| Title: | Water Quality Metrics Calculator |
|---|---|
| Description: | Calculate different metrics based on aquatic macroinvertebrate density data (individuals per square meter) to assess water quality (Prat N et al. 2009). |
| Authors: | Juan Manuel Cabrera [aut, cre], Julieta Capeletti [aut] |
| Maintainer: | Juan Manuel Cabrera <[email protected]> |
| License: | GPL (>= 3) |
| Version: | 1.1.0 |
| Built: | 2025-03-12 03:15:22 UTC |
| Source: | https://github.com/cran/metrix |
Calculates BMWP, BMWP', BMWP”, IMRP and ICBrio indexes
biotic_ind(dataset, store = FALSE, dec_c = ".", verbose = FALSE)biotic_ind(dataset, store = FALSE, dec_c = ".", verbose = FALSE)
dataset |
A data.frame obtained from |
store |
A logical value indicating if the user want to store the results in a file. |
dec_c |
A character used for decimal separator on results file. |
verbose |
A logical value indicating if progress messages should be given. |
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.
This function returns a list with the following components:
bioind_n |
The numerical values of the biotic indexes. |
bioind_c |
The water quality class assign to each sample site according to the numerical value of the biotic indexes |
Juan Manuel Cabrera and Julieta Capeletti.
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
read_data, bmwp_ind, bmwp_p_ind, bmwp_p_p_ind, imrp_ind, icbrio_ind
#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#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
Calculate Biological Monitoring Working Party (BMWP) and Average Score Per Taxon (ASPT) indexes.
bmwp_ind(dataset, store = FALSE, dec_c = ".", verbose = FALSE)bmwp_ind(dataset, store = FALSE, dec_c = ".", verbose = FALSE)
dataset |
A data.frame obtained from |
store |
A logical value indicating if the user want to store the results in a file. |
dec_c |
A character used for decimal separator on results file. |
verbose |
A logical value indicating if progress messages should be given. |
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).
This function returns a list with the following components:
Ibmwp_n |
The numerical BMWP and ASPT index (Armitage et al. 1983). |
Ibmwp_c |
The BMWP and ASPT water quality class (Armitage et al. 1983). |
Juan Manuel Cabrera and Julieta Capeletti.
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
read_data, bmwp_p_ind, bmwp_p_p_ind, biotic_ind
#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#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
Calculate Biological Monitoring Working Party (BMWP) prime index.
bmwp_p_ind(dataset, store = FALSE, dec_c = ".", verbose = FALSE)bmwp_p_ind(dataset, store = FALSE, dec_c = ".", verbose = FALSE)
dataset |
A data.frame obtained from |
store |
A logical value indicating if the user want to store the results in a file. |
dec_c |
A character used for decimal separator on results file. |
verbose |
A logical value indicating if progress messages should be given. |
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).
This function returns a list with the following components:
Ibmwp_p_n |
The numerical BMWP’ index (Alba Tercedor and Sánchez Ortega 1988). |
Ibmwp_p_c |
The BMWP’ index water quality classes (Alba Tercedor and Sánchez Ortega 1988). |
Juan Manuel Cabrera and Julieta Capeletti.
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
read_data, bmwp_ind, bmwp_p_p_ind, biotic_ind
#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#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
Calculate Biological Monitoring Working Party (BMWP) prime prime index (Loyola, 2000)
bmwp_p_p_ind(dataset, store = FALSE, dec_c = ".", verbose = FALSE)bmwp_p_p_ind(dataset, store = FALSE, dec_c = ".", verbose = FALSE)
dataset |
A data.frame obtained from |
store |
A logical value indicating if the user want to store the results in a file. |
dec_c |
A character used for decimal separator on results file. |
verbose |
A logical value indicating if progress messages should be given. |
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).
This function returns a list with the following components:
Ibmwp_p_p_n |
The numerical BMWP’’ index (Loyola, 2000). |
Ibmwp_p_p_c |
The BMWP’’ water quality classes (Loyola, 2000). |
Juan Manuel Cabrera and Julieta Capeletti.
