \cdot P\left(Chl \right)}{P \left(Event\right)} \]

What is the probability of low/medium/high chlorophyll given other events?

• Do water quality conditions differ by restoration type?
• Does it differ by salinity as a natural covariate?
• Is the change in agreement with expectation?

BN lets us evaluate likelihood of potential outcomes given conditional distributions

GitHub: https://github.com/fawda123/oss2017_synthesis

Extra slides: final_pres_supp

Shiny: http://tbeptech.org:3939/content/5/, http://tbeptech.org:3939/content/6/

Jiggly plot: bayes_network