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Title: Automated and Controlled Extraction, Cleaning, and Processing of Occurrence Data for Generating Biogeographic Ranges of Marine Organisms
Version: 1.0.1
Description: Provides step-by-step automation for integrating biodiversity data from multiple online aggregators, merging and cleaning datasets while addressing challenges such as taxonomic inconsistencies, georeferencing issues, and spatial or environmental outliers. Includes functions to extract environmental data and to define the biogeographic ranges in which species are most likely to occur.
License: MIT + file LICENSE
Encoding: UTF-8
RoxygenNote: 7.3.3
Suggests: knitr, rgbif, robis, ridigbio, rmarkdown, testthat (≥ 3.0.0)
Config/testthat/edition: 3
Imports: dplyr, geodata, geosphere, ggplot2, mregions2, patchwork, rlang, sdmpredictors, sf, taxize, terra, tidyr
Depends: R (≥ 3.5)
LazyData: true
VignetteBuilder: knitr
Config/Needs/website: rmarkdown
NeedsCompilation: no
Packaged: 2025-11-14 18:27:10 UTC; sonip
Author: Priyanka Soni ORCID iD [aut, cre], Austin Hendy [aut], David Bottjer [aut], Vijay Barve [ctb]
Maintainer: Priyanka Soni <sonip@usc.edu>
Repository: CRAN
Date/Publication: 2025-11-19 19:30:02 UTC

Get Decimal Places of Coordinate Values

Description

Get Decimal Places of Coordinate Values

Usage

decimal_places(coord)

Arguments

coord

A coordinate value in the numeric format of decimal degree

Value

a numerical value which represent the number of decimal places for the coordiante

Examples

decimal_places(12.7000000)
decimal_places(45.67788)

Calculate geographic distance and mahalanobis distance to estimate outlier probability of a data point

Description

Calculate geographic distance and mahalanobis distance to estimate outlier probability of a data point

Usage

distance_calc(data, latitude, longitude, env_layers, itr = 15, k = 3)

Arguments

data

data table with spatial and environmental variables

latitude

nested input from ec_flag_outlier

longitude

nested input from ec_flag_outlier

env_layers

header names of env variables. env_layers <- c("Temperature", "pH")

itr

iteration to run the clustering 100 or 1000 times

k

number of cluster to choose in each iteration

Value

A list of results that shows result of calculated distance for each iteration

Examples


data <- data.frame(
  scientificName = "Mexacanthina lugubris",
  decimalLongitude = c(-117, -117.8, -116.9),
  decimalLatitude = c(32.9, 33.5, 31.9),
  temperature_mean = c(12, 13, 14),
  temperature_min = c(9, 6, 10),
  temperature_max = c(14, 16, 18)
)

env_layers <- c("temperature_mean", "temperature_min", " temperature_max")

result_list <- distance_calc(data,
  latitude = "decimalLatitude",
  longitude = "decimalLongitude",
  env_layers,
  itr = 100,
  k = 3
)

Merge the Data sets Extracted from Various datasources.

Description

condition to run this function: all the data frames should have same fields follwing DwC standards: e.g. attribute_list <- c("source","catalogNumber", "basisOfRecord", "occurrenceStatus", "institutionCode", "verbatimEventDate", "scientificName", "individualCount", "organismQuantity", "abundance", "decimalLatitude", "decimalLongitude", "coordinateUncertaintyInMeters", "locality", "verbatimLocality", "municipality", "county", "stateProvince", "country", "countryCode") Assign manually the source name in "source" field. example - gbif, obis, invertEBase etc Assign values of individual count or organism count into abundance. Most online sources has one of them updated with specimen count. this function depends on successful download of data files, it also allow to input csv files from local system

Usage

ec_db_merge(
  db_list,
  datatype = "modern",
  occurrenceStatus = "occurrenceStatus",
  basisOfRecord = "basisOfRecord"
)

Arguments

db_list

list of data frames which we want to merge. e.g. GBIF, iDigbio, InvertEBase and any local file.

datatype

default "modern". datatype accept text input as "modern" or "fossil"

occurrenceStatus

default name for occurrenceStatus column is occurrenceStatus but a different name can be inserted if required.

basisOfRecord

default name for basis of record column is basis of record but a different name can be inserted if required.

Value

A data frame of occurrence records filtered to include only those classified as "modern" or "fossil".

Examples


db1 <- data.frame(
  species = "A",
  decimalLongitude = c(-120, -117, NA, NA),
  decimalLatitude = c(20, 34, NA, NA),
  catalogNumber = c("12345", "89888", "LACM8898", "SDNHM6767"),
  occurrenceStatus = c("present", "", "ABSENT", "Present"),
  basisOfRecord = c("preserved_specimen", "", "fossilspecimen", "material_sample"),
  source = "db1",
  abundance = c(1, NA, 8, 23)
)

db2 <- data.frame(
  species = "A",
  decimalLongitude = c(-120.2, -117.1, NA, NA),
  decimalLatitude = c(20.2, 34.1, NA, NA),
  catalogNumber = c("123452", "898828", "LACM82898", "SDNHM62767"),
  occurrenceStatus = c("present", "", "ABSENT", "Present"),
  basisOfRecord = c("preserved_specimen", "", "fossilspecimen", "material_sample"),
  source = "db2",
  abundance = c(1, 2, 3, 19)
)
db_list <- list(db1, db2)
merge_modern_data <- ec_db_merge(db_list = db_list, "modern")

