Pipe operator

Description

Pipe operator

Usage

lhs %>% rhs

Posterior probs of genetic region origin from Leave-one-out cross validation for breeding WIWAs

Description

A data frame of the same birds (roughly) that appear in breeding_wiwa_isotopes. A long format data frame with 2,358 rows and 5 columns

Usage

breeding_wiwa_genetic_posteriors

Format

A tibble with 2,358 rows and 5 variables. The variables are:

ID

unique identifier for each bird

Short_Name

another id for the bird

NumberOfLoci

Number of loci successfully typed

region

one of the genetic regions

posterior

the posterior prob of originating from that region

Source

Kristen Ruegg, Eric Anderson, Thomas Smith

Isotope values, latitude, longitude and more data from 357 breeding Wilson's warblers

Description

A data frame containing hydrogen isotope values, lat, long, and IDs and some other columns of data for birds sampled on the breeding grounds. Notice that the latitude column is named "lat" and the longitude column is named "long". Those names are both all lowercase. That is the way we roll here. Make sure that you use "lat" and "long" instead of "Lat" and "Long".

Usage

breeding_wiwa_isotopes

Format

A tibble with 357 rows and 15 variables. The relevant variables for analyses here are:

ID

unique identifier for each bird

Isotope.Value

hydrogen isotope ratios measured in the bird's feather

lat

latitude of the bird's breeding/sampling location

long

latitude of the bird's breeding/sampling location

Source

Kristen Ruegg, Jeff Kelly, Thomas Smith

combine genetics, isotopes, and habitat raster with exponents as given

Description

This just multiplies the rasters together, each raised to the appropriate exponent, normalizes and returns the result

Usage

comboize(Mgen, Miso, Mhab, beta_gen, beta_iso, beta_hab)

Arguments

Examples

# first, run through the example for isotope_posterior_probs() to get
# the rasters for two migrant birds. This gives us the list "birds2"
example(isotope_posterior_probs)

# extract the posterior probs rasters from  that output into a raster stack
miso <- lapply(birds2$regular, function(x) x$posterior_probs) %>%
  raster::stack()

# see the names of the birds we are dealing with:
names(miso)

# get the genetic posteriors for those two birds
mig_gen2 <- migrant_wiwa_genetic_posteriors %>%
  dplyr::filter(ID %in% c(names(miso)))

# make genetic posterior rasters for those two birds, make sure they are
# sorted in the same order as miso, and make a raster stack of it
mgen <- genetic_posteriors2rasters(G = mig_gen2, R = genetic_regions)[names(miso)] %>%
  raster::stack()

# make a normalized prior from habitat quality that is zeros everywhere
# outside of the "known" range.
tmp <- wiwa_habitat_unclipped * wiwa_breed
mhab <- tmp / raster::cellStats(tmp, sum)

# combine genetics, isotopes and habitat with exponents of 1 on each
mcombo <- comboize(mgen, miso, mhab, 1, 1, 1)

prepare fortified output for multipanel plot

Description

This takes Mgen, Miso, and Mhab for a single bird and, if available, the true breeding location. Then it computes the combo-ized raster at all the requested levels of the exponents, and creates a fortified data frame of the results suitable for plotting in ggplot

Usage

comboize_and_fortify(
  mgen,
  miso,
  mhab,
  gen_beta_levels = 1,
  iso_beta_levels = c(1),
  hab_beta_levels = c(1)
)

Arguments

Examples

# run through the example for comboize to get the variables
# mgen, miso, and mhab that we will use.
example(comboize)

# then run that on the first bird to get a data frame
# that you can use with ggplot
ff <- comboize_and_fortify(mgen[[1]], miso[[1]], mhab)

# this can be plotted with ggplot2
## Not run: 
library(ggplot2)
wmap <- get_wrld_simpl()
ggplot(mapping = aes(x=long, y = lat)) +
  coord_fixed(1.3, xlim = c(-170, -50), ylim = c(33, 70)) +
  geom_polygon(data = wmap, aes(group = group), fill = NA, color = "black", size = .05) +
  geom_raster(data = ff, mapping = aes(fill = prob), interpolate = TRUE) +
  scale_fill_gradientn(colours = c("#EBEBEB", rainbow(7)), na.value = NA) +
  theme_bw() +
  facet_wrap( ~ beta_vals, ncol = 2) +
  theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank())

## End(Not run)

Output of isotope_posterior_probs for two migrant birds.

