Package 'benthos' reference manual

Title:	Marine Benthic Ecosystem Analysis
Description:	Preprocessing tools and biodiversity measures (species abundance, species richness, population heterogeneity and sensitivity) for analysing marine benthic data. See Van Loon et al. (2015) <doi:10.1016/j.seares.2015.05.002> for an application of these tools.
Authors:	Dennis Walvoort [aut, cre, cph]
Maintainer:	Dennis Walvoort <[email protected]>
License:	GPL (>= 3)
Version:	1.3-8
Built:	2024-11-27 06:37:26 UTC
Source:	CRAN

Marine Benthic Ecosystem Analysis

Description

benthos provides functions for facilitating the analysis of marine benthos data. Examples are indicators like species abundance, species richness, Margalef's index of diversity, Shannon's Entropy, AZTI's Marine Biotic Index, and the Infaunal Trophic Index (ITI). In addition functions for data pooling, genus-to-species conversion and validation and conversion of species names to those recommended by the World Register of Marine Species are provided.

Details

All functions are designed to work seamlessly with the dplyr-package which implements a grammar for structured data manimpulation.

The benthos-package contains functions for estimating various species abundance, species richness, species heterogeneity and species sensitivity measures:

total abundance (total_abundance)
abundance (abundance)
species richness (species_richness)
Margalef's index of diversity (margalef)
Rygg's index of diversity (rygg)
Hurlbert's Expected Number of Species (hurlbert)
Simpson's measure of concentration (simpson)
Hurlbert's probability of interspecific encounter (PIE) (hpie)
Shannon's index or entropy (shannon)
Hill's diversity number (hill)
AZTI Marine Biotic Index (AMBI) (ambi)
Infaunal Trophic Index (ITI) (iti)
Bray-Curtis dissimilarity (bray_curtis)

In addition, functions are available for data preparation, e.g.:

data pooling (pool)
genus to species conversion (genus_to_species)

For an overview of all the functions in the package click on the index link at the bottom of this page.

Author(s)

Dennis Walvoort [email protected]

Abundance

Description

The number of indiviuals in each taxon.

Usage

abundance(.data = NULL, taxon = NULL, count)

abundance_(.data = NULL, taxon = NULL, count)
abundance(.data = NULL, taxon = NULL, count)

abundance_(.data = NULL, taxon = NULL, count)

Arguments

`.data`	data in a `data.frame`, `tibble`, `data.table`, database etc.
`taxon`	name of column in `.data` containing taxa
`count`	name of column in `.data` containing counts

Value

numeric vector with abundance per taxon.

Functions

abundance_(): version suitable for calling from a function (see package lazyeval).

Note

due to pooling, the abundance is not necessarily an integer

Examples

 abundance(
     taxon = c("Euspira pulchella", "Nephtys cirrosa"), 
     count = c(4, 6)
 )
abundance(
     taxon = c("Euspira pulchella", "Nephtys cirrosa"), 
     count = c(4, 6)
 )

AZTI Marine Biotic Index (AMBI)

Description

AZTI Marine Biotic Index (AMBI) according to Borja et al. (2000)

Usage

ambi(.data = NULL, taxon, count, group = NULL)

ambi_(.data = NULL, taxon, count, group = NULL)

has_ambi(.data = NULL, taxon, group = NULL)

has_ambi_(.data = NULL, taxon, group = NULL)
ambi(.data = NULL, taxon, count, group = NULL)

ambi_(.data = NULL, taxon, count, group = NULL)

has_ambi(.data = NULL, taxon, group = NULL)

has_ambi_(.data = NULL, taxon, group = NULL)

Arguments

`.data`	data in a `data.frame`, `tibble`, `data.table`, database etc.
`taxon`	species names
`count`	counts of individuals (`numeric`)
`group`	sensitivity groups I, II, III, IV, or V

Details

The index is given by:

$c_\mathrm{b} = \frac{3}{2} \sum_{i=2}^5 (i-1) p_i$

where $p_i$ is the proportion of species in sensitivity group $i$ .

Value

numeric vector of length 1 containing the AMBI

Functions

ambi_(): version suitable for calling from a function (see package lazyeval).
has_ambi(): tests if an AMBI sensitivity group is available for taxon (returns TRUE (available) or FALSE (unavailable))
has_ambi_(): version suitable for calling from a function (see package lazyeval).

