PROJECT
Accounting for Uncertainty in Regional Flow-Ecology Relationships


CITATION
Morgan, B., B. Lane (2020). Accounting for Uncertainty in Regional Flow-Ecology Relationships, 
	HydroShare, http://www.hydroshare.org/resource/a731d9971eb44518898ea21e163544be

	
PROJECT DESCRIPTION
This repository contains the data and code needed to reproduce results from Morgan and Lane, 2022. 


PREREQUISITES
A basic working knowledge of R programming, ArcGIS Pro, and Netica is needed to replicate the results of this study. 

To use the data and code available in this project, please download and install: 

	>R (https://cran.r-project.org/bin/windows/base/ )  and Rstudio (https://rstudio.com/products/rstudio/download/#download ).
	License not required.

	>ArcGIS Pro (https://pro.arcgis.com/en/pro-app/latest/get-started/download-arcgis-pro.htm ). License required. 
	For installation and licensing questions, see the FAQ page (https://pro.arcgis.com/en/pro-app/latest/get-started/faq.htm#anchor2 ).

	>Netica (limited mode) (https://www.norsys.com/download.html ). License not required. For installation instructions, 
	see the Online Installation Help page (https://www.norsys.com/WebHelp/NETICA/X_Installation.htm ). 

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CONTENT DESCRIPTION
This repository is organized based on the two main objectives of this study: a systematic literature review of 
peer-reviewed flow-ecology studies and Bayesian Network modelling. The folders contain the necessary input data 
and code required to reproduce the results, as well as the expected outputs. 

The Literature_Review folder contains three main folders and the following subfolders and files: 

>Lit_Review_Files
	>>Flow_ecology_lit_review_master.xlsx is a supporting file and includes the recorded attributes for the 
	flow-ecology studies in this research.
	>>LitReview_codebook.xlsx is a supporting file and includes the codes applied in Atlas for the literature review. 
	>>Atlas_summaries.xlsx is a supporting file and includes the code-cooccurrence output tables from Atlas.

>Lit_Analysis_R 
	>>Data 
		Network_input_igraph_format.xlsx is a supporting file and shows how relationships in the Flow_ecology_lit_review_master.xlsx 
		were expanded into individual “from” and “to” links for the conceptual network model input files. 
		>>>Relationship_channel_types.csv is an input file for the script “Channel_type_SFE.R” and contains the channel types 
		represented in each flow-ecology relationship. 
		>>>SFE_edges.csv is an input file for the script “Network_Analysis_igraph.R” and contains the links between variables 
		for the conceptual network model. 
		SFE_nodes.csv is an input file for the script “Network_Analysis_igraph.R” and contains the variables (ie nodes) for 
		the conceptual network model. 
		>>>study_dates.csv is an input file for the scripts “WYT_lit_review.R” and “Seasonality.R” and includes the dates 
		of data collection for flow-ecology relationships. 

	>>Script 
		>>>Network_analysis_igraph.R is the R script used to produce the conceptual network diagram of flow-ecology relationships 
		and produces Network.png. 
		>>>Seasonality.R is the R script used to determine the seasonality of data collection for flow-ecology relationships 
		and produces seasonality.png.
		>>>WYT_CT_combined.R is the R script used to determine the distribution of relationships across water year types and 
		channel types and the number of unique water year types and channel types within relationships. It also produces 
		WYT_CT_combined.png.  

	>>Output 
		>>>Figures 
			>>>>Network.png is an output from the script “Network_analysis_igraph.R.”
			>>>>seasonality.png is an output from the script “Seasonality.R.”
			>>>>WYT_CT_combined.png is an output from the script “WYT_CT_combined.R.”

>Lit_Review_Map
	>>Study_density_SFER.ppkx is an ArcGIS Pro map package that contains the associated files and map of the density 
	of data collection within peer-reviewed flow-ecology studies. 		

Size: 2.56 MB
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The BN_Model folder contains two main folders and the following subfolders and files: 

>BN_Development 
	>>BN_Base_Netica.neta is a Netica file for the BN model under base probabilities and dry hydrologic conditions. 
	>>Conceptual_model.pptx is a PowerPoint file of the conceptual model that informed the structure of the BN model. 

>BN_Model_R
	>>Data
		>>>S_ranges
				>>>>CPT_Al.csv contains the conditional probabilities for the Algae node in scenario A. 
				>>>>CPT_Dis.csv contains the conditional probabilities for the Disease node in scenario A. 
				>>>>CPT_DS.csv contains the conditional probabilities for the Dry-season Baseflow node in scenario A. 
				>>>>CPT_FG.csv contains the conditional probabilities for the Fish Growth node in scenario A. 
				>>>>CPT_Fsup.csv contains the conditional probabilities for the Food Supply node in scenario A. 
				>>>>CPT_LC.csv contains the conditional probabilities for the Longitudinal Connectivity node in scenario A. 
				>>>>CPT_PF.csv contains the conditional probabilities for the Peak Flow node in scenario A. 
				>>>>CPT_Sed.csv contains the conditional probabilities for the Sediment node in scenario A. 
				>>>>CPT_Stcond.csv contains the conditional probabilities for the Steelhead Condition node in scenario A. 
				>>>>CPT_Temp.csv contains the conditional probabilities for the Temperature node in scenario A. 

