Subset a uhc_data object

Description

Subset a uhc_data object

Usage

## S3 method for class 'uhc_data'
x[i]

Arguments

GPS tracks from four fishers

Description

This file includes spatial data from 4 fisher (Pekania pennanti). These location data were collected via a 105g GPS tracking collar (manufactured by E-obs GmbH) and programmed to record the animal's location every 10 minutes, continuously. The data re projected in NAD84 (epsg: 5070). The data usage is permitted for exploratory purposes. For other purposes please get in contact (Scott LaPoint).

Usage

amt_fisher

Format

A tibble with 14230 rows and 5 variables:

x_

the x-coordinate

y_

the y-coordinate

t_

the timestamp

sex

the sex of the animal

id

the id of the animal

name

the name of the animal

Source

https://www.datarepository.movebank.org/handle/10255/move.330

References

For more information, contact Scott LaPoint sdlapoint@gmail.com

Environmental data for fishers

Description

A list with three entries that correspond to the following three layer: land use, elevation and population density.

Usage

amt_fisher_covar

Format

A list with three where each entry is a SpatRast.

Source

⁠https://lpdaac.usgs.gov/dataset_discovery/aster/aster_products_table⁠

⁠http://dup.esrin.esa.it/page_globcover.php⁠

⁠http://sedac.ciesin.columbia.edu/data/collection/gpw-v3/sets/browse⁠

Append "_x1"

Description

Helper function to append ⁠_x1⁠ to variable names

Usage

append_x1(string)

Arguments

Details

The function first checks if "_x1" is already appended and adds it if it is not. This is meant for internal use in ⁠\link{plot.log_rss}()⁠.

Value

A string.

Coerce a uhc_data object to data.frame

Description

Coerces uhc_data from list to data.frame

Usage

## S3 method for class 'uhc_data'
as.data.frame(x, row.names = NULL, optional = FALSE, ...)

Arguments

Details

This coercion aims to keep all of the information contained in the uhc_data list in the resulting data.frame representation. Factors are converted to numeric, but the levels are retained in the column "label".

Value

Returns a data.frame with columns:

• var: The name of the variable

• x: The x-coordinate of the density plot (the value of var).

• y: The y-coordinate of the density plot (the probability density for a numeric var and the proportion for a factor var).

• dist: The distribution represented. Either "U" for used, "A" for available, or "S" for sampled.

• iter: The iteration number if dist == "S".

• label: The label if var is a factor.

Author(s)

Brian J. Smith

See Also

prep_uhc(), conf_envelope()

Export track to lines

Description

Exports a track to (multi)lines from the sf package.

Usage

as_sf_lines(x, ...)

Arguments

Value

A tibble with a sfc-column

Coerces a track to points

Description

Coerces a track to points from the sf package.

Usage

as_sf_points(x, ...)
## S3 method for class 'steps_xy'
as_sf_points(x, end = TRUE, ...)

Arguments

Value

A data data.frame with a sfc-column

Coerce to track

Description

Coerce other classes to a track_xy.

Usage

as_track(x, ...)
## S3 method for class 'sfc_POINT'
as_track(x, ...)
## S3 method for class 'steps_xyt'
as_track(x, ...)
## S3 method for class 'data.frame'
as_track(x, ...)

Arguments

Value

An object of class track_xy(t)

Display available distributions for step lengths and turn angles.

Description

Display available distributions for step lengths and turn angles.

Usage

available_distr(which_dist = "all", names_only = FALSE, ...)

Arguments

Value

A tibble with the purpose of the distribution (turn angles [ta] or step length [sl]) and the distribution name.

hr_kde_pi wraps KernSmooth::dpik to select bandwidth for kernel density estimation the plug-in-the-equation method in two dimensions.

Description

This function calculates bandwidths for kernel density estimation by wrapping KernSmooth::dpik. If correct = TURE, the bandwidth is transformed with power 5/6 to correct for using an univariate implementation for bivariate data (Gitzen et. al 2006).

Usage

hr_kde_pi(x, ...)
## S3 method for class 'track_xy'
hr_kde_pi(x, rescale = "none", correct = TRUE, ...)

Arguments

Value

The bandwidth, the standardization method and correction.

References

Gitzen, R. A., Millspaugh, J. J., & Kernohan, B. J. (2006). Bandwidth selection for fixed-kernel analysis of animal utilization distributions. Journal of Wildlife Management, 70(5), 1334-1344.

See Also

KernSmooth::dpik

Reference bandwidth

Description

Calculate the reference bandwidth for kernel density home-range range estimates.

Usage

hr_kde_ref(x, ...)
## S3 method for class 'track_xy'
hr_kde_ref(x, rescale = "none", ...)

Arguments

Value

The estimated bandwidth in x and y direction.

Get bounding box of a track.

Description

Get bounding box of a track.

Usage

bbox(x, ...)
## S3 method for class 'track_xy'
bbox(x, spatial = TRUE, buffer = NULL, ...)
## S3 method for class 'steps_xy'
bbox(x, spatial = TRUE, buffer = NULL, ...)

Arguments

Value

If spatial = FALSE a named vector of length four with the extent of the bounding box. Otherwise a SpatialPolygon or a simple feature polygon with the bounding box.

Examples

data(deer)
bbox(deer)
bbox(deer, spatial = FALSE)
bbox(deer, buffer = 100, spatial = FALSE)
# For steps
deer |> steps_by_burst() |> bbox(spatial = FALSE)
deer |> steps_by_burst() |> bbox(buffer = 100, spatial = FALSE)
deer |> steps_by_burst() |> random_steps() |> bbox(spatial = FALSE)
# Further manipulations are possible
deer |> bbox() |> sf::st_transform(4326)

Single bootstrap iteration

Description

Runs a single iteration of the empirical bootstrap

Usage

boot1.glm(object, x1, x2)
boot1.fit_clogit(object, x1, x2)

Details

This function is meant for internal use by bootstrap_logrss() and is not meant to be called by the user.

Bootstrap log-RSS estimate

Description

Use empirical bootstrap to estimate log-RSS CI

Usage

bootstrap_logrss(object, ...)
## S3 method for class 'glm'
bootstrap_logrss(object, x1, x2, ci_level, n_boot, mle, ...)
## S3 method for class 'fit_clogit'
bootstrap_logrss(object, x1, x2, ci_level, n_boot, mle, ...)

Details

This function is meant for internal use by log_rss() and is not meant to be called by the user.

Calculate w(x)

Description

Calculates the value of the exponential habitat selection function

Usage

calc_w(f, b, newdata)

Arguments

Details

This is actually like to be w(x) * phi(x) for an iSSF.

Calculate SDR

Description

Calculates squared displacement rate for a given speed and duration

Usage

calculate_sdr(speed = 50, time, speed_unit = c("km/h", "m/s"))

Arguments

Value

Returns a numeric vector (of length 1) with the SDR in m^2/s.

Author(s)

Johannes Signer and Brian J. Smith

See Also

get_displacement()

Examples

# Assume a cheetah can sprint 100 km/h for 60 seconds
calculate_sdr(speed = 100, time = seconds(60), speed_unit = "km/h")
# 46296.3 m^2/s
# What is expected displacement in 1 h at that SDR?
get_displacement(46296.3, hours(1))
# 12909.95 m = 12.9 km/h (much slower than sprint speed!)

Calculate the centroid of a track.

Description

Calculate the centroid of a track.

Usage

centroid(x, ...)
## S3 method for class 'track_xy'
centroid(x, spatial = FALSE, ...)

Arguments

Value

The centroid of a track as numeric vector if spatial = FALSE, otherwise as SpatialPoints.

Examples

data(deer)
centroid(deer)

Check factor levels

Description

Check factor levels before log-RSS calculation

Usage

check_factors(model, x1, x2)

Arguments

Details

This function is meant for internal use by log_rss() and is not meant to be called by the user.

Calculate Speed

Description

Calculates speed

Usage

check_time_unit(tu)

Arguments

Details

Calculate Change in NSD

Calculates change in NSD

Check time_unit Parameter

Internal function to check time_unit parameter in various cleaning functions.

Coerce a track to other formats.

Description

Several other packages provides methods to analyze movement data, and amt provides coercion methods to some packages.