References

Borja, A., J. Franco and V. Perez, 2000. A Marine Biotic Index to Establish the Ecological Quality of Soft-Bottom Benthos Within European Estuarine and Coastal Environments. Marine Pollution Bulletin 40:1100-1114

Examples

 ambi(
     taxon = c("Euspira pulchella", "Nephtys cirrosa"), 
     count = c(4, 6)
 )
 
     data(oosterschelde)
     has_ambi(oosterschelde, TAXON)
 
ambi(
     taxon = c("Euspira pulchella", "Nephtys cirrosa"), 
     count = c(4, 6)
 )
 
     data(oosterschelde)
     has_ambi(oosterschelde, TAXON)

Convert Taxon Names to Comply with WoRMS/TWN

Description

Taxon names are standardized according to the World Register of Marine Species (WoRMS) database. The conversion is case-insensitive. For this conversion, the TWN-list (Taxa Water management the Netherlands) is used, extended with species of the Southern North Sea. See references below for download locations.

Usage

as_accepted(taxon, taxa = NULL)
as_accepted(taxon, taxa = NULL)

Arguments

`taxon`	`character` vector, containing taxon names
`taxa`	an optional table usually created with `read_taxa`.

Value

character vector with WoRMS/TWN compliant species names

References

https://www.marinespecies.org/

https://taxainfo.nl/

Bray-Curtis Dissimilarity

Description

Bray-Curtis Dissimilarity

Usage

bray_curtis(n1, n2)
bray_curtis(n1, n2)

Arguments

`n1`	abundances of species at site 1
`n2`	abundances of species at site 2

Value

Bray-Curtis dissimilarity (0..1, 0 = equal, 1 = different)

Note

species in n1 and n2 need to be aligned

Examples

     n1 <- c(11,  0, 7,  8, 0)
     n2 <- c(24, 37, 5, 18, 1)
     bray_curtis(n1, n2)
n1 <- c(11,  0, 7,  8, 0)
     n2 <- c(24, 37, 5, 18, 1)
     bray_curtis(n1, n2)

Ecological Quality Ratio (EQR)

Description

The ecological quality ratio is the ratio beween a parameter value and its reference value:

$EQR = \frac{x-bad}{ref-bad}$

Depending on bad and ref, the EQR usually (but not necessarily!) varies between 0 (bad ecological quality) and 1 ( ecological quality equals the reference status).

Usage

eqr(x, bad, ref)
eqr(x, bad, ref)

Arguments

`x`	numeric vector containing benthic indices
`bad`	the value for a bad status
`ref`	the value for a reference status

Value

numeric vector with EQR-values: low values indicate bad ecological quality and high values indicate good ecological quality.

Genus to Species Conversion

Description

This alorithm reallocates the counts of taxa, that are only indentified at the genus level to taxa in the same sampling unit and of the same genus but that are identified on the species level. The redistribution of counts is proportional to the number of counts at the species level.

Usage

genus_to_species(is_genus, count)
genus_to_species(is_genus, count)

Arguments

`is_genus`	`logical` vector with elements `TRUE` if the corresponding taxon is on the genus level, and `FALSE` if it is on the `species` level.
`count`	`numeric` vector with elements giving the counts of each corresponding taxon.

Value

numeric vector with updated counts. The counts for the taxon on the genus level have been set to zero.

Note

Parameters is_genus and count are of the same length and correspond to the same taxon.

The resulting counts are not necessarily integers.

Examples

     genus_to_species(is_genus = c(TRUE, FALSE, FALSE), count = c(3, 10, 20))
     genus_to_species(is_genus = c(TRUE, FALSE, FALSE), count = c(1, 10, 20))
genus_to_species(is_genus = c(TRUE, FALSE, FALSE), count = c(3, 10, 20))
     genus_to_species(is_genus = c(TRUE, FALSE, FALSE), count = c(1, 10, 20))

Get Supplementary AMBI Sensitivity Groups

Description

This function gets sensitivity groups that are supplementary to the AMBI of Borja et al., (2000)

Usage

get_ambi(which = "NL")
get_ambi(which = "NL")

Arguments

which

which AMBI supplement? Currently only the Dutch supplement is available (which = "NL")

Value

a data frame with columns TAXON containing taxa and GROUP containing Dutch AMBI-groups

References

Get Infaunal Trophic Index

Description

This function gets the sensitivity groups to estimate the infaunal trophic index of Gittenberger et al., (2011)

Usage

get_iti()
get_iti()

Value

a data frame with columns TAXON containing taxa and GROUP containing the ITI-groups of Gittenberger & Van Loon (2013).

References

Gittenberger A. and W. van Loon, 2013. Sensitivities of marine macrozoobenthos to environmental pressures in the Netherlands. Nederlandse Faunistische Mededelingen 41: 79-112.

Harmonize Case

Description

Convert text to the most occuring case. In case of ties, the first occurence in sorted order will be taken.

