README.md

In SCCWRP/SQOUnified: Tools for calculating SQO scores based on three Lines of Evidence (Benthic, Tox, Chem)

SQOUnified R Package

The SQOUnified R package provides a suite of tools for assessing sediment quality based on multiple lines of evidence, including benthic indices, chemistry, and toxicity. The package integrates these assessments into a comprehensive site evaluation according to Sediment Quality Objectives (SQO).

This is based on the California Sediment Quality Objectives (CASQO) Technical Manual

Installation

To install the SQOUnified package, you can use the devtools package. Ensure all dependencies are installed beforehand.

# Install necessary dependencies
install.packages(c("dbplyr","reshape2","vegan","dplyr","plyr","purrr","stringr","tidyr"))

# Install the package using devtools
devtools::install_github("SCCWRP/SQOUnified")

The package also comes with example data sets which are available when the package is loaded

# load the package
library(SQOUnified)

# package comes with sample data
# the dataframes are called 'benthic_sampledata', 'chem_sampledata', and 'tox_sampledata'
SQOUnified(benthic = benthic_sampledata, chem = chem_sampledata, tox = tox_sampledata)

### Other misc examples:
# Benthic integrated SQO (returns all individual indices as well)
benthic.sqo(benthic_sampledata)

# Tox summary
tox.summary(tox_sampledata)

# Tox integrated SQO Score
tox.sqo(tox_sampledata)

# Chemical Score Index
CSI(chem_sampledata)

# Chemistry Integrated SQO Score
chem.sqo(chem_sampledata)

Usage

Input Data Requirements for Benthic, Chemistry, and Toxicity Functions

1. Input Data for benthic functions (benthic.sqo, BRI,RBI,IBI,RIVPACS,MAMBI)

The benthic functions require a dataframe with the following columns:

• StationID: An alphanumeric identifier representing the sampling location.
• Replicate: A numeric value indicating the replicate sample number taken at the location.
• SampleDate: The date when the sample was collected.
• Latitude: Latitude in decimal degrees; use a negative sign for Western Hemisphere coordinates.
• Longitude: Longitude in decimal degrees; ensure a negative sign for Western coordinates.
• Taxon: The name of the organism, formatted according to SCAMIT standards. If no organisms are present, use NoOrganismsPresent with an abundance of 0.
• Abundance: The number of each species observed in the sample.
• Salinity: The salinity level (in PSU) at the sampling location.
• Stratum: The habitat stratum (e.g., Bays, Estuaries).
• Exclude: Information on whether the sample should be excluded.

2. Input Data for chemistry functions (chem.sqo, chemdata_prep, CSI, LRM)

The chemistry functions require a dataframe containing:

• StationID: Identifier for the station.
• AnalyteName: Name of the chemical analyte.
• Result: The measurement value for the analyte.
• RL: Reporting limit for the analyte.
• MDL: Method detection limit.
• units: (optional) - Metals should be in mg/dry kg (mg/kg dw) and all organic constituents should be in ug/dry kg (ug/kg dw).
• fieldrep: (optional) - Data is filtered to where fieldrep = 1 if this column is included
• labrep: (optional) - Data is filtered to where labrep = 1 if this column is included
• sampletypecode: (optional) Data is filtered to where sampletypecode = Result in order to prevent inclusion of QA/QC samples

Notes: - Non-detect values should be marked as -88. - All measurements should be expressed on a dry-weight basis: metals in mg/dry kg and organic compounds in ug/dry kg.

3. Input Data for toxicity functions (tox.sqo, tox.summary)

The toxicity functions require a dataframe containing:

• stationid: Alphanumeric identifier of the location.
• toxbatch: Identifier linking results to the control sample.
• species: The genus and species of the tested organism.
• sampletypecode: Type of sample (e.g., Grab, CNEG). Control samples must be included.
• matrix: (optional) - Type of sample matrix (e.g., Whole Sediment, Sediment Water Interface). Be sure to not include Reference Toxicants
• labrep: Numeric identifier for the lab replicate, typically with five replicates per station and species pair.
• result: The percentage of survival or normal development observed in the test.

All Package Functions

Below we will list all the functions of the SQOUnified package, grouped into 4 sections - SQOUnified - Toxicity - Chemistry - Benthic

SQOUnified Function

The primary function SQOUnified computes integrated sediment quality scores based on various lines of evidence: benthic, chemistry, and toxicity data. It requires input data for each category and outputs an integrated assessment.

