Package 'Mapinguari'

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)

---

Mapinguari: Tools for process-based biogeographical analysis.

Description

Mapinguari provides solutions for incorporating biological processes in biogeographical analysis.

Mapinguari functions

• clean_points

• get_predict

• get_rasters

• multi_extract

• transform_rasters

• sin_h

---

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)

---

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

...

---