Extract the Environmental data

Description

Extract the Environmental data

Usage

ec_extract_env_layers(
  data,
  env_layers = env_layers,
  latitude = "decimalLatitude",
  longitude = "decimalLongitude"
)

Arguments

data

data table which has coordinate information

env_layers

make a list of enviornmental layers which need to be extracted, example :BO_sstmean, BO_sstmax, BO_sstmin, BO_chomean, BO_phosphate or marspec layer, must check list_layer to know exact name of the layer code.

latitude

default assigned as "decimalLatitude"

longitude

default assigned as "decimalLongitude"

Value

A data table which has unique coordinates and env predictors

Examples

env_layers <- c("BO_sstmean", "BO_chlomean", "BO_dissox", "BO_salinity")
data <- data.frame(
  scientificName = "Mexacanthina lugubris",
  decimalLongitude = c(-117, -117.8, -116.9),
  decimalLatitude = c(32.9, 33.5, 31.9)
)

data_x <- ec_extract_env_layers(data,
  env_layers = env_layers,
  latitude = "decimalLatitude",
  longitude = "decimalLongitude"
)

Flag the Occurrences those has Extreme Uncertainty Error Radius

Description

Flag the Occurrences those has Extreme Uncertainty Error Radius

Usage

ec_filter_by_uncertainty(
  data,
  uncertainty_col = "coordinateUncertaintyInMeters",
  percentile = 0.96,
  ask = TRUE,
  latitude = "decimalLatitude",
  longitude = "decimalLongitude"
)

Arguments

data

data table which need to be cleaned with unwanted uncertainty values - extreme values

uncertainty_col

coordinateUncertaintyInMeters column

percentile

to derive threshold, e.g. extreme 5% uncertainty data points to be removed. give percentile value as 0.95

ask

this allow user to decide if the uncertainty threshold value is okay or too high/low

latitude

default set on decimalLatitude, this column is use to filter records those does not have georeferences.

longitude

default set on decimalLongitude.

Value

A data frame as result of removing extreme uncertain occurrences

Examples

data <- data.frame(
  species = "A",
  decimalLongitude = c(-120, -117, NA, NA),
  decimalLatitude = c(20, 34, NA, NA),
  cleaned_catalog = c("12345", "89888", "LACM8898", "SDNHM6767"),
  locality = c(NA, NA, "Los Angeles, CA", "San Pedro, CA"),
  coordinateUncertaintyInMeters = c(1000, 2000, 9999900, NA)
)
data <- ec_filter_by_uncertainty(
  data,
  uncertainty_col = "coordinateUncertaintyInMeters",
  latitude = "decimalLatitude",
  longitude = "decimalLongitude",
  percentile = 0.96,
  ask = TRUE
)

Flag the occurrences those are not in east Atlantic and are inland

Description

Flag the occurrences those are not in east Atlantic and are inland

Usage

ec_flag_non_east_atlantic(
  ocean_names,
  buffer_distance = 50000,
  data,
  latitude = "decimalLatitude",
  longitude = "decimalLongitude"
)

Arguments

ocean_names

Insert the name of oceans: "South Pacific Ocean", "North Pacific Ocean", North Atlantic Ocean", "South Atlantic Ocean"

buffer_distance

Its a certain buffer distance to consider if a data point is inland. Beyond this distance data points consider as bad data points. e.g. buffer_distance <- 25000

data

Data table which has latitude and longitude information

latitude

default set to "decimalLatitude"

longitude

default set to "decimalLongitude"

Value

A new column with flagged values, 1 means bad records 0 means good record. Column name: flag_non_region

Examples


ocean_names <- c("North Atlantic Ocean", "South Atlantic Ocean")
buffer_distance <- 25000
data <- data.frame(
  species = "A",
  decimalLongitude = c(-120, -78, -110, -60, -75, -130, -10, 5),
  decimalLatitude = c(20, 34, 30, 10, 40, 25, 15, 35)
)
data$flag_non_region <- ec_flag_non_east_atlantic(
  ocean_names,
  buffer_distance,
  data,
  latitude = "decimalLatitude",
  longitude = "decimalLongitude"
)

Flag occurrences those are not in east Pacific and are inland

Description

Flag occurrences those are not in east Pacific and are inland

Usage

ec_flag_non_east_pacific(
  ocean_names,
  buffer_distance = 50000,
  data,
  latitude = "decimalLatitude",
  longitude = "decimalLongitude"
)

Arguments

ocean_names

Insert the name of oceans: "South Pacific Ocean", "North Pacific Ocean", North Atlantic Ocean", "South Atlantic Ocean"

buffer_distance

Its a certain buffer distance to consider if a data point is inland. Beyond this distance data points consider as bad data points. e.g. buffer_distance <- 25000

data

Data table which has latitude and longitude information

latitude

default set to "decimalLatitude"

longitude

default set to "decimalLongitude"

Value

A new column with flagged values, 1 means bad records 0 means good record. Column name: flag_non_region

