Biotic indexes

Description

Calculates BMWP, BMWP', BMWP”, IMRP and ICBrio indexes

Usage

biotic_ind(dataset, store = FALSE, dec_c = ".", verbose = FALSE)

Arguments

Details

The biotic indicators consist of the combination of two or three properties of the association: taxa richness and tolerance/intolerance to contamination for qualitative indices, and these together with abundance (absolute or relative) for quantitative indices. They are usually expressed in the form of a single numerical value that synthesizes the characteristics of all the species present.

Value

This function returns a list with the following components:

Author(s)

Juan Manuel Cabrera and Julieta Capeletti.

References

Armitage PD, Moss D, Wright JF & Furse MT (1983). The performance of a new biological water quality score system based on macroinvertebrates over a wide range of unpolluted running-water sites. doi: 10.1016/0043-1354(83)90188-4

Alba-Tercedor J & Sánchez-Ortega A (1988). A simple and quick method to evaluate biological quality of running freshwater based on Hellawell (1978). https://www.limnetica.com/documentos/limnetica/limnetica-4-1-p-51.pdf

Loyola RGN (2000). Atual estágio do IAP no uso de índices biológicos de qualidade. Bioindicadores qualidade aguas https://www.iat.pr.gov.br/sites/agua-terra/arquivos_restritos/files/documento/2021-03/bioindicadores_qualidade_aguas_2001_2002.pdf

Rodrigues Capítulo A (1999). The macroinvertebrate as indicators of water quality in Pampean rivers. https://www.biotaxa.org/RSEA/article/view/32771

Kuhlmann M, Imbimbo HV, Ogura LL (2012). Protocolo para o biomonitoramento com as comunidades bentônicas de rios e reservatórios do estado de São Paulo. https://cetesb.sp.gov.br/aguas-interiores/wp-content/uploads/sites/12/2013/11/protocolo-biomonitoramento-2012.pdf

See Also

read_data, bmwp_ind, bmwp_p_ind, bmwp_p_p_ind, imrp_ind, icbrio_ind

Examples

#Load example data
example_data

#Run biotic_ind with that example_data
biotic<-biotic_ind(example_data)

#Check results
biotic$bioind_n
biotic$bioind_c

BMWP and ASPT index

Description

Calculate Biological Monitoring Working Party (BMWP) and Average Score Per Taxon (ASPT) indexes.

Usage

bmwp_ind(dataset, store = FALSE, dec_c = ".", verbose = FALSE)

Arguments

Details

The Biological Monitoring Working Party (BMWP) was set up in 1976. Its terms of reference were to recommend a system which could be used to assess the biological status of a river, and which would be suitable for presenting a broad picture of one aspect of the biological condition of rivers in the UK. Identification to family is sufficient to calculate the BMWP score. The average score per taxon (ASPT) is calculated by dividing the score by the total number of scoring taxa (Armitage et al. 1983).

Value

This function returns a list with the following components:

Author(s)

Juan Manuel Cabrera and Julieta Capeletti.

References

Armitage PD, Moss D, Wright JF & Furse MT (1983). The performance of a new biological water quality score system based on macroinvertebrates over a wide range of unpolluted running-water sites. doi: 10.1016/0043-1354(83)90188-4

See Also

read_data, bmwp_p_ind, bmwp_p_p_ind, biotic_ind

Examples

#Load example data
example_data

#Run bmwp_ind with that example_data
bmwp<-bmwp_ind(example_data)

#Check results
bmwp$Ibmwp_n
bmwp$Ibmwp_c

BMWP prime index

Description

Calculate Biological Monitoring Working Party (BMWP) prime index.

Usage

bmwp_p_ind(dataset, store = FALSE, dec_c = ".", verbose = FALSE)

Arguments

Details

The BMWP’ is an adaptation of the BMWP (Armitage et al. 1983) created to evaluate the biological quality of the Iberian Peninsula. Most of the macroinvertebrate families living in the Iberian Peninsula have been added to the original index and some of the scores have been changed (Alba Tercedor & Sánchez Ortega 1988).

Value

This function returns a list with the following components:

Author(s)

Juan Manuel Cabrera and Julieta Capeletti.

