RBI: Compute the relative benthic index (RBI) score.

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

Compute the relative benthic index (RBI) score.

Description

The RBI is the weighted sum of: (a) four community metrics related to biodiversity (total number of taxa, number of crustacean taxa, abundance of crustacean individuals, and number of mollusc taxa), (b) abundances of three positive indicator taxa, and (c) the presence of two negative indicator species.

Usage

BRI(benthic_data)

Arguments

BenthiCData

a data frame stored in the R environment. Note that this data frame MUST contain the following information with these headings: StationID - an alpha-numeric identifier of the location; Replicate - a numeric identifying the replicate number of samples taken at the location; SampleDate - the date of sample collection; Latitude - latitude in decimal degrees; Longitude - longitude in decimal degrees. Make sure there is a negative sign for the Western coordinates; Taxon - name of the fauna, ideally in SCAMIT ed12 format, do not use sp. or spp., use sp only or just the Genus. If no animals were present in the sample use NoOrganismsPresent with 0 abundance; Abundance - the number of each Species observed in a sample; Salinity - the salinity observed at the location in PSU, ideally at time of sampling; Stratum - ; Exclude - ;

Details

The RBI is the weighted sum of: (a) four community metrics related to biodiversity (total number of taxa, number of crustacean taxa, abundance of crustacean individuals, and number of mollusc taxa), (b) abundances of three positive indicator taxa, and (c) the presence of two negative indicator species.

The data needed to calculate the RBI are: (1) Total number of taxa, (2) Number of mollusc taxa, (3) Number of crustacean individuals, (4) Number of individuals of Monocorophium insidiosum, (5) Number of individuals of Asthenothaerus diegensis, (6) Number of individuals of Goniada littorea, (7) Whether the data has the presence of Capitella capitata complex, and (8) Whether the data has the presence of Oligochaeta.

To compute the RBI, the first step is to normalize the values for the benthic community metrics relative to maxima for the data used to develop the RBI for the Southern California Marine Bays habitat, to produce values relative to the maxima that are referred to as scaled values. The scaled value calculations use the following formulae:

Total Number of Taxa / 99 Number of Mollusc Taxa / 28 Number of Crustacean Taxa / 29

The next step is to calculate the Taxa Richness Weighted Value (TWV) from the scaled values by the equation:

TWV = Scaled Total Number of Taxa + Scaled Number of Mollusc Taxa + Scaled Number of Crustacean Taxa + (0.25 * Scaled Abundance of Crustacea)

Next, the value for the two negative indicator taxa (NIT) is calculated. The two negative indicator taxa are Capitella capitata complex and Oligochaeta. For each of these taxa that are present, in any abundance, the NIT is decreased by 0.1. Therefore, if neither were found the NIT = 0, if both are found the NIT = -0.2.

The next step is to calculate the value for the three positive indicator taxa (PIT). The positive indicator taxa are Monocorophium insidiosum, Asthenothaerus diegensis, and Goniada littorea. First, the PIT value is calculated for each species using the following equations:

\frac{\sqrt[4]{Monocorophium~ insidiosum \textrm{abundance}}}{\sqrt[4]{473}}

\frac{\sqrt[4]{Asthenothaerus~ diegensis \textrm{abundance}}}{\sqrt[4]{27}}

\frac{\sqrt[4]{Goniada littorea~ \textrm{abundance}}}{\sqrt[4]{15}}

The three species PIT values are then summed to calculate the PIT value for the sample. If none of the three species is present, then the sample PIT = 0.

The next step is to calculate the Raw RBI:

\textrm{Raw RBI} = \textrm{TWV + NIT + } (2 \times \textrm{PIT})

The final calculation is for the RBI score, normalizing the Raw RBI by the minimum and maximum Raw RBI values in the index development data:

\textrm{RBI Score} = (\textrm{Raw RBI} - 0.03)/4.69

The last step in the RBI process is to compare the RBI Score to a set of thresholds to determine the RBI category (Table 4).

<Insert Table 4>

For the function to run, the following packages NEED to be installed: tidyverse, reshape2, vegan, and readxl. Additionally the EQR.R function must also be installed and is included with this code.

The output of the function will be a dataframe with StationID, Replicate, SampleDate, Latitude, Longitude, SalZone (The Salinity Zone assigned by M-AMBI), AMBI_Score, S (Species Richness), H (Species Diversity), Oligo_pct (Relative Abundance of Oligochaetes), MAMBI_Score, Orig_MAMBI_Condition, New_MAMBI_Condition, Use_MAMBI (Can M-AMBI be applied?), Use_AMBI (Can AMBI be applied?), and YesEG (

Value

The output of the function will be a dataframe with

StationID,

Replicate,

SampleDate,

Latitude,

Longitude,

SalZone (The Salinity Zone assigned by M-AMBI),

AMBI_Score,

S (Species Richness),

H (Species Diversity),

Oligo_pct (Relative Abundance of Oligochaetes),

MAMBI_Score,

Orig_MAMBI_Condition,

New_MAMBI_Condition,

Use_MAMBI (Can M-AMBI be applied?),

Use_AMBI (Can AMBI be applied?),

YesEG (

Examples

  RBI(benthic_data)
  RBI(BenthicData)

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