Examples


ocean_names <- c("North Pacific Ocean", "South Pacific Ocean")
buffer_distance <- 25000
data <- data.frame(
  species = "A",
  decimalLongitude = c(-120, -78, -110),
  decimalLatitude = c(20, 34, 30)
)
data$flag_non_region <- ec_flag_non_east_pacific(
  ocean_names,
  buffer_distance,
  data,
  latitude = "decimalLatitude",
  longitude = "decimalLongitude"
)

Flag Occurrences those are in wrong ocean basins and are inland

Description

Flag Occurrences those are in wrong ocean basins and are inland

Usage

ec_flag_non_region(
  direction,
  ocean,
  buffer = 50000,
  data,
  latitude = "decimalLatitude",
  longitude = "decimalLongitude"
)

Arguments

direction

values as "east" or "west". These values help to filter the shape files for east or west of select ocean (e.g. pacific) for both north and south hemisphere.

ocean

values such as "pacific" or "atlantic"

buffer

Its a certain buffer distance to consider if a data point is inland. Beyond this distance data points consider as bad data points. e.g. buffer <- 25000

data

Data table which has latitude and longitude information

latitude

default set to "decimalLatitude"

longitude

default set to "decimalLongitude"

Value

A new column with flagged values, 1 means bad records 0 means good record. Column name: flag_non_region

Examples


direction <- "east"
buffer <- 25000
ocean <- "pacific"
data <- data.frame(
  species = "A",
  decimalLongitude = c(-120, -78, -110, -60, -75, -130, -10, 5),
  decimalLatitude = c(20, 34, 30, 10, 40, 25, 15, 35)
)
data$flag_non_region <- ec_flag_non_region(
  direction,
  ocean,
  buffer = 50000,
  data
)

Flag Occurrences those are not in west Atlantic and are inland

Description

Flag Occurrences those are not in west Atlantic and are inland

Usage

ec_flag_non_west_atlantic(
  ocean_names,
  buffer_distance = 50000,
  data,
  latitude = "decimalLatitude",
  longitude = "decimalLongitude"
)

Arguments

ocean_names

Insert the name of oceans: "South Pacific Ocean", "North Pacific Ocean", North Atlantic Ocean", "South Atlantic Ocean"

buffer_distance

Its a certain buffer distance to consider if a data point is inland. Beyond this distance data points consider as bad data points. e.g. buffer_distance <- 25000

data

Data table which has latitude and longitude information

latitude

default set to "decimalLatitude"

longitude

default set to "decimalLongitude"

Value

A new column with flagged values, 1 means bad records 0 means good record. Column name: flag_non_region

Examples


ocean_names <- c("North Atlantic Ocean", "South Atlantic Ocean")
buffer_distance <- 25000
data <- data.frame(
  species = "A",
  decimalLongitude = c(-120, -78, -110, -60, -75, -130, -10, 5),
  decimalLatitude = c(20, 34, 30, 10, 40, 25, 15, 35)
)
data$flag_non_region <- ec_flag_non_west_atlantic(
  ocean_names,
  buffer_distance,
  data,
  latitude = "decimalLatitude",
  longitude = "decimalLongitude"
)

Flag occurrences those are not in east Pacific and are inland

Description

Flag occurrences those are not in east Pacific and are inland

Usage

ec_flag_non_west_pacific(
  ocean_names,
  buffer_distance = 50000,
  data,
  latitude = "decimalLatitude",
  longitude = "decimalLongitude"
)

Arguments

ocean_names

Insert the name of oceans: "South Pacific Ocean", "North Pacific Ocean", North Atlantic Ocean", "South Atlantic Ocean"

buffer_distance

Its a certain buffer distance to consider if a data point is inland. Beyond this distance data points consider as bad data points. e.g. buffer_distance <- 25000

data

Data table which has latitude and longitude information

latitude

default set to "decimalLatitude"

longitude

default set to "decimalLongitude"

Value

A new column with flagged values, 1 means bad records 0 means good record. Column name: flag_non_region

Examples


ocean_names <- c("North Pacific Ocean", "South Pacific Ocean")
buffer_distance <- 25000
data <- data.frame(
  species = "A",
  decimalLongitude = c(-120, -78, -110),
  decimalLatitude = c(20, 34, 30)
)
data$flag_non_region <- ec_flag_non_west_pacific(
  ocean_names,
  buffer_distance,
  data,
  latitude = "decimalLatitude",
  longitude = "decimalLongitude"
)

Flag Outlier Occurrences - using Spatial and Non-spatial Attributes

Description

Flag Outlier Occurrences - using Spatial and Non-spatial Attributes

Usage

ec_flag_outlier(
  data,
  latitude = "decimalLatitude",
  longitude = "decimalLongitude",
  env_layers,
  itr = 50,
  k = 3,
  geo_quantile = 0.99,
  maha_quantile = 0.99
)

Arguments

data

data table with spatial and environmental variables

latitude

default set to "deciamlLatitude"

longitude

default set to "decimalLongitude"

env_layers

header names of env variables. env_layers <- c("Temperature", "pH")

itr

iteration to run the clustering 100 or 1000 times

k

number of cluster to choose in each iteration

geo_quantile

value with geo_quantile percentile would consider has threshold for geo_distance to derive the outlier. e.g. default 0.99

maha_quantile

value with maha_quantile percentile would consider has threshold for maha_distance to derive the outlier. e.g. default 0.99

Value

A column call flag_outlier which has outlier probability from 0 to 1. 1 is more towards outlier, 0 more towards good data points.