		>>>S_levels
				>>>>CPT_Al.csv contains the conditional probabilities for the Algae node in scenario B. 
				>>>>CPT_Dis.csv contains the conditional probabilities for the Disease node in scenario B. 
				>>>>CPT_DS.csv contains the conditional probabilities for the Dry-season Baseflow node in scenario B. 
				>>>>CPT_FG.csv contains the conditional probabilities for the Fish Growth node in scenario B. 
				>>>>CPT_Fsup.csv contains the conditional probabilities for the Food Supply node in scenario B. 
				>>>>CPT_LC.csv contains the conditional probabilities for the Longitudinal Connectivity node in scenario B. 
				>>>>CPT_PF.csv contains the conditional probabilities for the Peak Flow node in scenario B. 
				>>>>CPT_Sed.csv contains the conditional probabilities for the Sediment node in scenario B. 
				>>>>CPT_Stcond.csv contains the conditional probabilities for the Steelhead Condition node in scenario B. 
				>>>>CPT_Temp.csv contains the conditional probabilities for the Temperature node in scenario B. 

	>>Scripts 
		>>>SFER_BNmodel_SA_ranges.R is the R script used to generate the BN model outcomes for Scenario A, 
		which evaluates uncertainty ranges in base probabilities.
		>>>SFER_BNmodel_SB_levels.R is the R script used to generate the BN model outcomes for Scenario B, 
		which evaluates the location and magnitude of uncertainties in the model. It also produces BWP_Heatmap.png.
		>>>SFER_SA_Range_generation.R is an R script that generated the input conditional probabilities for Scenario A 
		(30 unique sets of conditional probabilities between a specified range for each variable). 
		This code does not need to be run to reproduce the results in this study. 

	>>Output 
		>>>Data 
			>>>>S_ranges_generation. This folder contains csv files of conditional probabilities for all nodes in the BN model, 
			which were output from the R script “SFER_SA_Range_generation.R.” These files are input data to Scenario A 
			(see BN_Model_R/Data/S_ranges).
			>>>>ScenarioA_Data_For_Plotting.csv is an output from SFER_BNModel_SA_Ranges.R and is used as a plotting input to 
			SFER_BNModel_SB_Levels.R.

		>>>Figures
			>>>>BWP_Heatmap.png is an output from the script “SFER_BNmodel_SB_levels.R.”
			

Size: 913 KB

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REPLICATION INSTRUCTIONS
Download and unzip the repository. 

>To reproduce the conceptual network diagram of flow-ecology relationships (Network.png): 
	>>Navigate to Literature_Review/Lit_Analysis_R/Script/Network_analysis_igraph.R and double click to open in R studio. 
	>>Run the code line-by-line using control-enter or run the entire code by highlighting all code and pressing control-enter. 
	The script automatically sets a working directory and installs all necessary packages for the code.
	>>Figure outputs from this code will automatically be placed in Lit_Analysis_R/Output/Figures and include Network.png. 
	>>Save the script and close R. 

>To reproduce the seasonality of data collection plot (seasonality.png): 
	>>Navigate to Literature_Review/Lit_Analysis_R/Script/Seasonality.R and double click to open in R studio. 
	>>Run the code line-by-line using control-enter or run the entire code by highlighting all code and pressing control-enter. 
	The script automatically sets a working directory and installs all necessary packages for the code.
	>>Figure outputs from this code will automatically be placed in Lit_Analysis_R/Output/Figures and include seasonality.png. 
	>>Save the script and close R. 

>To reproduce the water year type/ channel type and flow-ecology relationship plot (WYT_CT_Combined.png): 
	>>Navigate to Literature_Review/Lit_Analysis_R/Script/WYT_CT_combined.R and double click to open in R studio. 
	>>Run the code line-by-line using control-enter or run the entire code by highlighting all code and pressing control-enter. 
	The script automatically sets a working directory and installs all necessary packages for the code.
	>>Figure outputs from this code will automatically be placed in Lit_Analysis_R/Output/Figures and include 
	WYT_CT_combined.png. 
	>>Save the script and close R.  

>To reproduce the data collection density across flow-ecology studies map: 
	>>Navigate to Literature_Review/Lit_review_map/Study_density_SFER.ppkx and double click to open in ArcGIS Pro. 
	>>Click the “Studydensity_layout” tab to view the map of data collection density across flow-ecology studies. 
	>>Save the map and close ArcGIS Pro.

>To reproduce the conceptual/ Bayesian Model structure: 
	>>Navigate to BN_Model/BN_Development/Conceptual_model.pptx and double click to open in PowerPoint.  
	>>Save the file and close PowerPoint. 

>To reproduce the BN model in Netica: 
	>>Navigate to BN_Model/BN_Development/BN_Base_Netica.neta and double click to open in Netica.  
	>>A grey box will appear on the screen. Click “Limited Mode.”  The BN model should appear on the screen. 
	>>Save the model and close Netica.  

>To reproduce the box and whisker plot of model outcomes under Scenario A and heatmap plot of model outcomes under
Scenario B (BWP_Heatmap.png): 
	>>Navigate to BN_Model/BN_Model_R/Scripts/SFER_BNmodel_SA_levels.R and double click to open in R studio. 
	>>Run the code line-by-line using control-enter or run the entire code by highlighting all code and pressing control-enter. 
	The script automatically sets a working directory and installs all necessary packages for the code.
	>>A csv output from this code will automatically be placed in BN_Model_R/Output/Data and will be read as an input to 
	SFER_BNModel_SB_levels.R.
	>>Save the script and close. 

	>>Navigate to BN_Model/BN_Model_R/Scripts/SFER_BNmodel_SB_levels.R and double click to open in R studio. 
	>>Run the code line-by-line using control-enter or run the entire code by highlighting all code and pressing control-enter. 
	The script automatically sets a working directory and installs all necessary packages for the code.
	>>The figure output from this code will automatically be placed in BN_Model_R/Output/Figures and includes BWP_Heatmap.png. 
	>>Save the script and close R.