Package 'BioTIMEr'

BioTIME subset

Description

A subset of data from BioTIME temporal surveys.

Usage

BTsubset_data

Format

## 'BTsubset_data' A data frame with 81,084 rows and 17 columns:

ID_ALL_RAW_DATA

Unique BioTIME identifier for record

ABUNDANCE

Double representing the abundance for the record (see metadata for details of ABUNDANCE_TYPE

BIOMASS

Double representing the biomass for the record (see metadata for details of BIOMASS_TYPE

ID_SPECIES

Unique identifier linking to the species table

SAMPLE_DESC

Concatenation of variables comprising unique sampling event

PLOT

Name or identifier of plot field, only used for fixed plots such as forest quadrats

LATITUDE

Latitude of record

LONGITUDE

Longitude of record

DEPTH

Depth or elevation of record if available

DAY

Numerical day of record

MONTH

Numerical value of month for record, i.e. January=1

YEAR

Year of record

STUDY_ID

BioTIME study unique identifier

newID

Validated species identifier key

valid_name

Highest taxonomic resolution of individual, preferred is genus and species

resolution

Level of resolution, i.e. 'species' represented by genus and species

taxon

Higher level taxonomic grouping, i.e. Fish

Source

<https://biotime.st-andrews.ac.uk/download.php>

---

BioTIME subset metadata

Description

A subset of the metadata from BioTIME

Usage

BTsubset_meta

Format

## 'BTsubset_meta' A data frame with 12 rows and 25 columns:

STUDY_ID

BioTIME study unique identifier

REALM

Realm of study location, i.e. Marine

CLIMATE

Climate of study location, i.e. Temperate

HABITAT

Habitat of study location, i.e. Rivers

PROTECTED_AREA

binary variable indicating if the study is within a protected area

BIOME_MAP

Biome of study location (taken from the WWF biomes, i.e. Temperate broadleaf and mixed forests

TAXA

High level taxonomic identity of study species, i.e. Fish

ORGANISMS

More detailed information on taxonomy, i.e. woody plants

TITLE

Title of study as identified in original source

AB_BIO

A, B or AB to designate abundance only, biomass only or both

DATA_POINTS

Number of unique data points in study, e.g. 10 data points spanning 15 years = 10

START_YEAR

first year of study

END_YEAR

last year of study

CENT_LAT

Central latitude taken from the convex hull around all study coordinates

CENT_LONG

Central longitude taken from the convex hull around all study coordinates

NUMBER_OF_SPECIES

Number of distinct species in study

NUMBER_OF_SAMPLES

Number of distinct samples in study

NUMBER_LAT_LONG

Number of distinct geographic coordinates in study

TOTAL

Total number of records in study

GRAIN_SQ_KM

Grain size in km2, i.e. size of forest plots

AREA_SQ_KM

total area of study in km2

DATE_STUDY_ADDED

Date that the study was added to the database

ABUNDANCE_TYPE

Type of abundance, i.e. count

BIOMASS_TYPE

Type of biomass, i.e. weight

SAMPLE_DESC

concatenation of descriptors comprising the unique sampling event

Source

<https://biotime.st-andrews.ac.uk/download.php>

---

Alpha diversity metrics

Description

Calculates a set of standard alpha diversity metrics

Usage

getAlphaMetrics(x, measure)

Arguments

x	(data.frame) BioTIME data table in the format of the output of the gridding function and/or resampling function.
measure	(character) chosen currency defined by a single column name.

Details

The function getAlphaMetrics computes nine alpha diversity metrics for a given community data frame, where measure is a character input specifying the abundance or biomass field used for the calculations. For each row of the data frame with data, getAlphaMetrics calculates the following metrics:

- Species richness (S) as the total number of species in each year with currency > 0.

- Numerical abundance (N) as the total currency (sum) in each year (either total abundance or total biomass).

- Maximum Numerical abundance (maxN) as the highest currency value reported in each year.

- Shannon or Shannon–Weaver index is calculated as $\sum_{i}p_{i}log_{b}p_{i}$, where $p_{i}$ is the proportional abundance of species i and b is the base of the logarithm (natural logarithms), while exponential Shannon is given by exp(Shannon).

- Simpson's index is calculated as $1-sum(p_{i}^{2})$, while Inverse Simpson as $1/sum(p_{i}^{2})$.

- McNaughton's Dominance is calculated as the sum of the pi of the two most abundant species.

- Probability of intraspecific encounter or PIE is calculated as $\left(\frac{N}{N-1}\right)\left(1-\sum_{i=1}^{S}\pi_{i}^{2}\right)$.

Note that the input data frame needs to be in the format of the output of the gridding function and/or resampling functions, which includes keeping the default BioTIME data column names. If such columns are not found an error is issued and the computations are halted.