Usage

harmonize(x)
harmonize(x)

Arguments

`x`	character vector

Value

character vector with harmonized names (i.e., same case)

Examples

 x <- c("FOO", "Foo", "bar", "FOO", "bar", "FOO", "Bar")
 y <- harmonize(x)
 stopifnot(all.equal(y, c("FOO", "FOO", "bar", "FOO", "bar", "FOO", "bar")))
 
x <- c("FOO", "Foo", "bar", "FOO", "bar", "FOO", "Bar")
 y <- harmonize(x)
 stopifnot(all.equal(y, c("FOO", "FOO", "bar", "FOO", "bar", "FOO", "bar")))

Hill's Diversity Numbers

Description

According to Hill (1973): "a diversity number is figuratively a measure of how many species are present if we examine the sample down to a certain depth among its rarities. If we examine superficially (e.g., by using $N_2$ ) we shall see only the more abundant species. If we look deeply (e.g., by using $N_0$ ) we shall see all the species present."

Hill's diversity numbers are given by:

$N_a=\sum{i=1}^S (p_i^a)^{1/(1-a)}$

Special cases are:

$N_{-\infty}$: reciprocal of the proportional abundance of the rarest species;
$N_0$: total number of species present;
$N_1$: exp(H), where H: Shannon's index (see also shannon);
$N_2$: reciprocal of Simpson's index (see also simpson);
$N_{\infty}$: reciprocal of the proportional abundance of the commonest species.

Usage

hill(.data = NULL, taxon, count, a = 0)

hill_(.data = NULL, taxon, count, a = 0)

hill0(.data = NULL, taxon, count)

hill0_(.data = NULL, taxon, count)

hill1(.data = NULL, taxon, count)

hill1_(.data = NULL, taxon, count)

hill2(.data = NULL, taxon, count)

hill2_(.data = NULL, taxon, count)
hill(.data = NULL, taxon, count, a = 0)

hill_(.data = NULL, taxon, count, a = 0)

hill0(.data = NULL, taxon, count)

hill0_(.data = NULL, taxon, count)

hill1(.data = NULL, taxon, count)

hill1_(.data = NULL, taxon, count)

hill2(.data = NULL, taxon, count)

hill2_(.data = NULL, taxon, count)

Arguments

`.data`	data in a `data.frame`, `tibble`, `data.table`, database etc.
`taxon`	name of column in `.data` containing taxa
`count`	name of column in `.data` containing counts
`a`	exponent in Hill's diversity number (R, with special cases for `a` in 0, 1, 2 (see details))

Value

numeric vector of Hill's numbers

Functions

hill_(): version suitable for calling from a function (see package lazyeval).
hill0(): $N_0$
hill0_(): $N_0$ , version suitable for calling from a function (see package lazyeval).
hill1(): $N_1$
hill1_(): $N_1$ , version suitable for calling from a function (see package lazyeval).
hill2(): $N_2$
hill2_(): $N_2$ , version suitable for calling from a function (see package lazyeval).

References

Hill, M.O., 1973. Diversity and Evenness: A Unifying Notation and Its Consequences. Ecology 54:427-432

Examples

     hill(
         taxon = c("Euspira pulchella", "Nephtys cirrosa"),  
         count = c(6, 12),
         a = 0
     )
     hill0(
         taxon = c("Euspira pulchella", "Nephtys cirrosa"),  
         count = c(6, 12)
     )
 
hill(
         taxon = c("Euspira pulchella", "Nephtys cirrosa"),  
         count = c(6, 12),
         a = 0
     )
     hill0(
         taxon = c("Euspira pulchella", "Nephtys cirrosa"),  
         count = c(6, 12)
     )

Hurlbert's Probability of Interspecific Encounter (PIE)

Description

The probability that two individuals selected at random (without replacement) from a sample will belong to different species is given by (Hurlbert, 1971, p.579, Eq. 3):

$\Delta_1 = \sum_{i=1}^S (\frac{N_i}{N})(\frac{N-N_i}{N-1}) = (\frac{N}{N-1})\Delta_2$

where $\Delta_2$ (Hurlbert, 1971, p.579, Eq. 4) is the probability that two individuals selected at random (with replacement) from a sample will belong to different species:

$\Delta_2 = 1 - \sum_{i=1}^S \pi_i^2$

where $N_i$ is the number of individuals of the ith species in the community, $N$ is the total number of individuals in the community, $\pi_i = N_i/N$ , and $S$ is the number of species in the community. Note that Hurlbert's PIE hpie is the complement of simpson.

Usage

hpie(.data = NULL, taxon, count)

hpie_(.data = NULL, taxon, count)
hpie(.data = NULL, taxon, count)

hpie_(.data = NULL, taxon, count)

Arguments

`.data`	data in a `data.frame`, `tibble`, `data.table`, database etc.
`taxon`	name of column in `.data` containing taxa
`count`	name of column in `.data` containing counts

Value

A numeric vector with the probability of interspecific encounter (PIE).