Examples

data <- data.frame(
  scientificName = "Mexacanthina lugubris",
  decimalLongitude = c(-117, -117.8, -116.9),
  decimalLatitude = c(32.9, 33.5, 31.9),
  BO_sstmean = c(12, 13, 14),
  BO_sstmin = c(9, 6, 10),
  BO_sstmax = c(14, 16, 18)
)

env_layers <- c("BO_sstmean", "BO_sstmin", "BO_sstmax")
res <- ec_flag_outlier(data,
  latitude = "decimalLatitude",
  longitude = "decimalLongitude",
  env_layers,
  itr = 100,
  k = 3,
  geo_quantile = 0.99,
  maha_quantile = 0.99
)
data$outlier <- res$outlier
iteration_list <- res$result$list

Flag occurrences those has bad precision

Description

Flag occurrences those has bad precision

Usage

ec_flag_precision(
  data,
  latitude = "decimalLatitude",
  longitude = "decimalLongitude",
  threshold = 2
)

Arguments

data

dataframe

latitude

decimalLatitude, this a field in the data file. We prefer to use decimalLatitude as accepeted name based on TDWG standards

longitude

decimalLongitude, this a field in the data file. We prefer to use decimalLongitude as accepeted name based on TDWG standards

threshold

set on 2

Value

A column which has flagged records represents bad records based on low precision as well as rounding

Examples

data <- data.frame(
  species = "A",
  decimalLongitude = c(-120.67, -78, -110, -60, -75.5, -130.78, -10.2, 5.4),
  decimalLatitude = c(20.7, 34.6, 30.0, 10.5, 40.4, 25.66, 15.0, 35.9)
)

data$flag_cordinate_precision <- ec_flag_precision(
  data,
  latitude = "decimalLongitude",
  longitude = "decimalLatitude",
  threshold = 2
)

Filter records to georeference using GEOLocate

Description

Filter records to georeference using GEOLocate

Usage

ec_flag_with_locality(
  data,
  uncertainty = "coordinateUncertaintyInMeters",
  locality = "locality",
  verbatimLocality = "verbatimLocality"
)

Arguments

data

data table with occurrence information

uncertainty

Mendatory to have coordinateUncertaintyInMeters column in the data table

locality

Mandatory to have locality column in the data table.

verbatimLocality

Mandatory to have verbatimLocality in the data table.

Details

Records those does not have coordinates assigned but has locality and varbatim locality information to assign coordinates by using external tools such as GEOLocate

Value

A column with flagged records as 1, which means these records has potential to be georeferenced.

Examples

data <- data.frame(
  coordinateUncertaintyInMeters = c(NA, "N/A", 50, "30", NA, "N/A", NA),
  locality = c("Santa Cruz", NA, "Los Angeles", "N/A", "", "San Diego", NA),
  verbatimLocality = c(NA, "CA coast", "", "N/A", "Long Beach", NA, "")
)
data$flag_check_geolocate <- ec_flag_with_locality(
data, uncertainty = "coordinateUncertaintyInMeters",
locality = "locality",
verbatimLocality = "verbatimLocality"
)

Map view of occurrence data points

Description

Map view of occurrence data points

Usage

ec_geographic_map(
  data,
  latitude = "decimalLatitude",
  longitude = "decimalLongitude"
)

Arguments

data

Data table

latitude

default set to "decimalLatitude"

longitude

default set to "decimalLongitude"

Value

A map view shows occurrence records.

Examples

data <- data.frame(
  scientificName = "Mexacanthina lugubris",
  decimalLongitude = c(-117, -117.8, -116.9),
  decimalLatitude = c(32.9, 33.5, 31.9),
  temperature_mean = c(12, 13, 14),
  temperature_min = c(9, 6, 10),
  temperature_max = c(14, 16, 18)
)
ec_geographic_map(data,
  latitude = "decimalLatitude",
  longitude = "decimalLongitude"
)

Map view to visualize data points with outlier probability 0 to 1 on a map view

Description

Map view to visualize data points with outlier probability 0 to 1 on a map view

Usage

ec_geographic_map_w_flag(
  data,
  flag_column,
  latitude = "decimalLatitude",
  longitude = "decimalLongitude"
)

Arguments

data

Data table which has information of coordinates (decimalLongitude and decimalLatitude) and a column which has flags 0 to 1

flag_column

column name which has flag, e.g. flag_outlier

latitude

default set on "decimalLatitude", change if the name of column is different.

longitude

default set on "decimalLongitude", change if the name of column is different.

Value

A geographic map which shows occurrence data points with the color gradient to show flagged records in warm color.