Usage

as_sf(x, ...)
## S3 method for class 'steps_xy'
as_sf(x, end = TRUE, ...)
as_sp(x, ...)
as_ltraj(x, ...)
## S3 method for class 'track_xy'
as_ltraj(x, id = "animal_1", ...)
## S3 method for class 'track_xyt'
as_ltraj(x, ...)
as_telemetry(x, ...)
## S3 method for class 'track_xyt'
as_telemetry(x, ...)
as_moveHMM(x, ...)
## S3 method for class 'track_xy'
as_moveHMM(x, ...)

Arguments

Value

An object of the class to which coercion is performed to.

Create confidence envelopes from a uhc_data_frame

Description

Simplifies sampled distributions in a uhc_data_frame to confidence envelopes

Usage

conf_envelope(x, levels = c(0.95, 1))

Arguments

Details

This can dramatically improve plotting time for UHC plots by simplifying the many sampled lines down to the boundaries of a polygon.

Value

Returns a data.frame with columns:

• var: The name of the variable

• x: The x-coordinate of the density plot (the value of var).

• label: If var is a factor, the label for the value given by x.

• U: The y-coordinate of the density plot for the use distribution.

• A: The y-coordinate of the density plot for the availability distribution.

• ⁠CI*_lwr⁠: The lower bound of the confidence envelope for the corresponding confidence level.

• ⁠CI*_upr⁠: The upper bound of the confidence envelope for the corresponding confidence level.

Author(s)

Brian J. Smith

See Also

prep_uhc(), plot.uhc_envelopes()

Converts angles to radians

Description

Converts angles to radians

Usage

as_rad(x)
as_degree(x)

Arguments

Value

A numeric vector with the converted angles.

Examples

as_rad(seq(-180, 180, 30))
# The default unit of turning angles is rad.
data(deer)
deer |> steps() |> mutate(ta_ = as_degree(ta_))

Coordinates of a track.

Description

Coordinates of a track.

Usage

coords(x, ...)

Arguments

Value

⁠[tibble]⁠
The coordinates.

Examples

data(deer)
coords(deer)

Calculate a cumulative UD

Description

Calculate the cumulative utilization distribution (UD).

Usage

hr_cud(x, ...)
## S3 method for class 'SpatRaster'
hr_cud(x, ...)

Arguments

Value

⁠[RasterLayer]⁠
The cumulative UD.

Note

This function is typically used to obtain isopleths.

Relocations of 1 red deer

Description

826 GPS relocations of one red deer from northern Germany. The data is already resampled to a regular time interval of 6 hours and the coordinate reference system is transformed to epsg:3035.

Usage

deer

Format

A track_xyt

x_

the x-coordinate

y_

the y-coordinate

t_

the timestamp

burst_

the burst a particular points belongs to.

Source

Verein für Wildtierforschung Dresden und Göttingen e.V.

Difference in x and y

Description

Difference in x and y coordinates.

Usage

diff_x(x, ...)
diff_y(x, ...)

Arguments

Value

Numeric vector

Name of step-length distribution and turn-angle distribution

Description

Name of step-length distribution and turn-angle distribution

Usage

sl_distr_name(x, ...)
## S3 method for class 'random_steps'
sl_distr_name(x, ...)
## S3 method for class 'fit_clogit'
sl_distr_name(x, ...)
ta_distr_name(x, ...)
ta_distr_name(x, ...)
## S3 method for class 'random_steps'
ta_distr_name(x, ...)
## S3 method for class 'fit_clogit'
ta_distr_name(x, ...)

Arguments

Value

Character vector of length 1.

Functions create statistical distributions

Description

make_distributions creates a distribution suitable for using it with integrated step selection functions

Usage

make_distribution(name, params, vcov = NULL, ...)
make_exp_distr(rate = 1)
make_hnorm_distr(sd = 1)
make_lnorm_distr(meanlog = 0, sdlog = 1)
make_unif_distr(min = -pi, max = pi)
make_vonmises_distr(kappa = 1, vcov = NULL)
make_gamma_distr(shape = 1, scale = 1, vcov = NULL)

Arguments

Value

A list of class amt_distr that contains the name (name) and parameters (params) of a distribution.

Extent of a track

Description

Obtain the extent of a track in x y or both directions

Usage

extent_x(x, ...)
extent_y(x, ...)
extent_both(x, ...)
extent_max(x, ...)

Arguments

Value

Numeric vector with the extent.

Extract covariate values

Description

Extract the covariate values at relocations, or at the beginning or end of steps.

Usage

extract_covariates(x, ...)
## S3 method for class 'track_xy'
extract_covariates(x, covariates, ...)
## S3 method for class 'random_points'
extract_covariates(x, covariates, ...)
## S3 method for class 'steps_xy'
extract_covariates(x, covariates, where = "end", ...)
extract_covariates_along(x, ...)
## S3 method for class 'steps_xy'
extract_covariates_along(x, covariates, ...)
extract_covariates_var_time(x, ...)
## S3 method for class 'track_xyt'
extract_covariates_var_time(
 x,
 covariates,
 when = "any",
 max_time,
 name_covar = "time_var_covar",
 ...
)
## S3 method for class 'steps_xyt'
extract_covariates_var_time(
 x,
 covariates,
 when = "any",
 max_time,
 name_covar = "time_var_covar",
 where = "end",
 ...
)

Arguments

Details

extract_covariates_along extracts the covariates along a straight line between the start and the end point of a (random) step. It returns a list, which in most cases will have to be processed further.

Value

A tibble with additional columns for covariate values.

Examples

data(deer)
sh_forest <- get_sh_forest()
mini_deer <- deer[1:20, ]
mini_deer |> extract_covariates(sh_forest)
mini_deer |> steps() |> extract_covariates(sh_forest)
# Illustration of extracting covariates along the a step
mini_deer |> steps() |> random_steps() |>
 extract_covariates(sh_forest) |> # extract at the endpoint
 (\(.) mutate(., for_path = extract_covariates_along(., sh_forest)))() |>
 # 1 = forest, lets calc the fraction of forest along the path
 mutate(for_per = purrr::map_dbl(for_path, function(x) mean(x == 1)))

Filter bursts by number of relocations

Description

Only retain bursts with a minimum number (= min_n) of relocations.

Usage

filter_min_n_burst(x, ...)
## S3 method for class 'track_xy'
filter_min_n_burst(x, min_n = 3, ...)

Arguments

Value

A tibble of class track_xy(t).

Fit a conditional logistic regression

Description

This function is a wrapper around survival::clogit, making it usable in a piped workflow.

Usage

fit_clogit(data, formula, more = NULL, summary_only = FALSE, ...)
fit_ssf(data, formula, more = NULL, summary_only = FALSE, ...)
fit_issf(data, formula, more = NULL, summary_only = FALSE, ...)

Arguments

Value

A list with the following entries

• model: The model output.

• sl_: The step length distribution.

• ta_: The turn angle distribution.

Fit a continuous time movement model with ctmm

Description

Fit a continuous time movement model with ctmm

Usage

fit_ctmm(x, model, uere = NULL, ...)

Arguments

Value

An object of class ctmm from the package ctmm.

References

C. H. Fleming, J. M. Calabrese, T. Mueller, K.A. Olson, P. Leimgruber, W. F. Fagan, "From fine-scale foraging to home ranges: A semi-variance approach to identifying movement modes across spatiotemporal scales", The American Naturalist, 183:5, E154-E167 (2014).

Examples

data(deer)
mini_deer <- deer[1:20, ]
m1 <- fit_ctmm(mini_deer, "iid")
summary(m1)

Fit distribution to data

Description

Wrapper to fit a distribution to data. Currently implemented distributions are the exponential distribution (exp), the gamma distribution (gamma) and the von Mises distribution (vonmises).

Usage

fit_distr(x, dist_name, na.rm = TRUE)

Arguments

Value

An amt_distr object, which consists of a list with the name of the distribution and its parameters (saved in params).

Examples

set.seed(123)
dat <- rexp(1e3, 2)
fit_distr(dat, "exp")

Fit logistic regression

Description

This function is a wrapper around stats::glm for a piped workflows.