Description

Because it takes too long to generate this output for future examples, we just store it as a data object to use in examples. See the example in isotope_posterior_probs to see what this is.

Usage

example_isotope_posteriors

Format

An object of class list of length 2.

Source

Ruegg et al 2014

Add the isomap prediction and sd in columns attached to the feather isotope data frame

Description

Rasterizes the isomap predictions and standard deviation (using isomap2raster) and then extracts the values associated with each location from the raster and returns the full data frame with those values joined on in columns named iso_pred and iso_sd. It overwrites those columns with a warning if either of those columns already exists in the data.

Usage

extract_isopredictions(isoscape, birds, pred = "predkrig", sd = "stdkrig")

Arguments

Examples

# Using the provided data from breeding Wilson's warblers and the provided
# predictions from isomap_job54152:
x <- extract_isopredictions(isoscape = isomap_job54152_prediction,
                       birds = breeding_wiwa_isotopes,
                       pred = "predkrig",
                       sd = "stdkrig")

gaiah: Genetic and Isotopic Assignment Accounting for Habitat Suitability

Description

Tools for using genetic markers, stable isotope data, and habitat suitability data to calculate posterior probabilities of breeding origin of migrating birds.

Details

There is not a tutorial within the package, currently. The best place to find an example of how to use the functions in this package is at the GitHub repository: https://github.com/eriqande/gaiah-wiwa. Go ahead and read the README there. It should provide you with everything you need to get up and running with the gaiah package.

Finally, note that the development version of gaiah is available at https://github.com/eriqande/gaiah.

Convert posteriors to particular genetic reporting groups into raster

Description

When birds have been assigned to breeding groups or "general areas" as in Ruegg et al. 2014 then the posterior probabilty with which the birds were assigned to the groups needs to be "smeared out" in a raster over the spatial extent of the groups.

Usage

genetic_posteriors2rasters(G, R)

Arguments

Value

This returns a list of rasters for each bird in G. The entries in the raster are the posterior probability of being from that cell. This assumes that birds are equally likely to come from any cell within the group's region. It doesn't return a rasterStack because you can't subset rasterStacks to change orders, etc., and it mangles names.

Examples

library(raster)  # needed to deal with "genetic_regions" variable
# get a small subset of individuals so it doesn't take too long
data(breeding_wiwa_genetic_posteriors)
data(genetic_regions)
BW <- breeding_wiwa_genetic_posteriors %>%
  dplyr::filter(Short_Name %in% c("eNBFR01", "wABCA05", "wORHA21"))

# run the function on those
GPRs <-  genetic_posteriors2rasters(BW, genetic_regions)

RasterStack showing the 6 genetic regions that Wilson's warblers may be assigned to

Description

The sum over layers gives the same as wiwa_breed

Usage

genetic_regions

Format

RasterStack with 6 layers. Each contains 1's in the genetic region and 0's elsewhere. The sum of these layers is the raster wiwa_breed.

class

RasterStack

dimensions

80, 228, 18240, 6 (nrow, ncol, ncell, nlayers)

resolution

0.5, 0.5 (x, y)

extent

-168.1, -54.1, 31.2, 71.2 (xmin, xmax, ymin, ymax)

coord. ref.

+proj=longlat +ellps=WGS84 +datum=WGS84 +no_defs +towgs84=0,0,0

data source

in memory

names

CalSierra, Basin.Rockies, Eastern, AK.EastBC.AB, Wa.To.NorCalCoast, CentCalCoast

Source

Ruegg et al 2014

return the wrld_simpl data set from maptools

Description

I define this as a function so that we don't have to attach maptools, but we can just have it in the imports. Couldn't figure out how to do it otherwise.