Examples

data <- data.frame(
  scientificName = "Mexacanthina lugubris",
  decimalLongitude = c(-117, -117.8, -116.9),
  decimalLatitude = c(32.9, 33.5, 31.9),
  temperature_mean = c(12, 13, 14),
  temperature_min = c(9, 6, 10),
  temperature_max = c(14, 16, 18),
  flag_outlier = c(0, 0.5, 1)
)
ec_geographic_map_w_flag(data,
  flag_column = "flag_outlier",
  latitude = "decimalLatitude",
  longitude = "decimalLongitude"
)

Impute Environmental Variables using Mean Values of occurrences within a certain radius

Description

Impute Environmental Variables using Mean Values of occurrences within a certain radius

Usage

ec_impute_env_values(
  data_x,
  latitude = "decimalLatitude",
  longitude = "decimalLongitude",
  radius_km = 10,
  iter = 3
)

Arguments

data_x

this is data_x which is the output of ec_extract_env_layers

latitude

default set to "decimalLatitude"

longitude

default set to "decimalLongitude"

radius_km

radius to average the values of data points within the circle to imput the values for missing datta points

iter

number of times to iterate the imputation, e.g. 1 or 2 or 3

Value

An updated table of data_x which has imputed values for the missing env variables, condition applies that the this imputation wont work if the data points are too sparse.

Examples


data_x <- data.frame(
  scientificName = "Mexacanthina lugubris",
  decimalLongitude = c(-117, -117.8, -116.9),
  decimalLatitude = c(32.9, 33.5, 31.9),
  BO_sstmean = c(12, NA, 14),
  BO_sstmin = c(9, NA, 10),
  BO_sstmax = c(14, NA, 18)
)
radius_km <- 10
iter <- 3
data_x <- ec_impute_env_values(data_x,
  latitude = "decimalLatitude",
  longitude = "decimalLongitude",
  radius_km, iter
)

Merge the Update Georeferenced Occurrence Points back to the Main Data File.

Description

Merge the Update Georeferenced Occurrence Points back to the Main Data File.

Usage

ec_merge_corrected_coordinates(
  data_corrected,
  data,
  catalog = "cleaned_catalog",
  latitude = "decimalLatitude",
  longitude = "decimalLongitude",
  uncertainty_col = "coordinateUncertaintyInMeters"
)

Arguments

data_corrected

After assigning coordinate values using online georeference tools such as GeoLocate, upload the csv file back to R with the name call data_corrected, we hardcoded the field names as "corrected_longitude", "corrected_latitude" and "corrected_uncertainty" and "cleaned_catalog" for column names of data_corrected dataset" which will be merge with "decimalLongitude", "decimalLantitude", "coordinateUncertaintyInMeters" and "cleaned_catalog" of data table.

data

data table which needs to updated with the assign coordiantes

catalog

this is an important attribute to use matching the records back to the main data file.

latitude

default set to "decimalLatitude", this is a column name of data

longitude

default set to "decimalLongitude", this is a column name of data

uncertainty_col

this is a column name of data and default set to "coordinateUncertaintyInMeters"

Value

A data frame with updated coordinate information

Examples

data <- data.frame(
  species = "A",
  decimalLongitude = c(-120, -119.8, NA, NA),
  decimalLatitude = c(20, 34, NA, NA),
  cleaned_catalog = c("12345", "89888", "LACM8898", "SDNHM6767"),
  locality = c(NA, NA, "Los Angeles, CA", "San Pedro, CA"),
  coordinateUncertaintyInMeters = c(9999, NA, NA, NA)
)
data_corrected <- data.frame(
corrected_longitude = c(-120, -119.8, 118, 118.3),
 corrected_latitude = c(20, 34, 33, 32.9),
 cleaned_catalog = c("12345", "89888", "LACM8898", "SDNHM6767"),
 corrected_uncertainty = c(9999, NA, 5000, 1000)
)


data<- ec_merge_corrected_coordinates(data_corrected, data,
catalog = "cleaned_catalog",
latitude = "decimalLatitude",
longitude = "decimalLongitude",
uncertainty_col = "coordinateUncertaintyInMeters" )

Scatter Plot between geo_distance vs maha_distance with geo- and maha- Quantile Threshold to Demonstrate the Outliers outside those threshold.

Description

Scatter Plot between geo_distance vs maha_distance with geo- and maha- Quantile Threshold to Demonstrate the Outliers outside those threshold.

Usage

ec_plot_distance(
  x,
  geo_quantile = 0.99,
  maha_quantile = 0.99,
  iterative = TRUE,
  geo_distance = "geo_distance",
  maha_distance = "maha_distance"
)

Arguments

x

iteration_list derived from ec_flag_outlier can be used to plot these scatter plots between geo_distance vs maha_distance

geo_quantile

value with geo_quantile percentile would consider has threshold for geo_distance to derive the outlier. e.g. default 0.99

maha_quantile

value with maha_quantile percentile would consider has threshold for maha_distance to derive the outlier. e.g. default 0.99

iterative

= TRUE/FALSE, default set on TRUE, which provide a iterative loop to check maps of each iteration of listed outcome of outlier probability, if it is FALSE, loop exit with first iteration outcome of outlier probability.

geo_distance

default set on "geo_distance", this column has calculated distance - output of ec_flag_outlier

maha_distance

default set on "maha_distance", this column has calculated distance - output of ec_flag_outlier

Value

A list of plots for each iteration outcome

Examples

df1 <- data.frame(
  latitude = runif(5, 30, 35),
  longitude = runif(5, -120, -115),
  temperature = rnorm(5, 15, 2),
  pH = rnorm(5, 8, 0.1),
  geo_distance = runif(5, 0, 100),
  maha_distance = runif(5, 0, 10)
)
df2 <- data.frame(
 latitude = runif(5, 30, 35),
 longitude = runif(5, -120, -115),
 temperature = rnorm(5, 16, 2),
 pH = rnorm(5, 7.9, 0.1),
 geo_distance = runif(5, 0, 100),
 maha_distance = runif(5, 0, 10)
)

iteration_list <- list(df1, df2)#Store both data frames in a list

iteration_list <- list(df1, df2)
plot <- ec_plot_distance(iteration_list, geo_quantile = 0.99, maha_quantile = 0.99,
iterative = TRUE)