Usage

fit_logit(data, formula, ...)
fit_rsf(data, formula, ...)

Arguments

Value

A list with the model output.

Flag Defunct Clusters

Description

Flags defunct clusters at the end of a track

Usage

flag_defunct_clusters(x, zeta, eta, theta, ...)
## S3 method for class 'track_xyt'
flag_defunct_clusters(x, zeta, eta, theta, ...)

Arguments

Details

Locations at the end of a trajectory may represent a dropped collar or an animal mortality. In some cases, the device may be recording locations for quite some time that are not biologically meaningful. This function aims to flag those locations at the end of the trajectory that belong to a mortality (or similar) cluster. The first location at the cluster remains unflagged, but all subsequent locations are flagged.

The algorithm detects steps that represent zero movement, within a precision threshold given by zeta. That is, if zeta = 5 (units determined by CRS; typically meters), all points that differ by less than 5 will be considered zero movement. Consecutive steps of zero movement (within the tolerance) form a cluster. The parameter eta gives the cutoff for the minimum number of zero steps to be considered a cluster. Finally, the algorithm requires that clusters persist without a non-zero step for a minimum amount of time, given by theta.

Value

Returns x (a track_xyt) with a flagging column added (x$defunct_cluster_).

Author(s)

Brian J. Smith and Johannes Signer, based on code by Tal Avgar

See Also

flag_duplicates(), flag_fast_steps(), flag_roundtrips()

Flag Low Quality Duplicates

Description

Flags locations with duplicate timestamps by DOP and distance

Usage

flag_duplicates(x, gamma, time_unit = "mins", DOP = "dop", ...)
## S3 method for class 'track_xyt'
flag_duplicates(x, gamma, time_unit = "mins", DOP = "dop", ...)

Arguments

Details

Locations are considered duplicates if their timestamps are within gamma of each other. However, the function runs sequentially through the track object, so that only timestamps after the focal point are flagged as duplicates (and thus removed from further consideration). E.g., if gamma = minutes(5), then all locations with timestamp within 5 minutes after the focal location will be considered duplicates.

When duplicates are found, (1) the location with the lowest dilution of precision (given by DOP column) is kept. If there are multiple duplicates with equally low DOP, then (2) the one closest in space to previous location is kept. In the event of exact ties in DOP and distance, (3) the first location is kept. This is unlikely unless there are exact coordinate duplicates.

In the case that the first location in a trajectory has a duplicate, there is no previous location with which to calculate a distance. In that case, the algorithm skips to (3) and keeps the first location.

In the event your data.frame does not have a DOP column, you can insert a dummy with constant values such that all duplicates will tie, and distance will be the only criterion (e.g., x$dop <- 1). In the event you do have an alternate measure of precision where larger numbers are more precise (e.g., number of satellites), simply multiply that metric by -1 and pass it as if it were DOP.

Internally, the function drops duplicates as it works sequentially through the data.frame. E.g., if location 5 was considered a duplicate of location 4 – and location 4 was higher quality – then location 5 would be dropped. The function would then move on to location 6 (since 5 was already dropped). However, the object returned to the user has all the original rows of x – i.e., locations are flagged rather than removed.

Value

Returns x (a track_xyt) with a flagging column added (x$duplicate_).

Author(s)

Brian J. Smith, based on code by Johannes Signer and Tal Avgar

See Also

flag_fast_steps(), flag_roundtrips(), flag_defunct_clusters()

Flag Fast Steps

Description

Flags locations that imply SDR exceeding a threshold

Usage

flag_fast_steps(x, delta, time_unit = "secs", ...)
## S3 method for class 'track_xyt'
flag_fast_steps(x, delta, time_unit = "secs", ...)

Arguments

Details

Locations are flagged if the SDR from the previous location to the current location exceeds delta. Internally, flagged locations are dropped from future consideration.

The time_unit should be the same time unit with which the SDR threshold was calculated. SDR is typically calculated in m^2/s, so time_unit defaults to "secs". The spatial unit is determined by the CRS, which should typically be in meters.

Value

Returns x (a track_xyt) with a flagging column added (x$fast_step_).

Author(s)

Brian J. Smith, based on code by Johannes Signer and Tal Avgar

See Also

flag_duplicates(), flag_roundtrips(), flag_defunct_clusters()

Flag Fast Round Trips

Description

Flags locations that imply fast round trips

Usage

flag_roundtrips(x, delta, epsilon, time_unit = "secs", ...)
## S3 method for class 'track_xyt'
flag_roundtrips(x, delta, epsilon, time_unit = "secs", ...)

Arguments

Details

Locations implying a single fast step can be flagged using flag_fast_steps(). However, it is more likely that a single location is imprecise if it implies an unrealistically fast out-and-back round trip. In that case, the user might be willing to scale the threshold SDR. In this function, delta gives the base SDR and epsilon is the scaling factor, such that locations are considered for flagging if the SDR from the previous location (location i - 1) to the focal location (i) [call it sdr1] and the focal location (i) to the next location (i + 1) [call it sdr2] both have SDR > delta/epsilon.

In that case, the SDR from the previous location (i - 1) to the next location (i + 1) is computed; i.e., the SDR assuming we omit the focal location (i) [call it sdr3]. The remaining locations should be closer together than they are to the omitted location. Thus the focal location is flagged if (sdr1 > epsilon * sdr3) & (sdr2 > epsilon * sdr3).

Note that epsilon both decreases delta in the out-and-back case and increases sdr3 (between the remaining neighbors).

Internally, flagged locations are dropped from future consideration.

The time_unit should be the same time unit with which the SDR threshold was calculated. SDR is typically calculated in m^2/s, so time_unit defaults to "secs". The spatial unit is determined by the CRS, which should typically be in meters. The epsilon parameter is unitless.

Value

Returns x (a track_xyt) with a flagging column added (x$fast_roundtrip_).

Author(s)

Brian J. Smith, based on code by Johannes Signer and Tal Avgar

See Also

flag_duplicates(), flag_fast_steps(), flag_defunct_clusters()

Duration of tracks

Description

Function that returns the start (from), end (to), and the duration (from_to) of a track.

Usage

from_to(x, ...)
## S3 method for class 'track_xyt'
from_to(x, ...)
from(x, ...)
## S3 method for class 'track_xyt'
from(x, ...)
to(x, ...)
## S3 method for class 'track_xyt'
to(x, ...)

Arguments

Value

A vector of class POSIXct.

Helper function to get fisher covars

Description

The current version of terra (1.7.12) requires SpatRasters to be wrapped in order to be saved locally. This function unwraps the covariates for the fisher data and returns a list.

Usage

get_amt_fisher_covars()

Value

A list with covariates

Obtains the Coordinate Reference Systems

Description

Returns the proj4string of an object.

Usage

get_crs(x, ...)

Arguments

Value

The proj4string of the CRS.

Examples

data(deer)
get_crs(deer)

Calculate Expected Displacement

Description

Calculates expected displacement for a given SDR and time span

Usage

get_displacement(delta, time_span)

Arguments

Value

Returns a numeric vector (of length 1) with the expected displacement in meters.

Author(s)

Johannes Signer and Brian J. Smith

See Also

calculate_sdr()

Obtain the step length and/or turn angle distributions from random steps or a fitted model.

Description

Obtain the step length and/or turn angle distributions from random steps or a fitted model.

Usage

sl_distr(x, ...)
## S3 method for class 'random_steps'
sl_distr(x, ...)
## S3 method for class 'fit_clogit'
sl_distr(x, ...)
ta_distr(x, ...)
## S3 method for class 'random_steps'
ta_distr(x, ...)
## S3 method for class 'fit_clogit'
ta_distr(x, ...)

Arguments

Value

An amt distribution

Get the maximum distance

Description

Helper function to get the maximum distance from a fitted model.

Usage

get_max_dist(x, ...)
## S3 method for class 'fit_clogit'
get_max_dist(x, p = 0.99, ...)

Arguments

Helper function to get forest cover

Description

The current version of terra (1.7.12) requires SpatRasters to be wrapped in order to be saved locally. This function unwraps the the forest layer and returns a SpatRast.

Usage

get_sh_forest()

Value

A SpatRast with forest cover.