Usage

get_wrld_simpl()

Examples

ws <- get_wrld_simpl()
head(ws)
## Not run: plot(ws)

return a raster of great circle distances (in km)

Description

Given an input raster R, this returns a raster of the same dimension where every cell is the great circle distance between lat, and long, and the center of every cell in R.

Usage

great_circle_raster(R, lat, long)

Arguments

Examples

# We compute the great circle distance between the lat/long of my office in
# California, to every cell in the raster denoting the breeding habitat
# of Wilson's warbler:
gcr <- great_circle_raster(wiwa_breed, lat = 36.951564, long = -122.065116)

# plot that if you want
## Not run: 
plot(gcr)
lines(get_wrld_simpl())

## End(Not run)

Group bird isotope data by locations

Description

This takes as input a data frame of feather isotope data that also has the isoscape predictions attached to it, just like the data frame returned by extract_isopredictions. The data frame must have a column that gives the general location by which you will group birds for the rescaling function. The isoscape predictions by default should be in columns named iso_pred for the actual prediction, and iso_sd for the standard deviation, as produced by extract_isopredictions, but those are user configurable, as well.

Usage

group_birds_by_location(
  D,
  feather_isotope_col,
  location_col,
  iso_pred_col = "iso_pred",
  iso_sd_col = "iso_sd"
)

Arguments

Details

This function returns a data frame with columns for the mean and SD of feather/bird values, (meanH and sdH) and the mean predicted isotope value and the mean sd of the predicted isotope values (meaniso and sdiso) for all the samples within each location. It also returns the Location column itself and a column cnt that gives the number of bird/tissue samples from each location.

This function throws an error if any of the locations has only 1 sample. If that is the case, you may consider merging that sample with another location (or dropping it?).

Examples

# first run the example for extract_isopredictions to get the variable "x"
example("extract_isopredictions")

# If this were run it gives an error because there is only 1 bird at the
# location "Charlevoix"
## Not run: 
group_birds_by_location(x, feather_isotope_col = "Isotope.Value", location_col = "Location")

## End(Not run)


# remove that one bird at Charlevoix and re-run
y <- x %>%
  dplyr::filter(Location != "Charlevoix")

# then group birds by location
gbl <- group_birds_by_location(D = y,
                               feather_isotope_col = "Isotope.Value",
                               location_col = "Location")

finds the cumulative posterior probability of each cell for HPD calculation

Description

This function is not exported currently, but is used in min_hpd_inclusion_area

Usage

hpd_cumul(B)

Arguments

convert columns of an ISOMAP isoscape to a raster object

Description

Just simple conversion, but nice to have this in a brief function

Usage

isomap2raster(isoscape, column, Proj = raster::projection(get_wrld_simpl()))

Arguments

Examples

isorast <- isomap2raster(isomap_job54152_prediction, "predreg")
isorast

Predicted isotope values from ISOMAP

Description

A data frame containing predicted hydrogen isotope values, lat, long, and IDs and some other columns of data prections made by ISOMAP

Usage

isomap_job54152_prediction

Format

A tibble with 10,786 rows and 12 variables. The relevant variables for analyses here are:

lat

latitude of the predicted location

long

longitude of the predicted location

predreg

Fill in

stdreg

Fill in

predkrig

Fill in

stdkrig

Fill in

Source

Kristina Paxton and ISOMAP (http://isomap.rcac.purdue.edu:8080/gridsphere/gridsphere)

compute posterior probabilities of origin given isotope values

Description

This function automates the whole process described in the appendix of Vander Zanden et al. (2014) for computing the posterior probability of origin of an individual (or group of individuals) given its stable isotope values, and those of a set of reference individuals, and an ISOMAP prediction of isotope values across a landscape.

