Help for package Mapinguari

Type:

Package

Title:

Process-Based Biogeographical Analysis

Version:

2.0.1

Description:

Facilitates the incorporation of biological processes in biogeographical analyses. It offers conveniences in fitting, comparing and extrapolating models of biological processes such as physiology and phenology. These spatial extrapolations can be informative by themselves, but also complement traditional correlative species distribution models, by mixing environmental and process-based predictors. Caetano et al (2020) <doi:10.1111/oik.07123>.

Depends:

R (≥ 3.5)

License:

GPL-2

Encoding:

UTF-8

LazyData:

true

URL:

https://github.com/gabrielhoc/Mapinguari

BugReports:

https://github.com/gabrielhoc/Mapinguari/issues

Suggests:

geosphere, mgcv

Imports:

dplyr, magrittr, parallel, raster, rlang, stringr, testthat

RoxygenNote:

7.2.1

NeedsCompilation:

Packaged:

2023-06-26 10:52:56 UTC; gabri

Author:

Gabriel Caetano [aut, cre], Juan Santos [aut], Barry Sinervo [aut]

Maintainer:

Gabriel Caetano <gabrielhoc@gmail.com>

Repository:

CRAN

Date/Publication:

2023-06-26 11:10:05 UTC

Mapinguari: Tools for process-based biogeographical analysis.

Description

Mapinguari provides solutions for incorporating biological processes in biogeographical analysis.

Breeding status of Tropidurus torquatus lizards at 15 locations in Brazil during each month of the year.

Description

A dataset containing information on if *Tropidurus torquatus* is breeding or not at specific locations and times.

Usage

TtorquatusBreeding

Format

A data frame with 15 rows and 14 variables:

Lon: Longitude of occurrence records in decimal degrees
Lat: Latitude of occurrence records in decimal degrees
January: Binary breeding status at each location for the month of January, 1 means breeding, 0 means not breeding
February: Binary breeding status at each location for the month of February, 1 means breeding, 0 means not breeding
March: Binary breeding status at each location for the month of March, 1 means breeding, 0 means not breeding
April: Binary breeding status at each location for the month of April, 1 means breeding, 0 means not breeding
May: Binary breeding status at each location for the month of May, 1 means breeding, 0 means not breeding
June: Binary breeding status at each location for the month of June, 1 means breeding, 0 means not breeding
July: Binary breeding status at each location for the month of July, 1 means breeding, 0 means not breeding
August: Binary breeding status at each location for the month of August, 1 means breeding, 0 means not breeding
September: Binary breeding status at each location for the month of September, 1 means breeding, 0 means not breeding
October: Binary breeding status at each location for the month of October, 1 means breeding, 0 means not breeding
November: Binary breeding status at each location for the month of November, 1 means breeding, 0 means not breeding
December: Binary breeding status at each location for the month of December, 1 means breeding, 0 means not breeding

...

359 occurrence records of Tropidurus torquatus in Brazil

Description

A dataset containing *Tropidurus torquatus* distribution records

Usage

TtorquatusDistribution

Format

A data frame with 359 rows and 3 variables:

species: species name
Lon: Longitude of occurrence point
Lat: Latitude of occurrence point

...

Tropidurus torquatus body temperatures at temperature gradient experiments.

Description

A dataset containing 3443 body temperature records of 52 *Tropidurus torquatus* from 6 localities at temperature gradients.

Usage

TtorquatusGradient

Format

A data frame with 3443 rows and 3 variables:

id: individual identity of the lizard perform
temp: lizard body temperature at the moment of the trial, in Celsius
site: place where lizard was collected

...

Operative temperatures of multiple microhabitats at 6 localities in Brazil from 2014 to 2016.

Description

A dataset containing operative temperatures of multiple microhabitats at 6 localities in Brazil from 2014 to 2016.

Usage

TtorquatusOperative

Format

A data frame with 915684 rows and 13 variables:

site: place where temperatures were collected
description: description of site
Lon: Longitude of sampling point
Lat: Latitude of sampling point
temp: temperature at microhabitat, in degrees Celsius
microhabitat: microhabitat sampled
year: year of sampling
month: month of sampling
day: day of sampling
hour: hour of sampling
minute: minute of sampling
t_air_max: maximum air temperature of the day at nearest weather station, in degrees Celsius

...

Running speed achieved by 72 Tropidurus torquatus lizards in 274 trials under different temperatures.

Description

A dataset containing 274 running speed trials of *Tropidurus torquatus* lizards under different temperatures, the temperatures of the runs, individual identities for each lizard, body size of each individual and the site where they were captured.