Example Usage:

# Load the package
library(SQOUnified)

# Example data files
benthic_data <- read.csv("path/to/your/benthic_data.csv")
chem_data <- read.csv("path/to/your/chem_data.csv")
tox_data <- read.csv("path/to/your/tox_data.csv")

# Run the function
result <- SQOUnified(benthic = benthic_data, chem = chem_data, tox = tox_data)
print(result)

Parameters

• benthic: A dataframe containing benthic data for assessment.
• chem: A dataframe containing chemical concentration data.
• tox: A dataframe containing toxicity test results.

The function will compute and log the results for each of these inputs, generating an integrated score based on the criteria defined in the package. The output dataframe also includes all individual scores and indices: - Integrated Chemistry Score - CSI - LRM - Integrated Toxicity Score - (Results from all the tox endpoint tests in the data, typically 'Mytilus NormDev' and 'Eohaustorius Survival') - Integrated Benthic Score - BRI - RBI - IBI - RIVPACS - MAMBI (Not used for calculation of SQO Score)

Toxicity Functions

tox.sqo

The tox.sqo function assesses sediment toxicity by analyzing toxicity test results.

Usage:

tox_results <- tox.sqo(tox_data)

• Input: tox_data - A dataframe containing toxicity test results.
• Output: A dataframe with SQO scores and categories for each station based on toxicity criteria.

tox.summary

The tox.summary function provides a summary of the toxicity results for easier interpretation and reporting.

Usage:

tox_summary <- tox.summary(tox_data)

• Input: tox_data - A dataframe containing toxicity test results.
• Output: A summary dataframe providing an overview of toxicity conditions for all stations.

Chemistry Functions

chem.sqo

The chem.sqo function evaluates the chemical condition of sediments using specific chemical metrics.

Usage:

chem_results <- chem.sqo(chem_data)

• Input: chem_data - A dataframe containing sediment chemistry data.
• Output: A dataframe with SQO scores and categories for each station based on chemical criteria.

chemdata_prep

The chemdata_prep function reformats/prepares the data for getting the SQO score for the chemistry LOE

Usage:

chem <- chemdata_prep(chem_data)

• Input: chem_data - A dataframe containing sediment chemistry data.
• Output: A dataframe prepared for SQO calculation for the chemistry LOE

NOTE: When you put your raw chemistry data in the chemistry functions, this chemdata_prep function is called from within, so no need to preprocess the data before so long as it has those required columns

CSI

The CSI function calculates the Chemical Score Index for the input chemistry data

Usage:

csi.results <- CSI(chem_data)

• Input: CSI - A dataframe containing sediment chemistry data.
• Output: A dataframe with the Chemical Score Index results

LRM

The LRM function calculates the Chemical Score Index for the input chemistry data

Usage:

lrm.results <- LRM(chem_data)

• Input: LRM - A dataframe containing sediment chemistry data.
• Output: A dataframe with the Logistic Regression Model results

Benthic Functions

benthic.sqo

The benthic.sqo function computes benthic indices to evaluate the biological condition of the sediments.

Usage:

benthic_results <- benthic.sqo(benthic_data)

• Input: benthic_data - A dataframe containing benthic community data.
• Output: A dataframe with SQO scores and categories for each station based on benthic criteria.

benthic.sqo

The benthic.sqo function computes benthic indices to evaluate the biological condition of the sediments.

Usage:

benthic_results <- benthic.sqo(benthic_data)

• Input: benthic_data - A dataframe containing benthic community data.
• Output: A dataframe with SQO scores and categories for each station based on benthic criteria. It also contains the individual benthic index scores (BRI, RBI, IBI, RIVPACS and MAMBI)

NOTE: It will be the same for each of the benthic functions - BRI, RBI, IBI, RIVPACS and MAMBI

Example Workflow

Here’s how you can use the SQOUnified package to perform a complete sediment quality assessment:

# Load sample data
benthic_data <- read.csv("path/to/benthic_data.csv")
chem_data <- read.csv("path/to/chem_data.csv")
tox_data <- read.csv("path/to/tox_data.csv")

# Calculate individual SQO components, if you would like
benthic_results <- benthic.sqo(benthic_data)
chem_results <- chem.sqo(chem_data)
tox_results <- tox.sqo(tox_data)

# Get the tox summary table, on its own, if you would like
tox_summary <- tox.summary(tox_data)

# Compute the integrated SQO scores - a dataframe with all scores, including the overall site assessments
integrated_result <- SQOUnified(benthic = benthic_data, chem = chem_data, tox = tox_data)

# View the results
print(integrated_result)

Contributing

Contributions to SQOUnified are welcome, however, if you come across issues using the package, it would be preferable for one to submit an issue rather than a pull request in most cases. If you have a request for a feature(s) then please do not hesitate at all to post an issue.

SCCWRP/SQOUnified documentation built on Nov. 3, 2024, 12:54 a.m.