Usage

isotope_posterior_probs(
  isoscape,
  ref_birds,
  assign_birds = NULL,
  isoscape_pred_column = "predkrig",
  isoscape_sd_column = "stdkrig",
  self_assign = FALSE
)

Arguments

Details

For details see:

Vander Zanden HB, Wunder MB, Hobson KA, Van Wilgenburg SL, Wassenaar LI, Welker JM, Bowen GJ (2014) Contrasting assignment of migratory organisms to geographic origins using long-term versus year-specific precipitation isotope maps. Methods in Ecology and Evolution, 5, 891-900.

And the re-explanation of that method in Ruegg et al. (2017).

Examples

# obtain posterior probability rasters for the first 2 birds in the migrant_wiwa_isotopes
# data set.  This takes about 15 seconds on my laptop (most of that time is preparatory---once
# that is done, each bird goes much faster). So we don't run it here.
## Not run: 
birds2 <- isotope_posterior_probs(isoscape = isomap_job54152_prediction,
                        ref_birds = breeding_wiwa_isotopes,
                        assign_birds = migrant_wiwa_isotopes[1:2,]
                        )

## End(Not run)

# However, you can load the results as a saved data object to see what they look like:
birds2 <- example_isotope_posteriors


# Since the ref_birds above were separate from the migrant birds, no leave-one-out was
# performed.  Hence birds2$loo_results is NULL, and all the results are in
# birds2$regular.

# Look at the names of the resulting output for the first bird:
names(birds2$regular[[1]])

names(birds2$regular[[1]]$assignment_parameters)



# If you want to do self-assignment for a whole bunch of reference birds, it takes much longer.
# It looks like this:
## Not run: 
self_ass <- isotope_posterior_probs(isoscape = isomap_job54152_prediction,
                                    ref_birds = breeding_wiwa_isotopes,
                                    self_assign = TRUE)

## End(Not run)

Posterior probs of genetic region of origin for 926 WIWAs sampled during migration

Description

A long format data frame with 5,556 rows and 6 columns

Usage

migrant_wiwa_genetic_posteriors

Format

A tibble with 5,556 rows and 6 columns. The relevant variables for analyses here are:

ID

unique identifier for each bird

Short_Name

same id for the bird

Collection_Date

The date the bird was sampled.

NumberOfLoci

Number of loci successfully typed

region

one of the genetic regions

posterior

the posterior prob of originating from that region

Source

Kristina Paxton, Kristen Ruegg, Eric Anderson, Thomas Smith

Isotope values and meta data for 688 migrating Wilson's Warblers

Description

A data frame containing hydrogen isotope values, lat, long, and IDs and some other columns of data for birds sampled during migration from Arizona. 604 of the individuals in this data set also have values in migrant_wiwa_genetic_posteriors.

Usage

migrant_wiwa_isotopes

Format

A tibble with 688 rows and 14 variables. The relevant variables for analyses here are:

ID

unique identifier for each bird

Isotope.Value

hydrogen isotope ratios measured in the bird's feather

Source

Kristina Paxton

compute the minimum hpd inclusion area for all the birds in a rasterStack

Description

This is a convenient wrapper that lets you pass in a data frame of bird names with lats and longs and a rasterStack or rasterBrick and it returns the min hpd inclusion area for each bird in a tbl_df-ed data frame.

Usage

min_hpd_inc_area_df(birds, R)

Arguments

Value

This returns a list with three components:

• area the minimum hpd inclusion area as a data frame with columns "Short_Name" and "min_hpd_area"

• missingFromRastercharacter vector of the names of birds found in birds$Short_Name but not found in the names of R

• missingFromDF character vector of names in R that are not found in birds$Short_Name.

compute the smallest area of an HPD credible set that includes the true bird location

Description

This function computes the area of the HPD credible set that is as small as possible but just barely includes the cell with the true location (or, if the true location is not actually inluded in the range map, we use the closest cell on the range map to the true location). This operates on a single rasterLayer. To operate on a rasterBrick of posteriors and a data frame of lat-longs, use 'min_hpd_inc_area_df()'.