Functions

hpie_(): suitable for calling from a function (see package lazyeval).

References

Hurlbert, S.H., 1971. The Nonconcept of Species Diversity: A Critique and Alternative Parameters. Ecology 52:577-586.

Examples

     hpie(
         taxon = c("Euspira pulchella", "Nephtys cirrosa"), 
         count = c(6, 12)
     )
 
hpie(
         taxon = c("Euspira pulchella", "Nephtys cirrosa"), 
         count = c(6, 12)
     )

Hurlbert's Expected Number of Species

Description

The expected number of species in a sample of n individuals:

Usage

hurlbert(.data = NULL, taxon, count, n = 100L)

hurlbert_(.data = NULL, taxon, count, n = 100L)
hurlbert(.data = NULL, taxon, count, n = 100L)

hurlbert_(.data = NULL, taxon, count, n = 100L)

Arguments

`.data`	data in a `data.frame`, `tibble`, `data.table`, database etc.
`taxon`	name of column in `.data` containing taxa
`count`	name of column in `.data` containing counts
`n`	number of individuals in a standard sample

Value

expected number of species in a sample of n individuals

Functions

hurlbert_(): version suitable for calling from a function (see package lazyeval).

References

Hurlbert, S.H., 1971. The Nonconcept of Species Diversity: A Critique and Alternative Parameters. Ecology 52:577-586.

Examples

     hurlbert(
         taxon = c("Euspira pulchella", "Nephtys cirrosa"), 
         count = c(4, 6),
         n = 8
     )
     
hurlbert(
         taxon = c("Euspira pulchella", "Nephtys cirrosa"), 
         count = c(4, 6),
         n = 8
     )

Test for Azoic Samples

Description

Case-insensitive test for taxa starting with 'azoi'

Usage

is_azoic(x)
is_azoic(x)

Arguments

`x`	character vector containing taxa

Details

Azoic samples need special attention during data analysis. They should be marked as 'azoic', and taken care of during analysis. Note that an azoic sample is not the same as a record where a taxon has zero counts. The latter should be removed from further analysis, whereas the former provides important information.

Value

logical vector, with elements TRUE for azoic samples, and FALSE otherwise.

Binomial Names `is_binomial` tests for valid binomial names, `generic_name` extracts the genus to which the species belongs, `specific_name` extracts the species within the genus.

Description

Binomial Names

is_binomial tests for valid binomial names, generic_name extracts the genus to which the species belongs, specific_name extracts the species within the genus.

Usage

is_binomen(x)

generic_name(x)

specific_name(x)

strip_sp(x)
is_binomen(x)

generic_name(x)

specific_name(x)

strip_sp(x)

Arguments

`x`	`character` vector, containing the binomial name(s) of species (a.k.a. binomen or scientific name)

Value

character vector with either the generic name or the specific name of the species.

Functions

generic_name(): extracts the genus to which the species belongs
specific_name(): extracts the species within the genus
strip_sp(): strips postfix sp. or spp. from a binomen

Examples

 is_binomen("Venerupis corrugata") # TRUE
 generic_name("Venerupis corrugata") # Venerupis
 specific_name("Venerupis corrugata") # corrugata
 generic_name("venerupis corrugata") # NA (genus part should be capitalized)

is_binomen("Venerupis corrugata") # TRUE
 generic_name("Venerupis corrugata") # Venerupis
 specific_name("Venerupis corrugata") # corrugata
 generic_name("venerupis corrugata") # NA (genus part should be capitalized)

Infaunal Trophic Index (ITI)

Description

Computes the Infaunal Trophic Index (ITI) according to Gittenberger & van Loon (2013).

Usage

iti(.data = NULL, taxon, count, group = NULL)

iti_(.data = NULL, taxon, count, group = NULL)

has_iti(.data = NULL, taxon, group = NULL)

has_iti_(.data = NULL, taxon, group = NULL)
iti(.data = NULL, taxon, count, group = NULL)

iti_(.data = NULL, taxon, count, group = NULL)

has_iti(.data = NULL, taxon, group = NULL)

has_iti_(.data = NULL, taxon, group = NULL)

Arguments

`.data`	data in a `data.frame`, `tibble`, `data.table`, database etc.
`taxon`	species names
`count`	counts of individuals (`numeric`)
`group`	sensitivity groups I, II, III, or IV

Details

The Infaunal Trophic Index (ITI) is given by

$\mathrm{ITI} = 100 \sum_{i=1}^3 \frac{(4-i)}{3} p_i$

where $p_i$ is the proportion of species in class $i$ , where

group I are suspension feeders (highest quality);
group II are interface feeders
group III are surface deposit feeders and
group IV are subsurface deposit feeders (lowest quality).