References

Armitage PD, Moss D, Wright JF & Furse MT (1983). The performance of a new biological water quality score system based on macroinvertebrates over a wide range of unpolluted running-water sites. doi: 10.1016/0043-1354(83)90188-4

Alba-Tercedor J & Sánchez-Ortega A (1988). A simple and quick method to evaluate biological quality of running freshwater based on Hellawell (1978). https://www.limnetica.com/documentos/limnetica/limnetica-4-1-p-51.pdf

See Also

read_data, bmwp_ind, bmwp_p_p_ind, biotic_ind

Examples

#Load example data
example_data

#Run bmwp_p_ind with that example_data
bmwpp<-bmwp_p_ind(example_data)

#Check results
bmwpp$Ibmwp_p_n
bmwpp$Ibmwp_p_c

BMWP prime prime index

Description

Calculate Biological Monitoring Working Party (BMWP) prime prime index (Loyola, 2000)

Usage

bmwp_p_p_ind(dataset, store = FALSE, dec_c = ".", verbose = FALSE)

Arguments

Details

The new BMWP’’ is an adaptation of the BMWP (Armitage et al. 1983) for the lotic environments of the Paraná River. This adaptation was based on the observation of the occurrence of the important families in the rivers in the region. Some families were added by ecological equivalence and others by similarity in the level of tolerance to contamination. The scores assigned to the different families were not changed (Loyola, 2000).

Value

This function returns a list with the following components:

Author(s)

Juan Manuel Cabrera and Julieta Capeletti.

References

Armitage PD, Moss D, Wright JF & Furse MT (1983). The performance of a new biological water quality score system based on macroinvertebrates over a wide range of unpolluted running-water sites. doi: 10.1016/0043-1354(83)90188-4

Loyola RGN (2000). Atual estágio do IAP no uso de índices biológicos de qualidade. Bioindicadores qualidade aguas https://www.iat.pr.gov.br/sites/agua-terra/arquivos_restritos/files/documento/2021-03/bioindicadores_qualidade_aguas_2001_2002.pdf

See Also

read_data, bmwp_ind, bmwp_p_ind, biotic_ind

Examples

#Load example data
example_data

#Run bmwp_p_p_ind with that example_data
bmwpp<-bmwp_p_p_ind(example_data)

#Check results
bmwpp$Ibmwp_p_p_n
bmwpp$Ibmwp_p_p_c

Composition metrics

Description

Calculates the relative abundance of particular taxa in the assemblage in percentage terms.

Usage

comp_metrics(dataset, store = FALSE, dec_c = ".", verbose = FALSE)

Arguments

Details

Provides information on the makeup of the assemblage and the relative contribution of the populations to the total fauna (Barbour et al., 1996).

Value

This function returns a data.frame with all the calculated composition measures:

Author(s)

Juan Manuel Cabrera and Julieta Capeletti.

References

Barbour MT, Gerritsen J, Griffith GE, Frydenborg R, McCarron E, White JS & Bastian ML (1996). A Framework for Biological Criteria for Florida Streams Using Benthic Macroinvertebrates. doi: 10.2307/1467948

See Also

read_data

Examples

#Load example data
example_data

#Run comp_metrics with that example_data
compmetrics<-comp_metrics(example_data)

#Check results
compmetrics

Density metrics

Description

Calculates density measures

Usage

densi_metrics(dataset, store = FALSE, dec_c = ".", verbose = FALSE)

Arguments

Details

Density is a universal measure used in all types of biological studies. Density is best classified with trophic measurements because it is an element of production; however, it is difficult to interpret because it requires careful quantification and is not monotonous in response (i.e., density can decrease or increase in response to contamination) (Barbour et al., 1996).

Value

This function returns a data.frame with all the calculated density measures:

Author(s)

Juan Manuel Cabrera and Julieta Capeletti.

References

Barbour MT, Gerritsen J, Griffith GE, Frydenborg R, McCarron E, White JS & Bastian ML (1996). A Framework for Biological Criteria for Florida Streams Using Benthic Macroinvertebrates. doi: 10.2307/1467948

See Also

read_data

Examples

#Load example data
example_data

#Run densi_metrics with that example_data
densimetrics<-densi_metrics(example_data)

#Check results
densimetrics

Example data for Metrix package.

Description

Example taxa data from two sites (P1 and P2).

Usage

example_data

Format

The data is properly formatted for being used with Metrix functions.

See Also

read_data, metrix_table_template

ICBrio index

Description

Calculate ICBrio index

Usage

icbrio_ind(dataset, store = FALSE, dec_c = ".", verbose = FALSE)

Arguments

Details

ICBrio was created to monitor the quality of inland waters in the state of São Paulo. It is a multimetric index that includes different metrics: richness, Shannon-Wiener diversity index (H’), Sequential Comparison index (ICS), Tanytarsini/Chironomidae ratio, richness of sensitive taxa and dominance of tolerant groups. Only one of the diversity indices (H’ or ICS) is considered to calculate it (in this case, the function H’ from 'vegan' package). The final value, which generates the diagnosis or classification of habitat quality, combines the arithmetic mean of the value obtained with the sum of the points of each metric.