Check for Coordinate Reference Systems (CRS)

Description

Checks if an object has a CRS.

Usage

has_crs(x, ...)

Arguments

Value

Logic vector of length 1.

Examples

data(deer)
has_crs(deer)

Home ranges

Description

Functions to calculate animal home ranges from a ⁠track_xy*⁠. hr_mcp, hr_kde, and hr_locoh calculate the minimum convex polygon, kernel density, and local convex hull home range respectively.

Usage

hr_akde(x, ...)
## S3 method for class 'track_xyt'
hr_akde(
 x,
 model = fit_ctmm(x, "iid"),
 keep.data = TRUE,
 trast = make_trast(x),
 levels = 0.95,
 wrap = FALSE,
 ...
)
hr_kde(x, ...)
## S3 method for class 'track_xy'
hr_kde(
 x,
 h = hr_kde_ref(x),
 trast = make_trast(x),
 levels = 0.95,
 keep.data = TRUE,
 wrap = FALSE,
 ...
)
hr_locoh(x, ...)
## S3 method for class 'track_xy'
hr_locoh(
 x,
 n = 10,
 type = "k",
 levels = 0.95,
 keep.data = TRUE,
 rand_buffer = 1e-05,
 ...
)
hr_mcp(x, ...)
hr_od(x, ...)

Arguments

Value

A hr-estimate.

References

Worton, B. J. (1989). Kernel methods for estimating the utilization distribution in home-range studies. Ecology, 70(1), 164-168. C. H. Fleming, W. F. Fagan, T. Mueller, K. A. Olson, P. Leimgruber, J. M. Calabrese, "Rigorous home-range estimation with movement data: A new autocorrelated kernel-density estimator", Ecology, 96:5, 1182-1188 (2015).

Fleming, C. H., Fagan, W. F., Mueller, T., Olson, K. A., Leimgruber, P., & Calabrese, J. M. (2016). Estimating where and how animals travel: an optimal framework for path reconstruction from autocorrelated tracking data. Ecology, 97(3), 576-582.

Examples

data(deer)
mini_deer <- deer[1:100, ]
# MCP ---------------------------------------------------------------------
mcp1 <- hr_mcp(mini_deer)
hr_area(mcp1)
# calculated MCP at different levels
mcp1 <- hr_mcp(mini_deer, levels = seq(0.3, 1, 0.1))
hr_area(mcp1)
# CRS are inherited
get_crs(mini_deer)
mcps <- hr_mcp(mini_deer, levels = c(0.5, 0.95, 1))
has_crs(mcps)
# Kernel density estimaiton (KDE) -----------------------------------------
kde1 <- hr_kde(mini_deer)
hr_area(kde1)
get_crs(kde1)
# akde
data(deer)
mini_deer <- deer[1:20, ]
ud1 <- hr_akde(mini_deer) # uses an iid ctmm
ud2 <- hr_akde(mini_deer, model = fit_ctmm(mini_deer, "ou")) # uses an OU ctmm
# od
data(deer)
ud1 <- hr_od(deer) # uses an iid ctmm
ud2 <- hr_akde(deer, model = fit_ctmm(deer, "ou")) # uses an OU ctmm

Home-range area

Description

Obtain the area of a home-range estimate, possible at different isopleth levels.

Usage

hr_area(x, ...)
## S3 method for class 'hr'
hr_area(x, units = FALSE, ...)
## S3 method for class 'SpatRaster'
hr_area(x, level = 0.95, ...)
## S3 method for class 'akde'
hr_area(x, units = FALSE, ...)

Arguments

Value

A tibble with the home-range level and the area.

Home-range isopleths

Description

Obtain the isopleths of a home-range estimate, possible at different isopleth levels.

Usage

hr_isopleths(x, ...)
## S3 method for class 'PackedSpatRaster'
hr_isopleths(x, levels, descending = TRUE, ...)
## S3 method for class 'SpatRaster'
hr_isopleths(x, levels, descending = TRUE, ...)
## S3 method for class 'mcp'
hr_isopleths(x, descending = TRUE, ...)
## S3 method for class 'locoh'
hr_isopleths(x, descending = TRUE, ...)
## S3 method for class 'hr_prob'
hr_isopleths(x, descending = TRUE, ...)
## S3 method for class 'akde'
hr_isopleths(x, conf.level = 0.95, descending = TRUE, ...)

Arguments

Value

A tibble with the home-range level and a simple feature columns with the isoploth as multipolygon.

Least square cross validation bandwidth

Description

Use least square cross validation (lscv) to estimate bandwidth for kernel home-range estimation.

Usage

hr_kde_lscv(
 x,
 range = do.call(seq, as.list(c(hr_kde_ref(x) * c(0.1, 2), length.out = 100))),
 which_min = "global",
 rescale = "none",
 trast = make_trast(x)
)

Arguments

Details

hr_kde_lscv calculates least square cross validation bandwidth. This implementation is based on Seaman and Powell (1996). If whichMin is "global" the global minimum is returned, else the local minimum with the largest candidate bandwidth is returned.

Value

vector of length two.

References

Seaman, D. E., & Powell, R. A. (1996). An evaluation of the accuracy of kernel density estimators for home range analysis. Ecology, 77(7), 2075-2085.

Select a bandwidth for Kernel Density Estimation

Description

Use two dimensional reference bandwidth to select a bandwidth for kernel density estimation. Find the smallest value for bandwidth (h) that results in n polygons (usually n=1) contiguous polygons at a given level.

Usage

hr_kde_ref_scaled(
 x,
 range = hr_kde_ref(x)[1] * c(0.01, 1),
 trast = make_trast(x),
 num.of.parts = 1,
 levels = 0.95,
 tol = 0.1,
 max.it = 500L
)

Arguments

Details

This implementation uses a bisection algorithm to the find the smallest value value for the kernel bandwidth within range that produces an home-range isopleth at level consisting of n polygons. Note, no difference is is made between the two dimensions.

Value

list with the calculated bandwidth, exit status and the number of iteration.

References

Kie, John G. "A rule-based ad hoc method for selecting a bandwidth in kernel home-range analyses." Animal Biotelemetry 1.1 (2013): 1-12.

Calculate the overlap between a home-range estimate and a polygon

Description

Sometimes the percentage overlap between a spatial polygon an a home-range is required.

Usage

hr_overlap_feature(x, sf, direction = "hr_with_feature", feature_names = NULL)

Arguments

Value

A tibble

Methods to calculate home-range overlaps

Description

Methods to calculate the overlap of two or more home-range estimates.

Usage

hr_overlap(x, ...)
## S3 method for class 'hr'
hr_overlap(x, y, type = "hr", conditional = FALSE, ...)
## S3 method for class 'list'
hr_overlap(
 x,
 type = "hr",
 conditional = FALSE,
 which = "consecutive",
 labels = NULL,
 ...
)

Arguments

Value

data.frame with the isopleth level and area in units of the coordinate reference system.

Convert home ranges to sfc

Description

Convert a list column with many home-range estimates to a tibble with a geometry column (as used by the sf-package).

Usage

hr_to_sf(x, ...)
## S3 method for class 'tbl_df'
hr_to_sf(x, col, ...)

Arguments

Value

A data.frame with a simple feature column (from the sf) package.

Examples

data("amt_fisher")
hr <- amt_fisher |> nest(data = -id) |>
 mutate(hr = map(data, hr_mcp), n = map_int(data, nrow)) |>
 hr_to_sf(hr, id, n)
hr <- amt_fisher |> nest(data = -id) |>
 mutate(hr = map(data, hr_kde), n = map_int(data, nrow)) |>
 hr_to_sf(hr, id, n)

Obtain the utilization distribution of a probabilistic home-range estimate

Description

Obtain the utilization distribution of a probabilistic home-range estimate

Usage

hr_ud(x, ...)

Arguments

Value

SpatRaster

Inspect a track

Description

Provides a very basic interface to leaflet and lets the user inspect relocations on an interactive map.

Usage

inspect(x, ...)
## S3 method for class 'track_xy'
inspect(x, popup = NULL, cluster = TRUE, ...)

Arguments

Value

An interactive leaflet map.

Note

Important, x requires a valid coordinate reference system.