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
read_data, bmwp_ind, bmwp_p_ind, biotic_ind
#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#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
Calculates the relative abundance of particular taxa in the assemblage in percentage terms.
comp_metrics(dataset, store = FALSE, dec_c = ".", verbose = FALSE)comp_metrics(dataset, store = FALSE, dec_c = ".", verbose = FALSE)
dataset |
A data.frame obtained from |
store |
A logical value indicating if the user want to store the results in a file. |
dec_c |
A character used for decimal separator on results file. |
verbose |
A logical value indicating if progress messages should be given. |
Provides information on the makeup of the assemblage and the relative contribution of the populations to the total fauna (Barbour et al., 1996).
This function returns a data.frame with all the calculated composition measures:
per_ephe |
% Ephemeroptera. |
per_molus |
% Mollusca. |
per_gastr |
%Gastropoda. |
per_biv |
%Bivalvia. |
per_crus |
%Crustacea. |
per_oli |
% Oligochaeta. |
per_amph |
% Amphipoda. |
per_ostr |
% Ostracoda. |
per_ephetricho |
% Ephemeroptera + Trichoptera. |
per_naid |
% Naididae. |
per_chir_dip |
% Diptera Chironomidae. |
per_non_chir_dip |
%Diptera no Chironomidae. |
per_polym |
%Polymitarcidae. |
per_hyal |
%Hyalella. |
per_coch |
%Cochliopidae. |
per_tricho |
%Trichoptera. |
per_subchiro |
%Chironominae. |
per_suborth |
%Orthocladiinae. |
per_subtany |
%Tanypodinae. |
Juan Manuel Cabrera and Julieta Capeletti.
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
#Load example data example_data #Run comp_metrics with that example_data compmetrics<-comp_metrics(example_data) #Check results compmetrics#Load example data example_data #Run comp_metrics with that example_data compmetrics<-comp_metrics(example_data) #Check results compmetrics
Calculates density measures
densi_metrics(dataset, store = FALSE, dec_c = ".", verbose = FALSE)densi_metrics(dataset, store = FALSE, dec_c = ".", verbose = FALSE)
dataset |
A data.frame obtained from |
store |
A logical value indicating if the user want to store the results in a file. |
dec_c |
A character used for decimal separator on results file. |
verbose |
A logical value indicating if progress messages should be given. |
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).
This function returns a data.frame with all the calculated density measures:
den_chir_dip |
Diptera Chironomidae density. |
den_non_chir_dip |
Diptera no Chironomidae density. |
den_ephe |
Ephemeroptera density. |
den_molus |
Mollusca density. |
den_gastr |
Gastropoda density. |
den_biv |
Bivalvia density. |
den_crus |
Crustacea density. |
den_nais |
Naididae density. |
den_lhoff |
Limnodrilus hoffmeisteri density. |
den_bothr |
Bothrioneurum density. |
den_tubi |
Tubifex density. |
den_dero |
Dero density. |
den_prist |
Pristina density. |
den_chiro |
Chironomus density. |
den_nais |
Nais density. |
den_hele |
Heleobia density. |
den_subchiro |
Chironominae density. |
den_suborth |
Orthocladiinae density. |
den_subtany |
Tanypodinae density. |
den_t |
Total density. |
den_t_bothr |
Bothrioneurum/Total density. |
den_t_lhoff |
Limnodrilus hoffmeisteri/Total density. |
den_t_tubi |
Tubifex/Total density. |
den_t_dero |
Dero/Total density. |
den_t_prist |
Pristina/Total density. |
den_t_chiro |
Chironomus/Total density. |
den_oli |
Oligochaeta density. |
den_tricho |
Trichoptera density. |
den_ostr |
Ostracoda density. |
den_amph |
Amphipoda density. |
den_polym |
Polymitarcidae density. |
den_hyal |
Hyalella density. |
den_coch |
Cochliopidae density. |
den_chironomidae |
Chironomidae density. |
Juan Manuel Cabrera and Julieta Capeletti.