Value

This function returns a list with the following components:

Author(s)

Juan Manuel Cabrera and Julieta Capeletti.

References

Kuhlmann M, Imbimbo HV, Ogura LL (2012). Protocolo para o biomonitoramento com as comunidades bentônicas de rios e reservatórios do estado de São Paulo. https://cetesb.sp.gov.br/aguas-interiores/wp-content/uploads/sites/12/2013/11/protocolo-biomonitoramento-2012.pdf

See Also

read_data, biotic_ind

Examples

#Load example data
example_data

#Run icbrio_ind with that example_data
icb<-icbrio_ind(example_data)

#Check results
icb$Icbrio_n
icb$Icbrio_c

IMRP index

Description

Calculates the Indice de Macroinvertebrados en Rios Pampeanos Index

Usage

imrp_ind(dataset, store = FALSE, dec_c = ".", verbose = FALSE)

Arguments

Details

IMRP was created for the rivers of the Pampean plain (Rodrigues Capítulo 1999). This index is based on the sum of ecological values for each taxon. This value is inversely proportional to the degree of tolerance to contamination, varying from 0.1 for highly tolerant taxa to 2.0 for the most sensitive. Identification to family is sufficient to calculate the IMRP score.

Value

This function returns a list with the following components:

Author(s)

Juan Manuel Cabrera and Julieta Capeletti.

References

Rodrigues Capítulo A (1999). The macroinvertebrate as indicators of water quality in Pampean rivers. https://www.biotaxa.org/RSEA/article/view/32771

See Also

read_data, biotic_ind

Examples

#Load example data
example_data

#Run imrp_ind with that example_data
imrp<-imrp_ind(example_data)

#Check results
imrp$Imrp_n
imrp$Imrp_c

Metrix data template

Description

Metrix compatible table format template generator

Usage

metrix_table_template(store = FALSE)

Arguments

Details

This function creates a properly formatted table for being used with metrix functions.
The format of the input table must contain 8 columns that refer to the scientific and functional classification of the taxa. The first 7 columns refer to Class, Order, Family, Subfamily, Tribe, Genus and Species. Special care must be taken when entering the taxa nomenclature, because if it is misspelled, the package will not take this classification into account. It is not necessary to put genera and species in italics. Column 8 refers to the functional group of the taxa, which can be filtering collectors (FC), gathering collectors (GC), predators (P), scrapers (SCR) and shredders (SHR). After these columns, the places where you want to calculate the packet metrics are entered. It is essential that the site data are located after these taxonomic and functional classification columns. The user will be able to load the table with the amount of taxa and sites, as desired.
If store = TRUE the function will create a .csv file with properly named columns and saves it as template.csv on the current working directory.

Value

The function returns:

Author(s)

Juan Manuel Cabrera and Julieta Capeletti.

Read taxa data

Description

Load data from a formatted taxon table

Usage

read_data(file_name, correct = TRUE, verbose = FALSE)

Arguments

Details

This function reads a formatted taxa .csv file and checks whether it is properly formatted. This function will determine which character to use as separator for data and decimals.
The format of the input table must contain 8 columns that refer to the scientific and functional classification of the taxa. The first 7 columns refer to Class, Order, Family, Subfamily, Tribe, Genus and Species. Special care must be taken when entering the taxa nomenclature, because if it is misspelled, the package will not take this classification into account. It is not necessary to put genera and species in italics. Column 8 refers to the functional group of the taxa, which can be filtering collectors (FC), gathering collectors (GC), predators (P), scrapers (SCR) and shredders (SHR). After these columns, the places where you want to calculate the packet metrics are entered. It is essential that the site data are located after these taxonomic and functional classification columns. The user will be able to load the table with the amount of taxa and sites, as desired.
Site columns with no entries and rows with no information of functional classification of the taxa will not be loaded. This function also has an autocorrect system that compares the words used to describe a taxon with a list of properly written words in order to find possible input errors. If correct = TRUE the autocorrect system will check all the names and perform corrections when possible (the file will not be modified). The autocorrect system will inform the user if it finds an issue that needs a manual check.

Value

The function returns:

Author(s)

Juan Manuel Cabrera and Julieta Capeletti.