See Also

leaflet::leaflet()

Examples

data(sh)
x <- track(x = sh$x, y = sh$y, crs = 31467)
inspect(x)
inspect(x, cluster = FALSE)
inspect(x, popup = 1:nrow(x), cluster = FALSE)

Create formula without stratum from iSSF

Description

Creates a formula without stratum variable

Usage

issf_drop_stratum(object, l)

Arguments

Create habitat formula from iSSF

Description

Creates a formula without movement variables

Usage

issf_w_form(object, l)

Arguments

Calculate log-RSS for a fitted model

Description

Calculate log-RSS(x1, x2) for a fitted RSF or (i)SSF

Usage

log_rss(object, ...)
## S3 method for class 'glm'
log_rss(object, x1, x2, ci = NA, ci_level = 0.95, n_boot = 1000, ...)
## S3 method for class 'fit_clogit'
log_rss(object, x1, x2, ci = NA, ci_level = 0.95, n_boot = 1000, ...)

Arguments

Details

This function assumes that the user would like to compare relative selection strengths from at least one proposed location (x1) to exactly one reference location (x2).

The objects object$model, x1, and x2 will be passed to predict(). Therefore, the columns of x1 and x2 must match the terms in the model formula exactly.

Value

Returns a list of class log_rss with four entries:

• df: A data.frame with the covariates and the log_rss

• x1: A data.frame with covariate values for x1.

• x2: A data.frame with covariate values for x2.

• formula: The formula used to fit the model.

Author(s)

Brian J. Smith

References

• Avgar, T., Lele, S.R., Keim, J.L., and Boyce, M.S.. (2017). Relative Selection Strength: Quantifying effect size in habitat- and step-selection inference. Ecology and Evolution, 7, 5322–5330.

• Fieberg, J., Signer, J., Smith, B., & Avgar, T. (2021). A "How to" guide for interpreting parameters in habitat-selection analyses. Journal of Animal Ecology, 90(5), 1027-1043.

See Also

See Avgar et al. 2017 for details about relative selection strength.

Default plotting method available: plot.log_rss()

Examples

# RSF -------------------------------------------------------
# Fit an RSF, then calculate log-RSS to visualize results.
# Load packages
library(ggplot2)
# Load data
data("amt_fisher")
amt_fisher_covar <- get_amt_fisher_covars()
# Prepare data for RSF
rsf_data <- amt_fisher |>
 filter(name == "Lupe") |>
 make_track(x_, y_, t_) |>
 random_points() |>
 extract_covariates(amt_fisher_covar$elevation) |>
 extract_covariates(amt_fisher_covar$popden) |>
 extract_covariates(amt_fisher_covar$landuse) |>
 mutate(lu = factor(landuse))
# Fit RSF
m1 <- rsf_data |>
 fit_rsf(case_ ~ lu + elevation + popden)
# Calculate log-RSS
# data.frame of x1s
x1 <- data.frame(lu = factor(50, levels = levels(rsf_data$lu)),
 elevation = seq(90, 120, length.out = 100),
 popden = mean(rsf_data$popden))
# data.frame of x2 (note factor levels should be same as model data)
x2 <- data.frame(lu = factor(50, levels = levels(rsf_data$lu)),
 elevation = mean(rsf_data$elevation),
 popden = mean(rsf_data$popden))
# Calculate (use se method for confidence interval)
logRSS <- log_rss(object = m1, x1 = x1, x2 = x2, ci = "se")
# Plot
ggplot(logRSS$df, aes(x = elevation_x1, y = log_rss)) +
 geom_hline(yintercept = 0, linetype = "dashed", color = "gray") +
 geom_ribbon(aes(ymin = lwr, ymax = upr), fill = "gray80") +
 geom_line() +
 xlab(expression("Elevation " * (x[1]))) +
 ylab("log-RSS") +
 ggtitle(expression("log-RSS" * (x[1] * ", " * x[2]))) +
 theme_bw()
# SSF -------------------------------------------------------
# Fit an SSF, then calculate log-RSS to visualize results.
# Load data
data(deer)
sh_forest <- get_sh_forest()
# Prepare data for SSF
ssf_data <- deer |>
 steps_by_burst() |>
 random_steps(n = 15) |>
 extract_covariates(sh_forest) |>
 mutate(forest = factor(forest, levels = 1:0,
 labels = c("forest", "non-forest")),
 cos_ta = cos(ta_),
 log_sl = log(sl_))
# Fit an SSF (note model = TRUE necessary for predict() to work)
m2 <- ssf_data |>
 fit_clogit(case_ ~ forest + strata(step_id_), model = TRUE)
# Calculate log-RSS
# data.frame of x1s
x1 <- data.frame(forest = factor(c("forest", "non-forest")))
# data.frame of x2
x2 <- data.frame(forest = factor("forest", levels = levels(ssf_data$forest)))
# Calculate
logRSS <- log_rss(object = m2, x1 = x1, x2 = x2, ci = "se")
# Plot
ggplot(logRSS$df, aes(x = forest_x1, y = log_rss)) +
 geom_hline(yintercept = 0, linetype = "dashed", color = "gray") +
 geom_errorbar(aes(ymin = lwr, ymax = upr), width = 0.25) +
 geom_point(size = 3) +
 xlab(expression("Forest Cover " * (x[1]))) +
 ylab("log-RSS") +
 ggtitle(expression("log-RSS" * (x[1] * ", " * x[2]))) +
 theme_bw()

Create an issf-model object from scratch

Description

In order to simulate from an issf a

Usage

make_issf_model(
 coefs = c(sl_ = 0),
 sl = make_exp_distr(),
 ta = make_unif_distr()
)

Arguments

Value

An object of fit_clogit.

Create an initial step for simulations

Description

An initial step for simulations. This step can either be created by defining a step from scratch or by using an observed step.

Usage

make_start(x, ...)
## S3 method for class 'numeric'
make_start(
 x = c(0, 0),
 ta_ = 0,
 time = Sys.time(),
 dt = hours(1),
 crs = NA,
 ...
)
## S3 method for class 'track_xyt'
make_start(x, ta_ = 0, dt = hours(1), ...)
## S3 method for class 'steps_xyt'
make_start(x, ...)

Arguments

Movement metrics

Description

Functions to calculate metrics such as straightness, mean squared displacement (msd), intensity use, sinuosity, mean turn angle correlation (tac) of a track.

Usage

straightness(x, ...)
cum_dist(x, ...)
tot_dist(x, ...)
msd(x, ...)
intensity_use(x, ...)
sinuosity(x, ...)
tac(x, ...)

Arguments

Details

The intensity use is calculated by dividing the total movement distance (tot_dist) by the square of the area of movement (= minimum convex polygon 100).

Value

A numeric vector of length one.

References

• Abrahms B, Seidel DP, Dougherty E, Hazen EL, Bograd SJ, Wilson AM, McNutt JW, Costa DP, Blake S, Brashares JS, others (2017). “Suite of simple metrics reveals common movement syndromes across vertebrate taxa.” Movement ecology, 5(1), 12.

• Almeida PJ, Vieira MV, Kajin M, Forero-Medina G, Cerqueira R (2010). “Indices of movement behaviour: conceptual background, effects of scale and location errors.” Zoologia (Curitiba), 27(5), 674–680.

• Swihart RK, Slade NA (1985). “Testing for independence of observations in animal movements.” Ecology, 66(4), 1176–1184.

Examples

data(deer)
tot_dist(deer)
cum_dist(deer)
straightness(deer)
msd(deer)
intensity_use(deer)

Net squared displacement (nsd)

Description

The function nsd() calculates the net squared displacement (i.e., the squared distance from the first location of a track) for a track. The function add_nsd() add a new column to a track or steps object with the nsd (the column name is nsd_).

Usage

nsd(x, ...)
## S3 method for class 'track_xy'
nsd(x, ...)
add_nsd(x, ...)
## S3 method for class 'track_xy'
add_nsd(x, ...)
## S3 method for class 'steps_xy'
add_nsd(x, ...)

Arguments

Value

Numeric vector (for nsd()) and a tillbe with the original data with a new column for add_nsd().

Occurrence Distribution

Description

od is a wrapper around ctmm::occurrence. See help(ctmm::occurrence) for more details. rolling_od estimates occurrence distributions for a subset of a track.