Usage

TtorquatusPerformance

Format

A data frame with 274 rows and 6 variables:

species: species name
id: individual identity of the lizard perform
temp: lizard body temperature at the moment of the trial, in Celsius
performance: maximum running speed at trial, in meters per second
size: lizard body size, in centimeters
site: place where lizard was collected

...

Clean occurrence records

Description

clean_points Eliminates species occurrence records that are too close to each other or at undesired locations.

Usage

clean_points(
  coord,
  merge_dist,
  coord_col = c("Lon", "Lat"),
  filter_layer = NULL,
  na.rm = FALSE
)

Arguments

coord

data.frame. Data frame containing longitudes (Lon) and latitudes (Lat) of occurrence records of a species.

merge_dist

numeric. Maximum distance between points to be merged, in meters.

coord_col

vector of strings or integers. If x has more than two columns, indicate the name or position of longitude and latitude columns

filter_layer

RasterLayer. Binary raster with 1 representing the regions where records should be kept and 0 the regions where they should be eliminated.

na.rm

logical. if TRUE, remove lines with NA in any coordinate.

Value

Data frame with remaining longitudes and latitudes.

Examples

## Not run: 
#First, we need to obtain an altitude raster to filter by altitude.
library(raster)
alt <- raster::getData("alt", country = "BRA", mask = TRUE)

# Then, we clean the points
 TtorquatusDistribution_clean <-
  clean_points(coord = TtorquatusDistribution,
               merge_dist = 20000,
               filter_layer = !is.na(alt))

## End(Not run)

Creates vectorized predict functions from models.

Description

get_predict Takes inputed models and create vectorized functions able to get the model predictionsfor any value inputed. Also outputs a table comparing models. nls gam glm lm randomForest gbm gls bam

Usage

get_predict(models, separator = "_", ...)

Arguments

models

list. List with models to create the prediction function. The model objects must have methods for function 'predict'.

separator

character. Character that separates variable names, years and scenarios.

...

additional arguments to be passed to predict function (specific for the method of the models supplied).

Value

Returns a list of vectorized functions that get predictions for the models inputted. The functions generated do not perform lazy evaluation, the user must be explicit

Examples


library(mgcv)

perf_no_size <-
  gamm(performance ~ s(temp, bs = 'cs'),
       random = list(id = ~ 1),
       data = TtorquatusPerformance)

perf_size <-
  gamm(performance ~ s(temp, bs = 'cs') + size,
       random = list(id = ~ 1),
       data = TtorquatusPerformance)

perf_functions <- get_predict(list(perf_s = perf_size,
                                   perf_ns = perf_no_size),
                              type = "response")

perf_nsFUN <- perf_functions$perf_ns
perf_sFUN <- perf_functions$perf_s

perf_nsFUN(temp = 30)
perf_sFUN(temp = 30, size = 70)
perf_nsFUN(temp = 30:35)
perf_sFUN(temp = 30, size = 70:75)
perf_sFUN(temp = 30:35, size = 70:75)

Retrieve and organize spatial rasters.

Description

get_rasters Loads rasters from directory and returns them in an organized list of specified scenarios.

Usage

get_rasters(
  var = NULL,
  scenario = NULL,
  raster_path = NULL,
  ext = c(-180, 180, -60, 90),
  coord_col = c("Lon", "Lat"),
  margin = 0,
  separator = "_"
)

Arguments

var

character. Names of variables to be loaded.

scenario

character. Names of scenarios for the variables.

raster_path

character. Path to folder with raster files. See writeFormats for supported formats

ext

numeric, data. frame or Extent object. Extent to crop rasters.

coord_col

character. Names of columns containing Longitude and Latitude.

margin

numeric. Additional distance to be added to margin of extent, in degrees.

separator

character. Character that separates variable names and scenario names.

Value

Returns a list of raster stacks for the variables required, organized by year/scenario combination.

Examples

## Not run: 
# replace rasterpath with the directory on your computer containing worldclim data

Fulanus_Ecorasters_present <-
  get_rasters(
    var = c('prec', 'tmin', 'tmax'),
    scenario = 'present',
    raster_path = "C:/Users/gabri/Dropbox/Mapinguari/global_grids_10_minutes",
    ext = FulanusDistribution[c(2,3)],
    margin = 5)

## End(Not run)

Gets values from multiple rasters.

Description

multi_extract Extract values of multiple spatial rasters for a set of geographical coordinates.