Richness metrics

Description

Calculates richness measures

Usage

rich_metrics(dataset, store = FALSE, dec_c = ".", verbose = FALSE)

Arguments

Details

The richness measures reflect the diversity of the aquatic complex (Resh et al. 1995). Increased diversity correlates with increased assemblage health and suggests that niche space, habitat, and food source are adequate to support the survival and spread of many taxa. The number of taxa measures the general variety of the macroinvertebrate assemblage. Identities of major taxonomic groups are not derived from the total taxa metric, but the removal of taxa from naturally diverse systems can be detected easily (Barbour et al., 1996).

Value

This function returns a data.frame with all the calculated richness measures:

Author(s)

Juan Manuel Cabrera and Julieta Capeletti.

References

Resh VH, Norris RH & Barbour MT (1995). Design and implementation of rapid assessment approaches for water resource monitoring using benthic macroinvertebrates. doi: 10.1111/j.1442-9993.1995.tb00525.x

Barbour MT, Gerritsen J, Griffith GE, Frydenborg R, McCarron E, White JS & Bastian ML (1996). A Framework for Biological Criteria for Florida Streams Using Benthic Macroinvertebrates. doi: 10.2307/1467948

See Also

read_data

Examples

#Load example data
example_data

#Run rich_metrics with that example_data
richmetrics<-rich_metrics(example_data)

#Check results
richmetrics

Tolerance metrics

Description

Indicate sensitivity of the assemblage and component species to various types of disturbance.

Usage

tol_metrics(dataset, store = FALSE, dec_c = ".", verbose = FALSE)

Arguments

Details

Most of the metrics applied in the study of macroinvertebrates use as a key factor the tolerance or intolerance of the different taxa to a certain disturbance, normally organic contamination. The relationship between the number of organisms that are tolerant and intolerant to contamination is a common resource in the metrics used. Further metrics (multimetric indexes) can be derived from a combination of these primary metrics (Prat et al., 2009). The Limnodrilus hoffmeisteri/total density ratio, which was developed by Marchese & Ezcurra de Drago (1999), increases in environments with organic contamination.

Value

This function returns a data.frame with all the calculated tolerance measures.:

Author(s)

Juan Manuel Cabrera and Julieta Capeletti.

References

Marchese M & Ezcurra de Drago I (1999). Use of benthic macroinvertebrates as organic pollution indicators in lotic environments of the Parana River drainage basin. https://agro.icm.edu.pl/agro/element/bwmeta1.element.agro-article-e981d07b-e469-4460-a7fe-3239650cd089

Prat N, Ríos B, Acosta R & Rieradevall M (2009). Los macroinvertebrados como indicadores de calidad de las aguas. http://www.ub.edu/riosandes/docs/MacroIndLatinAmcompag0908.pdf

See Also

read_data

Examples

#Load example data
example_data

#Run tol_metrics with that example_data
tolmetrics<-tol_metrics(example_data)

#Check results
tolmetrics

Trophic metrics

Description

Calculates trophic measures

Usage

troph_metrics(dataset, store = FALSE, dec_c = ".", verbose = FALSE)

Arguments

Details

Trophic metrics are surrogates of complex processes such as trophic interaction, production and food source availability. Specialized feeders, such as scrapers, piercers, and shredders, are the more sensitive, and are thought to be well represented in healthy streams. Generalists, such as collectors and filterers, have a broader range of acceptable food materials than specialists, and are thus more tolerant to pollution that might alter availability of certain food (Barbour et al., 1996).

Value

This function returns a data.frame with all the calculated trophic measures:

Author(s)

Juan Manuel Cabrera and Julieta Capeletti.

References

Barbour MT, Gerritsen J, Griffith GE, Frydenborg R, McCarron E, White JS & Bastian ML (1996). A Framework for Biological Criteria for Florida Streams Using Benthic Macroinvertebrates. doi: 10.2307/1467948

See Also

read_data

Examples

#Load example data
example_data

#Run troph_metrics with that example_data
trophmetrics<-troph_metrics(example_data)

#Check results
trophmetrics

Water quality analysis

Description

This function performs all the calculations available in metrix package.

Usage

water_quality_analysis(dataset, store = FALSE, dec_c = ".", verbose = FALSE)

Arguments

Value

This function returns a list with the following components:

Author(s)

Juan Manuel Cabrera and Julieta Capeletti.

See Also

read_data, biotic_ind, densi_metrics, icbrio_ind, tol_metrics, comp_metrics, troph_metrics, rich_metrics

Examples

#Load example data
example_data

#Run water_quality_analysis with that example_data
complete<-water_quality_analysis(example_data)

#Check results
complete$bioind_n
complete$bioind_c
complete$densimetrics
complete$tolmetrics
complete$compmetrics
complete$trophmetrics
complete$richmetrics