Plot cleaned data overlay overall occurrence data to demonstrate accepted ranges of spatial and non-spatial attributes

Description

Plot cleaned data overlay overall occurrence data to demonstrate accepted ranges of spatial and non-spatial attributes

Usage

ec_plot_var_range(
  data,
  summary_df,
  latitude = "decimalLatitude",
  longitude = "decimalLongitude",
  env_layers
)

Arguments

data

data table which even has outlier data points

summary_df

summmary output of final cleaned data, after executing function ec_var_summary

latitude

default set to "decimalLatitude"

longitude

default set to "decimalLongitude"

env_layers

list of environmental variables

Value

A plot which shows spatial and environmental variables with the acceptable range for species habitability

Examples

data <- data.frame(
  scientificName = "Mexacanthina lugubris",
  decimalLongitude = c(-117, -117.8, -116.9, -116.5),
  decimalLatitude = c(32.9, 33.5, 31.9, 32.4),
  temperature_mean = c(12, 13, 14, 11),
  temperature_min = c(9, 6, 10, 10),
  temperature_max = c(14, 16, 18, 17),
  flag_outlier = c(0, 0.5, 1, 0.7)
) # this data table has data points which was considered as outliers

data_x <- data.frame(
  scientificName = "Mexacanthina lugubris",
  decimalLongitude = c(-117, -117.8, -116.5),
  decimalLatitude = c(32.9, 33.5, 32.4),
  temperature_mean = c(12, 13, 11),
  temperature_min = c(9, 6, 10),
  temperature_max = c(14, 16, 17),
  flag_outlier = c(0, 0.5, 0.7)
)
# cleaned data base after removing outliers >x probability.
# in this example, removed data points >0.7 probability to be
# considering outliers


env_layers <- c("temperature_mean", "temperature_min", "temperature_max")
summary_df <- ec_var_summary(data_x,
  latitude = "decimalLatitude",
  longitude = "decimalLongitude",
  env_layers
)
# this is the final cleaned data table which
# will be used to derive summary of acceptable niche

ec_plot_var_range(data,
  summary_df,
  latitude = "decimalLatitude",
  longitude = "decimalLongitude",
  env_layers
)

Remove Duplicate Records from the Merged Data

Description

Remove Duplicate Records from the Merged Data

Usage

ec_rm_duplicate(data, catalogNumber = "catalogNumber", abundance = "abundance")

Arguments

data

this is merge data frame which is a output file after running ec_db_merge

catalogNumber

this is a mandatory field which consider unique for each occurrence record.

abundance

this is a mandatory field which has created while data extraction by combining individual count and quantity fields (may vary from one source to another, we aim to standardize those as "abundance").

Details

This function will provide a cleaned_catalog column as output, which has catalog numbers standardize and removed duplicates based on generated cleaned_catalog and abundance columns of data. mandatory fields are catalogNumber, source and abundance

Value

A data frame which has unique catalog numbers. the output file will have cleaned_catalog field instead of catalogNumber. Also the unique record will be chosen with the abundance value if there is any.

Examples


db1 <- data.frame(
  species = "A",
  decimalLongitude = c(-120.2, -117.1, NA, NA),
  decimalLatitude = c(20.2, 34.1, NA, NA),
  catalogNumber = c("12345", "89888", "LACM8898", "SDNHM6767"),
  occurrenceStatus = c("present", "", "ABSENT", "Present"),
  basisOfRecord = c("preserved_specimen", "", "fossilspecimen", "material_sample"),
  source = "db1",
  abundance = c(1, NA, 8, 23)
)

db2 <- data.frame(
  species = "A",
  decimalLongitude = c(-120.2, -117.1, NA, NA),
  decimalLatitude = c(20.2, 34.1, NA, NA),
  catalogNumber = c("123452", "898828", "LACM82898", "SDNHM62767"),
  occurrenceStatus = c("present", "", "ABSENT", "Present"),
  basisOfRecord = c("preserved_specimen", "", "fossilspecimen", "material_sample"),
  source = "db2",
  abundance = c(1, 2, 3, 19)
)
db_list <- list(db1, db2)
merge_modern_data <- ec_db_merge(db_list = db_list, "modern")
ecodata <- ec_rm_duplicate(merge_modern_data,
  catalogNumber = "catalogNumber",
  abundance = "abundance"
)

Trail Zeros from the Coordinate Values

Description

Trail Zeros from the Coordinate Values

Usage

ec_trail_zero(coord)

Arguments

coord

A coordinate value in the numeric format of decimal degree

Value

A numerical trailed coordinate value.