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
#Load example data example_data #Run densi_metrics with that example_data densimetrics<-densi_metrics(example_data) #Check results densimetrics#Load example data example_data #Run densi_metrics with that example_data densimetrics<-densi_metrics(example_data) #Check results densimetrics
Example taxa data from two sites (P1 and P2).
example_dataexample_data
The data is properly formatted for being used with Metrix functions.
read_data, metrix_table_template
Calculate ICBrio index
icbrio_ind(dataset, store = FALSE, dec_c = ".", verbose = FALSE)icbrio_ind(dataset, store = FALSE, dec_c = ".", verbose = FALSE)
dataset |
A data.frame obtained from |
store |
A logical value indicating if the user want to store the results in a file. |
dec_c |
A character used for decimal separator on results file. |
verbose |
A logical value indicating if progress messages should be given. |
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.
This function returns a list with the following components:
Icbrio_n |
The numerical ICBrio index (Kuhlmann et al. 2012). |
Icbrio_c |
The ICBrio water quality class (Kuhlmann et al. 2012). |
Juan Manuel Cabrera and Julieta Capeletti.
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
#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#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
Calculates the Indice de Macroinvertebrados en Rios Pampeanos Index
imrp_ind(dataset, store = FALSE, dec_c = ".", verbose = FALSE)imrp_ind(dataset, store = FALSE, dec_c = ".", verbose = FALSE)
dataset |
A data.frame obtained from |
store |
A logical value indicating if the user want to store the results in a file. |
dec_c |
A character used for decimal separator on results file. |
verbose |
A logical value indicating if progress messages should be given. |
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.
This function returns a list with the following components:
Imrp_n |
The numerical IMRP index (Rodrigues Capítulo 1999). |
Imrp_c |
The IMRP index water quality class (Rodrigues Capítulo 1999). |
Juan Manuel Cabrera and Julieta Capeletti.
Rodrigues Capítulo A (1999). The macroinvertebrate as indicators of water quality in Pampean rivers. https://www.biotaxa.org/RSEA/article/view/32771
#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#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 compatible table format template generator
metrix_table_template(store = FALSE)metrix_table_template(store = FALSE)
store |
A logical value indicating if the user want to store the results in a file.#' |
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.
The function returns:
template_table |
A table that can be used as input for other metrix functions. |
Juan Manuel Cabrera and Julieta Capeletti.
Load data from a formatted taxon table
read_data(file_name, correct = TRUE, verbose = FALSE)read_data(file_name, correct = TRUE, verbose = FALSE)
file_name |
Name of formatted taxon table file. Use |
correct |
A logical value indicating if the auto correct system should be used (default |
verbose |
A logical value indicating if progress messages should be given. |
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.
The function returns:
dataset |
A table that can be used as input for other metrix functions. |
Juan Manuel Cabrera and Julieta Capeletti.
Calculates richness measures
rich_metrics(dataset, store = FALSE, dec_c = ".", verbose = FALSE)rich_metrics(dataset, store = FALSE, dec_c = ".", verbose = FALSE)
dataset |
A data.frame obtained from |
store |
A logical value indicating if the user want to store the results in a file. |
dec_c |
A character used for decimal separator on results file. |
verbose |
A logical value indicating if progress messages should be given. |
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).
This function returns a data.frame with all the calculated richness measures:
n_taxa |
N° total taxa. |
n_fam |
N° of families. |
n_gen |
N° of genus. |
n_insec_fam |
N° of insects families. |
n_non_insec_order |
N° of orders of invertebrates no insects. |
n_dip_fam |
N° of Diptera families. |
n_dip_gen |
N° of Diptera genus. |
n_dip_chir_gen |
N° of Diptera Chironomidae genus. |
n_chir_tax |
N° of Diptera Chironomidae taxa. |
n_tany_tax |
N° of Tanytarisni taxa. |
n_stemp_tax |
N° of Stempellina taxa. |
n_non_chir_dip_tax |
N° of Diptera no Chironomidae taxa. |
n_mol_tax |
N° of Mollusca taxa. |
n_gastr_tax |
N° of Gastropoda taxa. |
n_biv_tax |
N° of Bivalvia taxa. |
n_crus_tax |
N° of Crustacea taxa. |
n_crusmol |
N° of Crustacea + Mollusca taxa. |
n_oligo_tax |
N° of Oligochaeta taxa. |
n_ephetrich |
N° of Ephemeroptera + Trichoptera taxa. |
Juan Manuel Cabrera and Julieta Capeletti.