Usage

rolling_od(x, ...)
## S3 method for class 'track_xyt'
rolling_od(
 x,
 trast,
 model = fit_ctmm(x, "bm"),
 res.space = 10,
 res.time = 10,
 n.points = 5,
 show.progress = TRUE,
 ...
)
od(x, ...)
## S3 method for class 'track_xyt'
od(x, trast, model = fit_ctmm(x, "bm"), res.space = 10, res.time = 10, ...)

Arguments

References

Fleming, C. H., Fagan, W. F., Mueller, T., Olson, K. A., Leimgruber, P., & Calabrese, J. M. (2016). Estimating where and how animals travel: an optimal framework for path reconstruction from autocorrelated tracking data. Ecology.

Examples

data(deer)
mini_deer <- deer[1:100, ]
trast <- make_trast(mini_deer)
md <- od(mini_deer, trast = trast)
terra::plot(md)
# rolling ud
xx <- rolling_od(mini_deer, trast)

Get parameters from a (fitted) distribution

Description

Get parameters from a (fitted) distribution

Usage

sl_distr_params(x, ...)
## S3 method for class 'random_steps'
sl_distr_params(x, ...)
## S3 method for class 'fit_clogit'
sl_distr_params(x, ...)
ta_distr_params(x, ...)
## S3 method for class 'random_steps'
ta_distr_params(x, ...)
## S3 method for class 'fit_clogit'
ta_distr_params(x, ...)

Arguments

Value

A list with the parameters of the distribution.

Examples

data(deer)
d <- deer |> steps() |> random_steps()
sl_distr_params(d)
ta_distr_params(d)

Plot a home-range estimate

Description

Plot a home-range estimate

Usage

## S3 method for class 'hr'
plot(x, add.relocations = TRUE, ...)

Arguments

Value

A plot

Plot a log_rss object

Description

Default plot method for an object of class log_rss

Usage

## S3 method for class 'log_rss'
plot(x, x_var1 = "guess", x_var2 = "guess", ...)

Arguments

Details

This function provides defaults for a basic plot, but we encourage the user to carefully consider how to represent the patterns found in their habitat selection model.

The function log_rss() is meant to accept a user-defined input for x1. The structure of x1 likely reflects how the user intended to visualize the results. Therefore, it is possible to "guess" which covariate the user would like to see on the x-axis by choosing the column from x1 with the most unique values. Similarly, if there is a second column with multiple unique values, that could be represented by a color. Note that if the user needs to specify x_var1, then we probably cannot guess x_var2. Therefore, if the user specifies x_var1 != "guess" & x_var2 == "guess", the function will return an error.

This function uses integers to represent colors, and therefore the user can change the default colors by specifying a custom palette() before calling the function.

Value

A plot.

Examples

# Load data
data("amt_fisher")
amt_fisher_covar <- get_amt_fisher_covars()
# Prepare data for RSF
rsf_data <- amt_fisher |>
 filter(name == "Leroy") |>
 make_track(x_, y_, t_) |>
 random_points() |>
 extract_covariates(amt_fisher_covar$landuse) |>
 mutate(lu = factor(landuse))
# Fit RSF
m1 <- rsf_data |>
 fit_rsf(case_ ~ lu)
# Calculate log-RSS
# data.frame of x1s
x1 <- data.frame(lu = sort(unique(rsf_data$lu)))
# data.frame of x2 (note factor levels should be same as model data)
x2 <- data.frame(lu = factor(140,
levels = levels(rsf_data$lu)))
# Calculate
logRSS <- log_rss(object = m1, x1 = x1, x2 = x2)
# Plot
plot(logRSS)

Plot UHC plots

Description

Plot an object of class uhc_data

Usage

## S3 method for class 'uhc_data'
plot(x, ...)

Arguments

Details

Makes plots mimicking those in Fieberg et al. (2018), with the bootstrapped distribution in gray, the observed distribution in black, and the available distribution as a dashed red line.

Author(s)

Brian J. Smith

See Also

prep_uhc(), conf_envelope(), plot.uhc_envelopes()

Plot simplified UHC plots

Description

Plot an object of class uhc_envelopes

Usage

## S3 method for class 'uhc_envelopes'
plot(x, ...)

Arguments

Details

Makes plots mimicking those in Fieberg et al. (2018), with the bootstrapped distribution in gray, the observed distribution in black, and the available distribution as a dashed red line. This differs from plot.uhc_data() in that the bootstrapped distribution (in gray) is drawn as a polygon rather than (many) lines, speeding up plotting performance.

Author(s)

Brian J. Smith

See Also

prep_uhc(), conf_envelope(), plot.uhc_data()

Plot step-length distribution

Description

Plot step-length distribution

Usage

plot_sl(x, ...)
## S3 method for class 'fit_clogit'
plot_sl(x, n = 1000, upper_quantile = 0.99, plot = TRUE, ...)
## S3 method for class 'random_steps'
plot_sl(x, n = 1000, upper_quantile = 0.99, plot = TRUE, ...)

Arguments

Value

A plot of the step-length distribution.

Examples

data(deer)
# with random steps
deer[1:100, ] |> steps_by_burst() |> random_steps() |> plot_sl()
deer[1:100, ] |> steps_by_burst() |> random_steps() |> plot_sl(upper_quantile = 0.5)

Prepares test_dat for uhc_prep()

Description

Internal function to check and format test_dat

Usage

prep_test_dat(object, test_dat, verbose = TRUE)

Arguments

Prepare Data for UHC Plots for a Fitted Model

Description

Creates data used to make used-habitat calibration plots

Usage

prep_uhc(object, test_dat, n_samp = 1000, n_dens = 512, verbose = TRUE)
## S3 method for class 'glm'
prep_uhc(object, test_dat, n_samp = 1000, n_dens = 512, verbose = TRUE)
## S3 method for class 'fit_logit'
prep_uhc(object, test_dat, n_samp = 1000, n_dens = 512, verbose = TRUE)
## S3 method for class 'fit_clogit'
prep_uhc(object, test_dat, n_samp = 1000, n_dens = 512, verbose = TRUE)

Arguments

Details

This function performs the heavy lifting of creating UHC plots. It creates the data used later by the plot() method, which actually draws the UHC plots. This function (1) creates density plots of the used and available locations from the test data, and (2) resamples the (a) fitted coefficients and (b) test data (weighted by the exponential habitat selection function) to create the distribution of used habitat under the model.

Note that test_dat should contain at least all of the variables that appear in the model object. Any further habitat variables in test_dat will also have UHC plots generated, treating these variables as possible candidate variables that are simply not included in this particular model.

Value

Returns a list of class uhc_data with elements:

• orig: List of data.frames, one per variable (see vars). Each data.frame contains the density plot data (x and y) for the original used (dist == "U") and available (dist == "A") data.

• samp: List of data.frames, one per variable (see vars). Each data.frame contains the density plot data (x and y) for each iteration of bootstrap resampling (iter).

• vars: Character vector with names of the habitat variables for which to create UHC plots.

• type: Named character vector with the type for each of vars (either "numeric" or "factor").

• resp: Character vector of length 1 with the name of the response variable.

Author(s)

Brian J. Smith

References

Fieberg, J.R., Forester, J.D., Street, G.M., Johnson, D.H., ArchMiller, A.A., and Matthiopoulos, J. 2018. Used-habitat calibration plots: A new procedure for validating species distribution, resource selection, and step-selection models. Ecography 41:737–752.

See Also

See Fieberg et al. 2018 for details about UHC plots.