Usage

min_hpd_inclusion_area(M, lat, long)

Arguments

internal function for isotope machinations

Description

does these things: 1. removes birds in Locations that have only 1 bird in them (printing a warning message while it does so) 2. does the vza rescaling process and returns the output that is needed for the vza_assign function. (basically the ouput of the mean and var rasters). Should return a-bar and b-bar too — all of that in a big list. Then with that output I can pump it all into vza_assign for a particular (left-out) ref_bird or for an assign bird. I should return the fitted model for each one.

Usage

private_rescale(birds, pred, std)

Arguments

assign posterior probability of origin for a bird in each cell in the raster

Description

This is a rewrite of the function assignment from the Vander Zanden appendix code.

Usage

vza_assign(rescale_mean, rescale_var, precip_sd, sd_indiv, bird_iso)

Arguments

Details

This is a fairly low-level function. It returns a raster of posterior probs (they are scaled to sum to one over all cells).

calculate a raster of mean and variance of expected tissue isotopes from precip data and resampled regressions

Description

This is a rewrite of the function raster.conversion from the Vander Zanden appendix. They expressed things in terms of the standard deviations, but they need to be turned into variances, anyway, so that is what we've done here. Following the notation of the paper on Wilson's warbler, this function computes $tildeT^(mu)$ (returned as list component mean.raster) and $R^(sigma^2)$ (returned as list component var.raster)

Usage

vza_mean_and_var_rasters(iso_raster, si)

Arguments

Parametric bootstrap for rescaling a la the vander zanden appendix

Description

This is a vectorized and much more efficient implementation of the original rescale function from the Vander Zanden appendix. It takes the output of group_birds_by_location directly and does the parametric bootstrapping for vza_rescale_reps samples.

Usage

vza_rescale(SBL, vza_rescale_reps = 1000)

Arguments

Value

Returns a matrix with vza_rescale_reps rows. Column 1 is "intercepts" and column two is "slopes"

a raster of the breeding range of Wilson's warbler

Description

a raster of the breeding range of Wilson's warbler

Usage

wiwa_breed

Format

This a rasterized version of the breeding range of Wilson's warbler It contains 1's in the breeding range and 0's elsewhere.

class

RasterLayer

dimensions

80, 228, 18240 (nrow, ncol, ncell)

resolution

0.5, 0.5 (x, y)

extent

-168.1, -54.1, 31.2, 71.2 (xmin, xmax, ymin, ymax)

coord. ref.

+proj=longlat +ellps=WGS84 +datum=WGS84 +no_defs +towgs84=0,0,0

data source

in memory

names

layer

values

0, 1 (min, max)

Source

The rasters were generated from shapefiles provided to us by BirdLife International. (BirdLife International and NatureServe (2012) Bird species distribution maps of the world. BirdLife International, Cambridge, UK and NatureServe, Arlington, USA). Persons interested in the range map should contact BirdLife International http://www.birdlife.org/ or NatureServe http://www.natureserve.org/ directly.

RasterLayer showing the MaxEnt habitat suitability model unclipped by the known breeding range

Description

class

RasterLayer

dimensions

80, 228, 18240, 6 (nrow, ncol, ncell, nlayers)

resolution

0.5, 0.5 (x, y)

extent

-168.1, -54.1, 31.2, 71.2 (xmin, xmax, ymin, ymax)

coord. ref.

+proj=longlat +ellps=WGS84 +datum=WGS84 +no_defs +towgs84=0,0,0

data source

in memory

values

0, 0.001093349 (min, max)

Usage

wiwa_habitat_unclipped

Format

An object of class RasterLayer of dimension 80 x 228 x 1.

Source

Ryan Harrigan

Simple map of the world

Description

This is wrld_simpl from the maptools package. It was all that I used from the maptools package which is going to be archived at the end of 2023. So, I just saved wrld_simpl as a data object in this package.

Usage

wrld_simpl

Format

a SpatialPolygonsDataFrame

Source

Got this from the old maptools package. See ?maptools::wrld_simpl