Value

numeric vector of length 1 containing the ITI

Functions

iti_(): version suitable for calling from a function (see package lazyeval).
has_iti(): tests if an ITI sensitivity group is available for taxon (returns TRUE (available) or FALSE (unavailable))
has_iti_(): version suitable for calling from a function (see package lazyeval).

References

Gittenberger A. and W. van Loon, 2013. Sensitivities of marine macrozoobenthos to environmental pressures in the Netherlands. Nederlandse Faunistische Mededelingen 41: 79-112.

Examples

     iti(taxon = c("Euspira pulchella", "Nephtys cirrosa"), count = c(4, 6))
     
 
   data(oosterschelde)
   has_iti(oosterschelde, TAXON)
 
iti(taxon = c("Euspira pulchella", "Nephtys cirrosa"), count = c(4, 6))
     
 
   data(oosterschelde)
   has_iti(oosterschelde, TAXON)

Margalef Index of Diversity

Description

Margalef Index of Diversity is given by

$D = \frac{S-1}{\ln(N)}$

For $N=1$ , the index is set to 0.

Usage

margalef(.data = NULL, taxon, count)

margalef_(.data = NULL, taxon, count)
margalef(.data = NULL, taxon, count)

margalef_(.data = NULL, taxon, count)

Arguments

`.data`	data in a `data.frame`, `tibble`, `data.table`, database etc.
`taxon`	taxa names (`character`)
`count`	counts (`numeric`)

Value

Margalef diversity index (numeric vector of length 1)

Functions

margalef_(): version suitable for calling from a function (see package lazyeval).

Examples

margalef(
    taxon = c("Euspira pulchella", "Nephtys cirrosa"), 
    count = c(4, 6)
)
 
margalef(
    taxon = c("Euspira pulchella", "Nephtys cirrosa"), 
    count = c(4, 6)
)

MWTL North Sea Bentos Data

Description

MWTL North Sea Bentos Data

Usage

northsea
northsea

Format

An object of class tbl_df (inherits from tbl, data.frame) with 24983 rows and 9 columns.

Oosterschelde Marine Benthos Data

Description

Oosterschelde data set. The Oosterschelde is located in the southwest of the Netherlands.

Usage

oosterschelde
oosterschelde

Format

An object of class tbl_df (inherits from tbl, data.frame) with 4269 rows and 8 columns.

Details

The Oosterschelde data contains the following columns:

ID sample identifier
HABITAT specification of the habitat
AREA sampled area
DATE sampling date (YYYY-MM-DD, ISO 8601)
TAXON standardized taxon code (see WoRMS-website https://www.marinespecies.org/)
COUNT number of individuals of 'TAXON'

Note

This is not the original data set, but a simplified version of it meant for didactic purposes only! For instance it only contains taxa identified at the species level. Other taxa have been removed.

Source

Rijkswaterstaat Water, Transport and Living Environment, Department of Information Management, Lelystad, The Netherland (contact: [email protected])

Pooling

Description

This function randomly assigns samples to pools of approximately equal area

Usage

pool(sample_id = 1:length(area), area, target_area, max_try = 100L)

.pool(sample_id = 1:length(area), area, target_area, max_try = 100L)
pool(sample_id = 1:length(area), area, target_area, max_try = 100L)

.pool(sample_id = 1:length(area), area, target_area, max_try = 100L)

Arguments

`sample_id`	sample identifier
`area`	sampling area of `sample_id` (in the same units as `target_area`)
`target_area`	vector of length 2 containing the lower and upper bound of the pooled area (same units as `area`)
`max_try`	maximum number of unsuccessful pooling tries before the algorithm gives up.

Value

vector with idenitifiers (integers) indicating the pool to which each sample belongs (NA for samples that could not be pooled)

Functions

.pool(): internal function not supposed to be called directly.

Read and Validate AMBI Sensitivity Data

Description

This function reads and checks files with AMBI sensitivity data. The data should be stored in 'comma separated values' format (csv) consisting of two columns:

TAXON species name;
GROUP Roman numeral (I, II, III, IV, V) giving the sensitivity group

Usage

read_ambi(filename)

validate_ambi(.data)
read_ambi(filename)

validate_ambi(.data)

Arguments

`filename`	name of the AMBI sensitivity file (character)
`.data`	table in AMBI-format

Details

The function performs the following tasks:

checks the existence of filename;
checks availablitity of required columns (case insensitive);
removes redundant spaces;
removes duplicated records.