Default plotting method available: plot.uhc_data()

Coercion to data.frame: as.data.frame.uhc_data()

Subsetting method: Extract.uhc_data

Examples

# Load packages
library(amt)
library(dplyr)
library(terra)
library(sf)
# HSF ----------------------------------------------
# Load data
data(uhc_hsf_locs)
data(uhc_hab)
hab <- rast(uhc_hab, type = "xyz", crs = "epsg:32612")
# Convert "cover" layer to factor
levels(hab[[4]]) <- data.frame(id = 1:3,
 cover = c("grass", "forest", "wetland"))
# Split into train (80%) and test (20%)
set.seed(1)
uhc_hsf_locs$train <- rbinom(n = nrow(uhc_hsf_locs),
 size = 1, prob = 0.8)
train <- uhc_hsf_locs[uhc_hsf_locs$train == 1, ]
test <- uhc_hsf_locs[uhc_hsf_locs$train == 0, ]
# Available locations
avail_train <- random_points(st_as_sf(st_as_sfc(st_bbox(hab))),
 n = nrow(train) * 10)
avail_test <- random_points(st_as_sf(st_as_sfc(st_bbox(hab))),
 n = nrow(test) * 10)
# Combine with used
train_dat <- train |>
 make_track(x, y, crs = 32612) |>
 mutate(case_ = TRUE) |>
 bind_rows(avail_train) |>
 # Attach covariates
 extract_covariates(hab) |>
 # Assign large weights to available
 mutate(weight = case_when(
 case_ ~ 1,
 !case_ ~ 5000
 ))
test_dat <- test |>
 make_track(x, y, crs = 32612) |>
 mutate(case_ = TRUE) |>
 bind_rows(avail_test) |>
 # Attach covariates
 extract_covariates(hab) |>
 # Assign large weights to available
 mutate(weight = case_when(
 case_ ~ 1,
 !case_ ~ 5000
 ))
# Fit (correct) HSF
hsf1 <- glm(case_ ~ forage + temp + I(temp^2) + pred + cover,
 data = train_dat, family = binomial(), weights = weight)
# Drop weights from 'test_dat'
test_dat$weight <- NULL
# Prep UHC plots
uhc_dat <- prep_uhc(object = hsf1, test_dat = test_dat,
 n_samp = 500, verbose = TRUE)
# Plot all variables
plot(uhc_dat)
# Plot only first variable
plot(uhc_dat[1])
# Plot only "cover" variable
plot(uhc_dat["cover"])
# Coerce to data.frame
df <- as.data.frame(uhc_dat)
# Simplify sampled lines to confidence envelopes
conf <- conf_envelope(df)
# Default plot for the envelopes version
plot(conf)

Sample random numbers

Description

Sample random numbers from a distribution created within the amt package.

Usage

random_numbers(x, n = 100, ...)

Arguments

Value

A numeric vector.

Generate random points

Description

Functions to generate random points within an animals home range. This is usually the first step for investigating habitat selection via Resource Selection Functions (RSF).

Usage

random_points(x, ...)
## S3 method for class 'hr'
random_points(x, n = 100, type = "random", presence = NULL, ...)
## S3 method for class 'sf'
random_points(x, n = 100, type = "random", presence = NULL, ...)
## S3 method for class 'track_xy'
random_points(x, level = 1, hr = "mcp", n = nrow(x) * 10, type = "random", ...)

Arguments

Value

A tibble with the observed and random points and a new column case_ that indicates if a point is observed (case_ = TRUE) or random (⁠case_ TRUE⁠).

Note

For objects of class track_xyt the timestamp (t_) is lost.

Examples

data(deer)
# track_xyt ---------------------------------------------------------------
# Default settings
rp1 <- random_points(deer)
plot(rp1)
# Ten random points for each observed point
rp <- random_points(deer, n = nrow(deer) * 10)
plot(rp)
# Within a home range -----------------------------------------------------
hr <- hr_mcp(deer, level = 1)
# 100 random point within the home range
rp <- random_points(hr, n = 100)
plot(rp)
# 100 regular point within the home range
rp <- random_points(hr, n = 100, type = "regular")
plot(rp)
# 100 hexagonal point within the home range
rp <- random_points(hr, n = 100, type = "hexagonal")
plot(rp)

Generate Random Steps

Description

Function to generate a given number of random steps for each observed step.

Usage

random_steps(x, ...)
## S3 method for class 'numeric'
random_steps(
 x,
 n_control = 10,
 angle = 0,
 rand_sl = random_numbers(make_exp_distr(), n = 1e+05),
 rand_ta = random_numbers(make_unif_distr(), n = 1e+05),
 ...
)
## S3 method for class 'steps_xy'
random_steps(
 x,
 n_control = 10,
 sl_distr = fit_distr(x$sl_, "gamma"),
 ta_distr = fit_distr(x$ta_, "vonmises"),
 rand_sl = random_numbers(sl_distr, n = 1e+05),
 rand_ta = random_numbers(ta_distr, n = 1e+05),
 include_observed = TRUE,
 start_id = 1,
 ...
)
## S3 method for class 'bursted_steps_xyt'
random_steps(
 x,
 n_control = 10,
 sl_distr = fit_distr(x$sl_, "gamma"),
 ta_distr = fit_distr(x$ta_, "vonmises"),
 rand_sl = random_numbers(sl_distr, n = 1e+05),
 rand_ta = random_numbers(ta_distr, n = 1e+05),
 include_observed = TRUE,
 ...
)

Arguments

Value

A tibble of class random_steps.

Simulate from an ssf model

Description

Simulate from an ssf model

Usage

random_steps_simple(start, sl_model, ta_model, n.control)

Arguments

Value

Simulated trajectory

Geographic range

Description

The range that in either x, y or both directions, that a track covers.

Usage

range_x(x, ...)
## S3 method for class 'track_xy'
range_x(x, ...)
range_y(x, ...)
## S3 method for class 'track_xy'
range_y(x, ...)
range_both(x, ...)
## S3 method for class 'track_xy'
range_both(x, ...)

Arguments

Value

Numeric vector with the range.

Create a redistribution kernel

Description

From a fitted integrated step-selection function for a given position a redistribution kernel is calculated (i.e., the product of the movement kernel and the selection function).

Usage

redistribution_kernel(
 x = make_issf_model(),
 start = make_start(),
 map,
 fun = function(xy, map) {
 extract_covariates(xy, map, where = "both")
 },
 covars = NULL,
 max.dist = get_max_dist(x),
 n.control = 1e+06,
 n.sample = 1,
 landscape = "continuous",
 compensate.movement = landscape == "discrete",
 normalize = TRUE,
 interpolate = FALSE,
 as.rast = FALSE,
 tolerance.outside = 0
)

Arguments

Objects exported from other packages

Description

These objects are imported from other packages. Follow the links below to see their documentation.

dplyr

arrange , distinct , filter , group_by , mutate , pull , select , summarise , summarize , ungroup , ungroup

lubridate

days , hours , minutes , seconds , weeks

purrr

map , map_int

sf

NA_crs_

survival

clogit , strata , Surv

tibble

tibble

tidyr

nest , unnest

Removes Capture Effects

Description

Removing relocations at the beginning and/or end of a track, that fall within a user specified period.

Usage

remove_capture_effect(x, ...)
## S3 method for class 'track_xyt'
remove_capture_effect(x, start, end, ...)

Arguments

Value

A tibble without observations that fall within the period of the capture effect.

Remove strata with missing values for observed steps

Description

Remove strata with missing values for observed steps

Usage

remove_incomplete_strata(x, ...)
## S3 method for class 'random_steps'
remove_incomplete_strata(x, col = "ta_", ...)

Arguments

Value

An object of class random_steps, where observed steps (case_ == TRUE) with missing values (NA) in the column col are removed (including all random steps).

Examples

mini_deer <- deer[1:4, ]
# The first step is removed, because we have `NA` turn angles.
mini_deer |> steps() |> random_steps() |> remove_incomplete_strata() |>
 select(case_, ta_, step_id_)

Extract sampling period

Description

Extracts sampling period from a track_xyt object

Usage

sampling_period(x, ...)

Arguments

Calculate SDR for an Object

Description

Calculates SDR for an object of certain class

Usage

sdr(x, time_unit = "secs", append_na = TRUE, ...)
## S3 method for class 'track_xyt'
sdr(x, time_unit = "secs", append_na = TRUE, ...)

Arguments

Details

time_unit defaults to seconds because calculate_sdr() returns SDR in m^2/s. We assume the user is also working in a projected CRS with units in meters, thus we expect SDR in m^2/s to be most relevant.