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
#Load example data example_data #Run rich_metrics with that example_data richmetrics<-rich_metrics(example_data) #Check results richmetrics#Load example data example_data #Run rich_metrics with that example_data richmetrics<-rich_metrics(example_data) #Check results richmetrics
Indicate sensitivity of the assemblage and component species to various types of disturbance.
tol_metrics(dataset, store = FALSE, dec_c = ".", verbose = FALSE)tol_metrics(dataset, store = FALSE, dec_c = ".", verbose = FALSE)
dataset |
A data.frame obtained from |
store |
A logical value indicating if the user want to store the results in a file. |
dec_c |
A character used for decimal separator on results file. |
verbose |
A logical value indicating if progress messages should be given. |
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.
This function returns a data.frame with all the calculated tolerance measures.:
r_oligochir |
Oligochaeta/Chironomidae. |
r_oligoset |
Oligochaeta with setaform chaetae/Oligochaeta without setaform chaetae. |
r_tanychir |
Tanytarsini/Chironomidae. |
den_t_lhoff |
Limnodrilus hoffmeisteri/Total density. |
den_t_bothr |
Bothrioneurum/Total density. |
den_t_tubi |
Tubifex/Total density. |
den_t_dero |
Dero/Total density. |
den_t_prist |
Pristina/Total density. |
den_t_chiro |
Chironomus/Total density. |
Juan Manuel Cabrera and Julieta Capeletti.
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
#Load example data example_data #Run tol_metrics with that example_data tolmetrics<-tol_metrics(example_data) #Check results tolmetrics#Load example data example_data #Run tol_metrics with that example_data tolmetrics<-tol_metrics(example_data) #Check results tolmetrics
Calculates trophic measures
troph_metrics(dataset, store = FALSE, dec_c = ".", verbose = FALSE)troph_metrics(dataset, store = FALSE, dec_c = ".", verbose = FALSE)
dataset |
A data.frame obtained from |
store |
A logical value indicating if the user want to store the results in a file. |
dec_c |
A character used for decimal separator on results file. |
verbose |
A logical value indicating if progress messages should be given. |
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).
This function returns a data.frame with all the calculated trophic measures:
per_pred |
% Predator. |
per_filt |
% Filtering collector. |
per_shred |
% Shredders. |
per_scrap |
% Scrapers. |
per_gath |
% Gathering collector. |
n_pred |
N° of Predator. |
n_filt |
N° of Filtering collector. |
n_shred |
N° of Shredders. |
n_scrap |
N° of Scrapers. |
n_gath |
N° of Gathering collector. |
Juan Manuel Cabrera and Julieta Capeletti.
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
#Load example data example_data #Run troph_metrics with that example_data trophmetrics<-troph_metrics(example_data) #Check results trophmetrics#Load example data example_data #Run troph_metrics with that example_data trophmetrics<-troph_metrics(example_data) #Check results trophmetrics
This function performs all the calculations available in metrix package.
water_quality_analysis(dataset, store = FALSE, dec_c = ".", verbose = FALSE)water_quality_analysis(dataset, store = FALSE, dec_c = ".", verbose = FALSE)
dataset |
A data.frame obtained from |
store |
A logical value indicating if the user want to store the results in a file. |
dec_c |
A character used for decimal separator on results file. |
verbose |
A logical value indicating if progress messages should be given. |
This function returns a list with the following components:
bioind_n |
The numerical values of the biotic indexes. |
bioind_c |
The water quality class asign to each sample site according to the numerical value of the biotic index. |
densimetrics |
A data.frame with all the calculated density measures. |
tolmetrics |
A data.frame with all the calculated tolerance measures. |
compmetrics |
A data.frame with all the calculated composition measures. |
trophmetrics |
A data.frame with all the calculated trophic measures. |
richmetrics |
A data.frame with all the calculated richness measures. |
Juan Manuel Cabrera and Julieta Capeletti.
read_data, biotic_ind, densi_metrics, icbrio_ind, tol_metrics, comp_metrics, troph_metrics, rich_metrics
#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$richmetrics#Load example data example_data #Run water_quality_analysis with that example_data complete<-water_quality_analysis(example_data) #Check results complete$bioind_n complete$bioind_c complete$densimetrics complete$tolmetrics complete$compmetrics complete$trophmetrics complete$richmetrics