Functions

validate_ambi(): validator for AMBI-format

References

Read and Validate BEQI2 Input Files

Description

This function reads and checks BEQI2 input files. The format has been specified in Van Loon (2013) and is described in the vignette of the BENMMI-package.

Usage

read_beqi2(filename)

validate_beqi2(.data)
read_beqi2(filename)

validate_beqi2(.data)

Arguments

`filename`	name of BEQI2 input file (`character`)
`.data`	table in BEQI2-format

Details

The function performs the following tasks:

checks the existence of filename;
checks availablitity of required columns (case insensitive);
make column names with aggregation data case-insensitive;
removes redundant spaces;
checks if DATE-field adheres to ISO 8601 (YYYY-mm-dd);
constructs a unique identifier ID by concatenating columns OBJECTID and DATE;
checks that each ID has a unique AREA;
checks azoic samples for VALUE=0;
removes records with VALUE=0, not belonging to azoic samples;
checks VALUE-field on missing values;
checks if VALUE-field is an integer;

Functions

validate_beqi2(): validator for BEQI2-format

References

Willem van Loon, 2013. BEQI2 INPUT FORMAT. See the package-vignette of the BENMMI-package.

Read and Validate Infaunal Trophic Index Files

Description

This function reads and checks files containing Infaunal Trophic Index (ITI) data (Gittenberger & Van Loon, 2013)

Usage

read_iti(filename)

validate_iti(.data)
read_iti(filename)

validate_iti(.data)

Arguments

`filename`	name of the ITI file (character).
`.data`	table in ITI-format

Details

The function performs the following tasks:

checks the existence of filename;
checks availablitity of required columns (case insensitive), i.e., TAXON and GROUP;
removes redundant spaces;
removes duplicated records.
checks if all ITI classes are I, II, III, or IV

The column 'GROUP' contains the Roman numerals I, II, III, and IV, with the following meaning:

I: suspension feeders;
II: interface feeders;
III: surface deposit feeders;
IV: subsurface deposit feeders.

Value

A data frame with columns TAXON containing taxa and GROUP containing user-defined ITI-groups (see Gittenberger & Van Loon, 2013).

Functions

validate_iti(): validator for ITI-format

References

Gittenberger A. and W. van Loon, 2013. Sensitivities of marine macrozoobenthos to environmental pressures in the Netherlands. Nederlandse Faunistische Mededelingen 41: 79-112.

Read and Validate Habitat References Files

Description

This function reads and checks files with reference values

Usage

read_ref(filename, indicators = c("S", "H", "AMBI"))

validate_ref(.data, indicators = c("S", "H", "AMBI"))
read_ref(filename, indicators = c("S", "H", "AMBI"))

validate_ref(.data, indicators = c("S", "H", "AMBI"))

Arguments

`filename`	name of the habitat reference file (`character`)
`indicators`	indicators to be processed (`character`, see details)
`.data`	table in REF-format

Details

The function performs the following tasks:

checks the existence of filename;
checks availablitity of required columns (case insensitive);
removes redundant spaces
removes duplicated records

Argument indicators is a character vector of additional benthic indicators to be checked for. For example, if indicators = "ITI", then the habitat reference file should also contain columns ITIREF and ITIBAD. Implemented indicators are N, LNN, S, D, SN, SNA, H, L, AMBI, ITI, PIE, N2 (see package vignette).

The format of the habitat reference file is documented in the BEQI2-package vignette.

Functions

validate_ref(): validator for REF-format

References

Van Loon, W, 2013. Loon2013-BEQI2-Specs-Ecotopes-27nov.doc

Read and Validate Taxa Data

Description

This function reads files in the taxa format.

Usage

read_taxa(filename)

get_taxa()

validate_taxa(.data)
read_taxa(filename)

get_taxa()

validate_taxa(.data)

Arguments

`filename`	name of taxa file
`.data`	table in taxa-format

Details

Taxa files have the following format:

group taxonomic group
provided provided taxon name
accepted accepted taxon name
level taxonomic level

Other columns are allowed, but silently ingored.

Functions

get_taxa(): get default taxa list (TWN list extended with species Southern North Sea)
validate_taxa(): validator for taxa-format

Read and Validate Taxa Waterbeheer Nederland (TWN) Data

Description

This function reads files in the Taxa Waterbeheer Nederland (TWN) format.