Value

Returns a data frame with results for species richness (S), numerical abundance (N), maximum numerical abundance (maxN), Shannon Index (Shannon), Exponential Shannon (expShannon), Simpson's Index (Simpson), Inverse Simpson (InvSimpson), Probability of intraspecific encounter (PIE) and McNaughton's Dominance (DomMc) for each year and assemblageID.

Examples

x <- data.frame(
    resamp = 1L,
    YEAR = rep(rep(2010:2015, each = 4), times = 4),
    Species = c(replicate(n = 8L, sample(letters, 24L, replace = FALSE))),
    ABUNDANCE = rpois(24 * 8, 10),
    assemblageID = rep(LETTERS[1L:8L], each = 24)
  )
  res <- getAlphaMetrics(x, measure = "ABUNDANCE")

---

Beta diversity metrics

Description

Calculates a set of standard beta diversity metrics

Usage

getBetaMetrics(x, measure)

Arguments

x	(data.frame) BioTIME data table in the format of the output of the gridding function and/or resampling functions.
measure	(character) chosen currency defined by a single column name.

Details

The function getBetaMetrics computes three beta diversity metrics for a given community data frame, where measure is a character input specifying the abundance or biomass field used for the calculations. getBetaMetrics calls the vegdist function which calculates for each row the following metrics: Jaccard dissimilarity (method = "jaccard"), Morisita-Horn dissimilarity (method = "horn") and Bray-Curtis dissimilarity (method = "bray"). Here, the dissimilarity metrics are calculated against the baseline year of each assemblage time series i.e. the first year of each time series. Note that the input data frame needs to be in the format of the output of the gridding and/or resampling functions, which includes keeping the default BioTIME data column names. If such columns are not found an error is issued and the computations are halted.

Value

Returns a data.frame with results for Jaccard dissimilarity (JaccardDiss), Morisita-Horn dissimilarity (MorisitaHornDiss), and Bray-Curtis dissimilarity (BrayCurtsDiss) for each year and assemblageID.

Examples

x <- data.frame(
  resamp = 1L,
  YEAR = rep(rep(2010:2015, each = 4), times = 4),
  Species = c(replicate(
   n = 8L,
   sample(letters, 24L, replace = FALSE))),
  ABUNDANCE = rpois(24 * 8, 10),
  assemblageID = rep(LETTERS[1L:8L], each = 24)
  )

res <- getBetaMetrics(x, measure = "ABUNDANCE")

---

Get Linear Regressions BioTIME

Description

Fits linear regression models to getAlphaMetrics or getBetaMetrics outputs

Usage

getLinearRegressions(x, divType, pThreshold = 0.05)

Arguments

x	('data.frame') BioTIME data table in the format of the output of getAlphaMetrics or getBetaMetrics functions
divType	('character') string specifying the nature of the metrics in the data; either 'divType = "alpha"' or 'divType = "beta"' are supported
pThreshold	('numeric') P-value threshold for statistical significance

Details

The function 'getLinearRegressions' fits simple linear regression models (see lm for details) for a given output ('data') of either getAlphaMetrics or getBetaMetrics function. 'divType' needs to be specified in agreement with x. The typical model has the form 'metric ~ year'. Note that assemblages with less than 3 time points and/or single species time series are removed.

Value

Returns a single long 'data.frame' with results of linear regressions (slope, p-value, significance, intercept) for each 'assemblageID'.

Examples

library(BioTIMEr)
  x <- data.frame(
    resamp = 1L,
    YEAR = rep(rep(2010:2015, each = 4), times = 4),
    Species = c(replicate(n = 8L * 6L, sample(letters[1L:10L], 4L, replace = FALSE))),
    ABUNDANCE = rpois(24 * 8, 10),
    assemblageID = rep(LETTERS[1L:8L], each = 24)
  )
  alpham <- getAlphaMetrics(x, "ABUNDANCE")
  getLinearRegressions(x = alpham, divType = "alpha", pThreshold = 0.01)
  betam <- getBetaMetrics(x = x, "ABUNDANCE")
  getLinearRegressions(x = betam, divType = "beta")

---

gridding BioTIME data

Description

grids BioTIME data into a discrete global grid based on the location of the samples (latitude/longitude).

Usage

gridding(meta, btf, res = 12, resByData = FALSE)

Arguments

meta	(data.frame) BioTIME metadata.
btf	(data.frame) BioTIME data.
res	(integer) cell resolution. Must be in the range [0,30]. Larger values represent finer resolutions. Default: 12 (~96 sq km). Passed to dgconstruct.
resByData	(logical) FALSE by default. If TRUE, the function dg_closest_res_to_area is called to adapt 'res' to the data extent.