Usage

multi_extract(
  raster_path,
  coord,
  folders = NULL,
  files = NULL,
  layers = NULL,
  ncores = 1
)

Arguments

raster_path

character. Path to the folder with raster folders.

coord

data.frame or matrix. Longitude and Latitude from where to extract raster values.

folders

character. folders from which to get rasters for extraction. If NULL, all folders are selected.

files

numeric. Index for raster files to be extracted from each folder. If NULL, all files are selected.

layers

numeric. Index for layers to be extracted from each raster file. If NULL, all layers are selected.

ncores

integer. Number of cores to use in parallel processing.

Value

Data frame with extracted values from multiple rasters

Examples


## Not run: 
# replace rasterpath with the directory on your computer containing worldclim data

temp_pres <-
  multi_extract(raster_path = "mydir/rasters/worldclim/global_rasters_10min/",
                coord = TtorquatusDistribution[-1],
                folders = c("tmax_present", "tmin_present"))
                
## End(Not run)

Sinervo (2010) hours of activity model

Description

sin_h Simulates daily variation in temperature and counts amount of time above a temperature threshold, as seen in Sinervo et al. 2010.

Usage

sin_h(tmax, tmin, thrs, res)

Arguments

tmax

Raster* object. Maximum temperature raster.

tmin

Raster* object. Minimum temperature raster.

thrs

numeric. Temperature threshold in same unit as rasters.

res

numeric. time resolution in parts of hour.

Value

numeric. Amount of time in hours above temperature threshold in simulated daily temperature variation.

Examples

sin_h(28, 10, 23, 3)

Transform raster values using custom calls.

Description

transform_rasters Applies custom expressions to transform the values of spatial rasters in a stack, taking into account temporal repetition of those rasters.

Usage

transform_rasters(raster_stack, separator = "_", ncores = 1, ...)

Arguments

raster_stack

RasterStack. Stack with environmental layers.

separator

character. Character that separates variable names, years and scenarios.

ncores

integer. Number of cores to use in parallel processing.

...

New rasters created.

Value

Returns a RasterStack with layers for the predictions required.

Examples

## Not run: 
FulanusEcoRasters_present <-
  get_rasters(
    var = c('prec', 'tmax', 'tmin'),
    scenarios = 'present',
    source = "C:/Users/gabri/Dropbox/Mapinguari/global_grids_10_minutes",
    ext = FulanusDistribution[c(2,3)],
    margin = 5,
    reorder = c(1, 10, 11, 12, 2, 3, 4, 5, 6, 7, 8, 9))

# You can apply any function to subsets of rasters in the stack,
# by selecting the layers with double brackets.

transform_rasters(raster_stack = FulanusEcoRasters_present$present,
    total_1sem = sum(tmax[1:6]),
    mean_1sem = mean(tmax[1:6]),
    sd_1sem = sd(tmax[1:6]),
    total_2sem = sum(tmax[7:12]),
    mean_2sem = mean(tmax[7:12]),
    sd_2sem = sd(tmax[7:12]))

## End(Not run)

Mapinguari: Tools for process-based biogeographical analysis.

Description

Mapinguari functions

Breeding status of *Tropidurus torquatus* lizards at 15 locations in Brazil during each month of the year.

Description

Usage

Format

359 occurrence records of *Tropidurus torquatus* in Brazil

Description

Usage

Format

*Tropidurus torquatus* body temperatures at temperature gradient experiments.

Description

Usage

Format

Operative temperatures of multiple microhabitats at 6 localities in Brazil from 2014 to 2016.

Description

Usage

Format

Running speed achieved by 72 *Tropidurus torquatus* lizards in 274 trials under different temperatures.

Description

Usage

Format

Clean occurrence records

Description

Usage

Arguments

Value

Examples

Creates vectorized predict functions from models.

Description

Usage

Arguments

Value

Examples

Retrieve and organize spatial rasters.

Description

Usage

Arguments

Value

Examples

Gets values from multiple rasters.

Description

Usage

Arguments

Value

Examples

Sinervo (2010) hours of activity model

Description

Usage

Arguments

Value

Examples

Transform raster values using custom calls.

Description

Usage

Arguments

Value

Examples

Breeding status of Tropidurus torquatus lizards at 15 locations in Brazil during each month of the year.

359 occurrence records of Tropidurus torquatus in Brazil

Tropidurus torquatus body temperatures at temperature gradient experiments.

Running speed achieved by 72 Tropidurus torquatus lizards in 274 trials under different temperatures.