Usage

read_twn(filename)

get_worms()

validate_twn(.data)
read_twn(filename)

get_worms()

validate_twn(.data)

Arguments

`filename`	name of TWN file (`character`)
`.data`	table in TWN-format

Details

The function adds a new column taxon. Its contents depending on TWN-status:

status = 10 taxonname
status = 20 prefername
status = 80 parentname

Value

a tibble with four columns:

GROUP TWN/WoRMS taxon group
LEVEL TWN/WoRMS taxon level
FROM taxon name to convert from
TO taxon name to convert to

Functions

get_worms(): get default WoRMS list (TWN list extended with species Southern North Sea)
validate_twn(): validator for TWN-format

References

https://taxainfo.nl/

Rygg's Index of Diversity

Description

Rygg's index of diversity is given by

$SN = \frac{\ln(S)}{\ln(\ln(N))}$

The adjusted version of Rygg's index which gives more consistent values for smaller S=2, N=2, N=3 and S=3, N=3 is

$SN = \frac{\ln(S)}{\ln(\ln(N+1)+1)}$

Usage

rygg(.data = NULL, taxon, count, adjusted = FALSE)

rygg_(.data = NULL, taxon, count, adjusted = FALSE)
rygg(.data = NULL, taxon, count, adjusted = FALSE)

rygg_(.data = NULL, taxon, count, adjusted = FALSE)

Arguments

`.data`	data in a `data.frame`, `tibble`, `data.table`, database etc.
`taxon`	taxa names (`character`)
`count`	counts (`numeric`)
`adjusted`	(defaults to `FALSE`)

Value

Rygg's index of diversity (numeric vector of length 1)

Functions

rygg_(): version suitable for calling from a function (see package lazyeval).

Note

Rygg's index is not defined for $N=exp(1)$ . For $N \leq exp(1)$ , rygg returns NA_real_.

References

Rygg, B. (2006). Developing indices for quality-status classification of marine soft-bottom fauna in Norway. Norwegian Institute for Water Research, Oslo, Norway. NIVA Report SNO 5208-2006.

Examples

 rygg(
     taxon = c("Euspira pulchella", "Nephtys cirrosa"), 
     count = c(4, 6)
 )
 
rygg(
     taxon = c("Euspira pulchella", "Nephtys cirrosa"), 
     count = c(4, 6)
 )

Shannon's Index or Entropy

Description

Compute entropy according to Shannon (1948)

Usage

shannon(.data = NULL, taxon, count, base = 2)

shannon_(.data = NULL, taxon, count, base = 2)
shannon(.data = NULL, taxon, count, base = 2)

shannon_(.data = NULL, taxon, count, base = 2)

Arguments

`.data`	data in a `data.frame`, `tibble`, `data.table`, database etc.
`taxon`	taxa names (`character`)
`count`	counts (`numeric`)
`base`	the base with respect to which logarithms are computed. Defaults to 2 (unit: bits).

Value

Shannon's entropy

Functions

shannon_(): version suitable for calling from a function (see package lazyeval).

References

Shannon, C. E., 1948. A Mathematical Theory of Communication. Bell System Technical Journal 27: 379-423.

Examples

 shannon(
     taxon = c("Euspira pulchella", "Nephtys cirrosa"), 
     count = c(4, 6)
 )

shannon(
     taxon = c("Euspira pulchella", "Nephtys cirrosa"), 
     count = c(4, 6)
 )

Simpson's Measure of Concentration

Description

The probability that two inidividuals selected at random (with replacement, Hurlbert, 1971, p.579) from a sample will belong to the same species. For an infinite sample Simpson's Index is given by (Peet, 1974):

$\lambda = \sum_{i=1}^S p_i^2$

For a finite sample by:

$L = \sum_{i=1}^S \frac{n_i (n_i-1)}{N (N-1)}$

where $p_i$ the proportion of the individuals in species $i$ , $n_i$ the number of individuals in species $i$ (relative abundance), and $N$ the total number of individuals (total_abundance). The finite sample case has been implemented in function simpson (and simpson_).

Usage

simpson(.data = NULL, taxon, count)

simpson_(.data = NULL, taxon, count)
simpson(.data = NULL, taxon, count)

simpson_(.data = NULL, taxon, count)

Arguments

`.data`	data in a `data.frame`, `tibble`, `data.table`, database etc.
`taxon`	name of column in `.data` containing taxa
`count`	name of column in `.data` containing counts

Value

The probability that two inidividuals selected at random from a sample will belong to the same species.

Functions

simpson_(): version suitable for calling from a function (see package lazyeval).

References

Peet, R. K. 1974, The Measurement of Species Diversity. Annual Review of Ecology and Systematics 5:285-307.

Examples

     simpson(
         taxon = c("Euspira pulchella", "Nephtys cirrosa"),  
         count = c(6, 12)
     )
 
simpson(
         taxon = c("Euspira pulchella", "Nephtys cirrosa"),  
         count = c(6, 12)
     )

Species Richness

Description

Species richness ( $S$ ) is defined as the number of taxa (lowest identification level possible) per sampling unit (data pool or box core sample).