Examples

ec_trail_zero(12.7000000)
ec_trail_zero(45.000000)

A Summary Table of Final Cleaned Spatial and Environmental Variables

Description

A Summary Table of Final Cleaned Spatial and Environmental Variables

Usage

ec_var_summary(
  data,
  latitude = "decimalLatitude",
  longitude = "decimalLongitude",
  env_layers
)

Arguments

data

data table after cleaning the records

latitude

default set to "decimalLatitude"

longitude

default set to "decimalLongitude"

env_layers

an array of col names of enviornmental layers

Value

A summary table with the mean, min and max values of final cleaned spatial and environmental variables

Examples

data <- data.frame(
  scientificName = "Mexacanthina lugubris",
  decimalLongitude = c(-117, -117.8, -116.9, -116.5),
  decimalLatitude = c(32.9, 33.5, 31.9, 32.4),
  BO_sstmean = c(12, 13, 14, 11),
  BO_sstmin = c(9, 6, 10, 10),
  BO_sstmax = c(14, 16, 18, 17)
)
env_layers <- c("BO_sstmean", "BO_sstmin", "BO_sstmax")
ec_var_summary(data,
  latitude = "decimalLatitude",
  longitude = "decimalLongitude",
  env_layers
)

Check Accepted Synonyms from WoRMs Taxonomy

Description

Check Accepted Synonyms from WoRMs Taxonomy

Usage

ec_worms_synonym(species_name, data, scientificName = "scientificName")

Arguments

species_name

input species name.e.g. Mexacanthina lugubris

data

data table which has information of all occurrence data of the selected species

scientificName

default set to scientificName, this is a column in the data extracted from online sources, may have various synonyms of species_name.

Value

A table with two columns, column one represent the accepted synonyms, and column two demonstrate the unique species names from the occurrence data base with the number of records tagged under species names.

Examples

species_name <- "Mexacanthina lugubris"
data <- data.frame(
  scientificName = "Mexacanthina lugubris",
  decimalLongitude = c(-120, -78, -110, -60, -75, -130, -10, 5),
  decimalLatitude = c(20, 34, 30, 10, 40, 25, 15, 35)
)
comparison <- ec_worms_synonym(species_name, data, scientificName = "scientificName")
print(comparison)

dataset1: Documentation of data file - ecodata.rda

Description

This data file is consider as raw data file after merging and removing duplicate records of all data sources. e.g. this file is an output of occurrence records of mollusc species "Mexacanthina lugubris" with all modern records extracted from GBIF, OBIS, IDIGBIO and InvertEBase

Usage

ecodata

Format

A data frame with 1115 rows and 19 variables:

X

index

basisOfRecord

Type of record (e.g., preserved specimen, fossil)

occurrenceStatus

Presence or absence of the organism

institutionCode

Code of the institution that holds the record

verbatimEventDate

Original recorded date of the event

scientificName

Full scientific name of the organism

individualCount

Number of individuals observed

organismQuantity

Reported quantity of the organism

abundance

Calculated or standardized abundance value

decimalLatitude

Latitude in decimal degrees

decimalLongitude

Longitude in decimal degrees

coordinateUncertaintyInMeters

Uncertainty in coordinates (meters)

locality

Named place where the occurrence was recorded

verbatimLocality

Original text for locality description

municipality

Municipality or town of the occurrence

county

County where the record was observed

stateProvince

State or province name

country

Country name

cleaned_catalog

Standardized catalog number for de-duplication

Source

dataset4: Documentation of data file - ecodata_cleaned.rda

Description

This data shows the final cleaned occurrence records

Usage

ecodata_cleaned

Format

A data frame with 698 rows and 35 variables:

X

Index

basisOfRecord

Type of occurrence record (e.g., preserved specimen, fossil)

occurrenceStatus

Indicates presence or absence of the species

institutionCode

Code of the institution that provided the record

verbatimEventDate

Original text for the event or collection date

scientificName

Scientific name of the organism

individualCount

Number of individuals recorded

organismQuantity

Reported quantity (unit may vary)

abundance

Standardized or calculated abundance value

decimalLatitude

Latitude in decimal degrees

decimalLongitude

Longitude in decimal degrees

coordinateUncertaintyInMeters

Spatial uncertainty of coordinates in meters

locality

Named location where the record was collected

verbatimLocality

Original locality text as provided by the source

municipality

Municipality or town of occurrence

county

County of occurrence

stateProvince

State or province of occurrence

country

Country of occurrence

cleaned_catalog

Standardized catalog number used for de-duplication

lat_precision

Number of decimal places in the latitude coordinate

lon_precision

Number of decimal places in the longitude coordinate

flag_cordinate_precision

Flag for low coordinate precision

flag_cc_val

Flag for invalid or impossible coordinates

flag_cc_equal

Flag for identical latitude and longitude (likely erroneous)

flag_cc_zero

Flag for coordinates at (0,0)

flag_cc_cent

Flag for coordinates placed at a country or region centroid

flag_cc_gbif

Flag for coordinates matching GBIF headquarters (artifact)

flag_cc_inst

Flag for coordinates matching institution location

flag_non_region

Flag for coordinates outside the study region

outliers

Flag for outliers based on clustering of spatial and environmental variables

BO_sstmean

Mean sea surface temperature from Bio-ORACLE

BO_sstmax

Maximum sea surface temperature from Bio-ORACLE

BO_sstmin

Minimum sea surface temperature from Bio-ORACLE

BO_chloro

Chlorophyll concentration from Bio-ORACLE

BO_dissox

Dissolved oxygen level from Bio-ORACLE

Source

Generated after filtering outlier data points

dataset2: Documentation of data file - ecodata_corrected.rda

Description

This data file created by using GEOLocate tool and we only kept 4 columns. These georeference information will be merge back with the main data file ecodata