Details

Each BioTIME study contains distinct samples which were collected with a consistent methodology over time, and each with unique coordinates and date. These samples can be fixed plots (i.e. SL or 'single-location' studies where measures are taken from a set of specific georeferenced sites at any given time) or wide-ranging surveys, transects, tows, and so on (i.e. ML or 'multi-location' studies where measures are taken from multiple sampling locations over large extents that may or may not align from year to year, see runResampling. gridding is a function designed to deal with the issue of varying spatial extent between studies by using a global grid of hexagonal cells derived from dgconstruct and assigning the individual samples to the cells across the grid based on its latitude and longitude. Specifically, each sample is assigned a different combination of study ID and grid cell resulting in a unique identifier for each assemblage time series within each cell (assemblageID). This allows for the integrity of each study and each sample to be maintained, while large extent studies are split into local time series at the grid cell level. By default meta represents a long form data frame containing the data information for BioTIME studies and btf is a data frame containing long form data from a main BioTIME query (see Example). res defines the global grid cell resolution, thus determining the size of the cells (see vignette("dggridR")). res = 12 was found to be the most appropriate value when working on the whole BioTIME database(corresponding to ~96 km2 cell area), but the user can define their own grid resolution (e.g. res = 14, or when resbyData = TRUE allow the function to find the best res based on the average study extent.

Value

Returns a 'data.frame', with selected columns from the btf and meta data frames, an extra integer column called 'cell' and two character columns called 'StudyMethod' and 'assemblageID' (concatenation of study_ID and cell).

Examples

library(BioTIMEr)
  gridded_data <- gridding(BTsubset_meta, BTsubset_data)

---

Rarefy BioTIME data to an equal number of samples per year

Description

Takes the output of gridding and applies sample-based rarefaction to standardise the number of samples per year within each cell-level time series (i.e. assemblageID).

Usage

resampling(x, measure, resamps = 1L, conservative = FALSE)

Arguments

x	(data.frame) BioTIME gridded data to be resampled (in the format of the output of the gridding function).
measure	(character) currency to be retained during the sample-based rarefaction. Can be either defined by a single column name or a vector of two or more column names.
resamps	(integer) number of repetitions. Default is 1.
conservative	(logical). FALSE by default. If TRUE, whenever a NA is found in the measure field(s), the whole sample is removed instead of the missing observations only.

Details

Sample-based rarefaction prevents temporal variation in sampling effort from affecting diversity estimates (see Gotelli N.J., Colwell R.K. 2001 Quantifying biodiversity: procedures and pitfalls in the measurement and comparison of species richness. Ecology Letters 4(4), 379-391) by selecting an equal number of samples across all years in a time series. resampling counts the number of unique samples taken in each year (sampling effort), identifies the minimum number of samples across all years, and then uses this minimum to randomly resample each year down to that number. Thus, standardising the sampling effort between years, standard biodiversity metrics can be calculated based on an equal number of samples (e.g. using getAlphaMetrics, getAlphaMetrics). measure is a character input specifying the chosen currency to be used during the sample-based rarefaction. It can be a single column name or a vector of two or more column names - e.g. for BioTIME, measure="ABUNDANCE", measure="BIOMASS" or measure = c("ABUNDANCE", "BIOMASS").

By default, any observations with NA within the currency field(s) are removed. You can choose to remove the full sample where such observations are present by setting conservative to TRUE. resamps can be used to define multiple iterations, effectively creating multiple alternative datasets as in each iteration different samples will be randomly selected for the years where number of samples > minimum. Note that the function always returns a single data frame, i.e. if resamps > 1, the returned data frame is the result of individual data frames concatenated together, one from each iteration identified by a numerical unique identifier 1:resamps.

Value

Returns a single long form data.frame containing the total currency or currencies of interest (sum) for each species in each year within each rarefied time series (i.e. assemblageID). An extra integer column called resamp indicates the specific iteration.

Examples

library(BioTIMEr)
  set.seed(42)
  x <- gridding(BTsubset_meta, BTsubset_data)
  resampling(x, measure = "BIOMASS")
  resampling(x, measure = "ABUNDANCE")
  resampling(x, measure = c("ABUNDANCE","BIOMASS"))

---

Scale construction for ggplot use

Description

Scale construction for ggplot use

Scale construction for filling in ggplot

Usage

scale_color_biotime(palette = "realms", discrete = TRUE, reverse = FALSE, ...)

scale_colour_biotime(palette = "realms", discrete = TRUE, reverse = FALSE, ...)

scale_fill_biotime(palette = "realms", discrete = TRUE, reverse = FALSE, ...)

Arguments

palette	One of: 'realms', 'gradient', 'cool', 'warm', default to 'realms'.
discrete	See Details. default to 'FALSE'
reverse	Default to 'FALSE'
...	Passed to discrete_scale or scale_color_gradient

Details

USAGE NOTE: Remember to change these arguments when plotting colours continuously.

Value

If discrete is TRUE, the function returns a colour palette produced by discrete_scale and if discrete is FALSE, the function returns a colour palette produced by scale_color_gradient.

If discrete is TRUE, the function returns a colour palette produced by discrete_scale and if discrete is FALSE, the function returns a colour palette produced by scale_color_gradient.

Author(s)

Cher F. Y. Chow

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