Usage

species_richness(.data = NULL, taxon, count = NULL)

species_richness_(.data = NULL, taxon, count = NULL)
species_richness(.data = NULL, taxon, count = NULL)

species_richness_(.data = NULL, taxon, count = NULL)

Arguments

`.data`	data in a `data.frame`, `tibble`, `data.table`, database etc.
`taxon`	taxa names (`character`)
`count`	number of individuals for each taxon (`numeric`)

Value

species richness (integer vector of length 1)

Functions

species_richness_(): version suitable for calling from a function (see package lazyeval).

Examples

 species_richness(
     taxon = c("Euspira pulchella", "Nephtys cirrosa"), 
     count = c(4, 6)
 )
 
species_richness(
     taxon = c("Euspira pulchella", "Nephtys cirrosa"), 
     count = c(4, 6)
 )

Remove Redundant Spaces

Description

This function removes redundant spaces from character vectors

Usage

strip_spaces(x)
strip_spaces(x)

Arguments

`x`	character vector

Value

character vector without trailing or multiple spaces

Convert Taxon Names to Comply with WoRMS

Description

Usage

to_worms(taxon, worms = NULL)

is_worms(.data = NULL, taxon)

is_worms_(.data, taxon)

is_accepted(.data = NULL, taxon)

is_accepted_(.data, taxon)
to_worms(taxon, worms = NULL)

is_worms(.data = NULL, taxon)

is_worms_(.data, taxon)

is_accepted(.data = NULL, taxon)

is_accepted_(.data, taxon)

Arguments

`taxon`	`character` vector, containing taxon names
`worms`	an optional table usually created with `read_twn`.
`.data`	data in a `data.frame`, `tibble`, `data.table`, database etc.

Value

character vector with WoRMS compliant species names

TRUE for WoRMS compliant species names, FALSE otherwise.

TRUE for WoRMS/TWN compliant species names, FALSE otherwise.

Functions

is_worms(): check if a taxon complies with WoRMS
is_worms_(): as is_worms but suitable for calling from a function (see package lazyeval).
is_accepted(): check if a taxon complies with WoRMS/TWN
is_accepted_(): as is_accepted but suitable for calling from a function (see package lazyeval).

References

https://www.marinespecies.org/

https://taxainfo.nl/

Total Abundance

Description

The total number of individuals.

Usage

total_abundance(.data = NULL, count, na.rm = FALSE)

total_abundance_(.data = NULL, count, na.rm = FALSE)

lnn(.data = NULL, count, na.rm = FALSE)

lnn_(.data = NULL, count, na.rm = FALSE)
total_abundance(.data = NULL, count, na.rm = FALSE)

total_abundance_(.data = NULL, count, na.rm = FALSE)

lnn(.data = NULL, count, na.rm = FALSE)

lnn_(.data = NULL, count, na.rm = FALSE)

Arguments

`.data`	data in a `data.frame`, `tibble`, `data.table`, database etc.
`count`	counts (`numeric`)
`na.rm`	Should missing values (including `NaN`) be removed? (`logical`)

Value

total number of individuals (integer)

Functions

total_abundance_(): version suitable for calling from a function (see package lazyeval).
lnn(): natural log of total abundance + 1 (see package lazyeval).
lnn_(): version of lnn suitable for calling from a function (see package lazyeval).

Examples

 total_abundance(count = c(4, 6))
     
total_abundance(count = c(4, 6))

Package 'benthos'

Help Index

Marine Benthic Ecosystem Analysis

Description

Details

Author(s)

See Also

Abundance

Description

Usage

Arguments

Value

Functions

Note

Examples

AZTI Marine Biotic Index (AMBI)

Description

Usage

Arguments

Details

Value

Functions

References

Examples

Convert Taxon Names to Comply with WoRMS/TWN

Description

Usage

Arguments

Value

References

Bray-Curtis Dissimilarity

Description

Usage

Arguments

Value

Note

Examples

Ecological Quality Ratio (EQR)

Description

Usage

Arguments

Value

Genus to Species Conversion

Description

Usage

Arguments

Value

Note

Examples

Get Supplementary AMBI Sensitivity Groups

Description

Usage

Arguments

Value

References

Get Infaunal Trophic Index

Description

Usage

Value

References

Harmonize Case

Description

Usage

Arguments

Value

Examples

Hill's Diversity Numbers

Description

Usage

Arguments

Value

Functions

References

See Also

Examples

Hurlbert's Probability of Interspecific Encounter (PIE)

Description

Usage

Arguments

Value

Binomial Names `is_binomial` tests for valid binomial names, `generic_name` extracts the genus to which the species belongs, `specific_name` extracts the species within the genus.