Usage

ecodata_corrected

Format

A data frame with 433 rows and 4 variables:

cleaned_catalog

catalog number

corrected_latitude

latitude values assigned by GEOLocate

corrected_longitude

longitude values assigned by GEOLocate

corrected_uncertainty

uncertainty values assigned by GEOLocate

Source

dataset3: Documentation of data file - ecodata_with_outliers.rda

Description

This data file created after running ec_flag_outlier function. It has records with outlier probability

Usage

ecodata_with_outliers

Format

A data frame with 713 rows and 35 variables:

X

index

basisOfRecord

Type of occurrence record (e.g., preserved specimen, fossil)

occurrenceStatus

Indicates presence or absence of the species

institutionCode

Code of the institution that provided the record

verbatimEventDate

Original text for the event or collection date

scientificName

Scientific name of the organism

individualCount

Number of individuals recorded

organismQuantity

Reported quantity (unit may vary)

abundance

Standardized or calculated abundance value

decimalLatitude

Latitude in decimal degrees

decimalLongitude

Longitude in decimal degrees

coordinateUncertaintyInMeters

Spatial uncertainty of coordinates in meters

locality

Named location where the record was collected

verbatimLocality

Original locality text as provided by the source

municipality

Municipality or town of occurrence

county

County of occurrence

stateProvince

State or province of occurrence

country

Country of occurrence

cleaned_catalog

Standardized catalog number used for de-duplication

lat_precision

Number of decimal places in the latitude coordinate

lon_precision

Number of decimal places in the longitude coordinate

flag_cordinate_precision

Flag for low coordinate precision

flag_cc_val

Flag for invalid or impossible coordinates

flag_cc_equal

Flag for identical latitude and longitude (likely erroneous)

flag_cc_zero

Flag for coordinates at (0,0)

flag_cc_cent

Flag for coordinates placed at a country or region centroid

flag_cc_gbif

Flag for coordinates matching GBIF headquarters (artifact)

flag_cc_inst

Flag for coordinates matching institution location

flag_non_region

Flag for coordinates outside the study region

outliers

Flag for outliers based clustering of spatial and env variables

BO_sstmean

Mean sea surface temperature from Bio-ORACLE

BO_sstmax

Maximum sea surface temperature from Bio-ORACLE

BO_sstmin

Minimum sea surface temperature from Bio-ORACLE

BO_chloro

Chlorophyll concentration from Bio-ORACLE

BO_dissox

Dissolved oxygen level from Bio-ORACLE

Source

dataset5: Documentation of data file - ecodata_x.rda

Description

This data was created to get unique combination of coordinate values to extract env variables from bio-oracle and merge back in main data table - ecodata

Usage

ecodata_x

Format

A data frame with 705 rows and 6 variables:

species

species name

decimalLatitude

Latitude in decimal degrees

decimalLongitude

Longitude in decimal degrees

temperature_mean_BO

Mean sea surface temperature from Bio-ORACLE

temperature_max_BO

Maximum sea surface temperature from Bio-ORACLE

temperature_min_BO

Minimum sea surface temperature from Bio-ORACLE

Source

dataset6: Documentation of data file - example_sp_invertebase.rda

Description

This is a data dump downloaded from invertEbase, as the R package link with InverEbase is currently archive and not maintained, we are providing an example file.

Usage

example_sp_invertebase

Format

A data frame with 710 rows and 20 variables:

source

invertEbase

catalogNumber

CatalogNumber

basisOfRecord

type of observations

occurrenceStatus

presence or absent

institutionCode

Institution code

verbatimEventDate

when was this occurrence created

scientificName

species name

individualCount

abundance

organismQuantity

abundance

abundance

abundance

decimalLatitude

Latitude in decimal degrees

decimalLongitude

Longitude in decimal degrees

coordinateUncertaintyInMeters

uncertainty of coordiantes

locality

location information

verbatimLocality

verbatim location information

municipality

municipality

country

country

stateProvince

State or Provinces

county

county

countryCode

country code

Source

Calculate Harversine distance

Description

Calculate Harversine distance

Usage

haversine_kmeans(data, latitude, longitude, k)

Arguments

data

is a dataframe with spatial attributes - Latitude and Logitude

latitude

nested imput from ec_flag_outlier

longitude

nested imput from ec_flag_outlier

k

is number of cluster required for the data set you have. Normally visual inspection can give a sense on number of clusters. Cautious to have more than expected clusters to fit all data points, as overfitting can end up inluding bad data points in the analysis. e.g. k = 3

Value

A data frame with centroid and clusters using Harversine distance matrix

Examples

data_x <- data.frame(
  scientificName = "Mexacanthina lugubris",
  decimalLongitude = c(-117, -117.8, -116.9),
  decimalLatitude = c(32.9, 33.5, 31.9),
  BO_sstmean = c(12, 13, 14),
  BO_sstmin = c(9, 6, 10),
  BO_sstmax = c(14, 16, 18)
)

result <- haversine_kmeans(
  data_x,
  latitude = "decimalLatitude",
  longitude = "decimalLongitude",
  k = 3
)

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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