Author(s)

Brian J. Smith and Johannes Signer

See Also

calculate_sdr(), get_displacement()

Relocations of one Red Deer

Description

1500 GPS fixes of one red deer from northern Germany.

Usage

sh

Format

A data frame with 1500 rows and 4 variables:

x_epsg31467

the x-coordinate

y_epsg31467

the y-coordinate

day

the day of the relocation

time

the hour of the relocation

Source

Verein für Wildtierforschung Dresden und Göttingen e.V.

Forest cover

Description

Forest cover for the home range of one red deer from northern Germany.

Usage

sh_forest

Format

A SpatRast

0

other

1

forest

Source

JRC

References

A. Pekkarinen, L. Reithmaier, P. Strobl (2007): Pan-European Forest/Non-Forest mapping with Landsat ETM+ and CORINE Land Cover 2000 data.

Simulate a movement trajectory.

Description

Function to simulate a movement trajectory (path) from a redistribution kernel.

Usage

simulate_path(x, ...)
## Default S3 method:
simulate_path(x, ...)
## S3 method for class 'redistribution_kernel'
simulate_path(x, n.steps = 100, start = x$args$start, verbose = FALSE, ...)

Arguments

Test for site fidelity of animal movement.

Description

Calculates two indices (mean squared displacement and linearity) to test for site fidelity. Significance testing is done by permuting step lengths and drawing turning angles from a uniform distribution.

Usage

site_fidelity(x, ...)
## S3 method for class 'steps_xy'
site_fidelity(x, n = 100, alpha = 0.05, ...)

Arguments

Value

A list of length 4. msd_dat and li_dat is the mean square distance and linearity for the real date. msd_sim and 'li_sim“ are the mean square distances and linearities for the simulated trajectories.

References

Spencer, S. R., Cameron, G. N., & Swihart, R. K. (1990). Operationally defining home range: temporal dependence exhibited by hispid cotton rats. Ecology, 1817-1822.

Examples

# real data
data(deer)
ds <- deer |> steps_by_burst()
site_fidelity(ds)

Speed

Description

Obtain the speed of a track.

Usage

speed(x, ...)
## S3 method for class 'track_xyt'
speed(x, append_na = TRUE, ...)

Arguments

Value

⁠[numeric]⁠
The speed in m/s.

Takes a clogit formula and returns a formula without the strata and the left-hand side

Description

Takes a clogit formula and returns a formula without the strata and the left-hand side

Usage

ssf_formula(formula)

Arguments

Examples

f1 <- case_ ~ x1 * x2 + strata(step_id_)
ssf_formula(f1)

Given a fitted ssf, and new location the weights for each location is calculated

Description

Given a fitted ssf, and new location the weights for each location is calculated

Usage

ssf_weights(xy, object, compensate.movement = FALSE)

Arguments

Functions to create and work with steps

Description

step_lengths can be use to calculate step lengths of a track. direction_abs and direction_rel calculate the absolute and relative direction of steps. steps converts a ⁠track_xy*⁠ from a point representation to a step representation and automatically calculates step lengths and relative turning angles.

Usage

direction_abs(x, ...)
## S3 method for class 'track_xy'
direction_abs(
 x,
 full_circle = FALSE,
 zero_dir = "E",
 clockwise = FALSE,
 append_last = TRUE,
 lonlat = FALSE,
 ...
)
direction_rel(x, ...)
## S3 method for class 'track_xy'
direction_rel(x, lonlat = FALSE, append_last = TRUE, zero_dir = "E", ...)
step_lengths(x, ...)
## S3 method for class 'track_xy'
step_lengths(x, lonlat = FALSE, append_last = TRUE, ...)
steps_by_burst(x, ...)
## S3 method for class 'track_xyt'
steps_by_burst(x, lonlat = FALSE, keep_cols = NULL, ...)
steps(x, ...)
## S3 method for class 'track_xy'
steps(x, lonlat = FALSE, keep_cols = NULL, ...)
## S3 method for class 'track_xyt'
steps(x, lonlat = FALSE, keep_cols = NULL, diff_time_units = "auto", ...)

Arguments

Details

⁠dierctions_*()⁠ returns NA for 0 step lengths.

step_lengths calculates the step lengths between points a long the path. The last value returned is NA, because no observed step is 'started' at the last point. If lonlat = TRUE, step_lengths() wraps sf::st_distance() .

Value

⁠[numeric]⁠
For step_lengths() and ⁠direction_*⁠ a numeric vector.
⁠[data.frame]⁠
For steps and steps_by_burst, containing the steps.

Examples

xy <- tibble(
 x = c(1, 4, 8, 8, 12, 12, 8, 0, 0, 4, 2),
 y = c(0, 0, 0, 8, 12, 12, 12, 12, 8, 4, 2))
trk <- make_track(xy, x, y)
# append last
direction_abs(trk, append_last = TRUE)
direction_abs(trk, append_last = FALSE)
# degrees
direction_abs(trk) |> as_degree()
# full circle or not: check
direction_abs(trk, full_circle = TRUE)
direction_abs(trk, full_circle = FALSE)
direction_abs(trk, full_circle = TRUE) |> as_degree()
direction_abs(trk, full_circle = FALSE) |> as_degree()
# direction of 0
direction_abs(trk, full_circle = TRUE, zero_dir = "N")
direction_abs(trk, full_circle = TRUE, zero_dir = "E")
direction_abs(trk, full_circle = TRUE, zero_dir = "S")
direction_abs(trk, full_circle = TRUE, zero_dir = "W")
# clockwise or not
direction_abs(trk, full_circle = TRUE, zero_dir = "N", clockwise = FALSE)
direction_abs(trk, full_circle = TRUE, zero_dir = "N", clockwise = TRUE)
# Bearing (i.e. azimuth): only for lon/lat
direction_abs(trk, full_circle = FALSE, zero_dir = "N", lonlat = FALSE, clockwise = TRUE)
direction_abs(trk, full_circle = FALSE, zero_dir = "N", lonlat = TRUE, clockwise = TRUE)

Returns a summary of sampling rates

Description

Returns a summary of sampling rates

Usage

summarize_sampling_rate(x, ...)
## S3 method for class 'track_xyt'
summarize_sampling_rate(
 x,
 time_unit = "auto",
 summarize = TRUE,
 as_tibble = TRUE,
 ...
)
summarize_sampling_rate_many(x, ...)
## S3 method for class 'track_xyt'
summarize_sampling_rate_many(x, cols, time_unit = "auto", ...)

Arguments

Value

Depending on summarize and as_tibble, a vector, table or tibble.

Examples

data(deer)
amt::summarize_sampling_rate(deer)
data(amt_fisher)
# Add the month
amt_fisher |> mutate(yday = lubridate::yday(t_)) |>
summarize_sampling_rate_many(c("id", "yday"))

Summarize step lengths

Description

Summarizes step lengths for a ⁠track_xy*⁠ object

Usage

summarize_sl(x, ...)

Arguments

Summarize speed

Description

Summarizes speeds for a track_xyt object

Usage

summarize_speed(x, ...)

Arguments

Time of the day when a fix was taken

Description

A convenience wrapper around suncalc::getSunlightTimes to annotate if a fix was taken during day or night (optionally also include dawn and dusk).

Usage

time_of_day(x, ...)
## S3 method for class 'track_xyt'
time_of_day(x, include.crepuscule = FALSE, ...)
## S3 method for class 'steps_xyt'
time_of_day(x, include.crepuscule = FALSE, where = "end", ...)

Arguments

Value

A tibble with an additional column tod_ that contains the time of the day for each relocation.

Examples

data(deer)
deer |> time_of_day()
deer |> steps_by_burst() |> time_of_day()
deer |> steps_by_burst() |> time_of_day(where = "start")
deer |> steps_by_burst() |> time_of_day(where = "end")
deer |> steps_by_burst() |> time_of_day(where = "both")

Create a ⁠track_*⁠

Description

Constructor to crate a track, the basic building block of the amt package. A track is usually created from a set of x and y coordinates, possibly time stamps, and any number of optional columns, such as id, sex, age, etc.

Usage

mk_track(
 tbl,
 .x,
 .y,
 .t,
 ...,
 crs = NA_crs_,
 order_by_ts = TRUE,
 check_duplicates = FALSE,
 all_cols = FALSE,
 verbose = FALSE
)
make_track(
 tbl,
 .x,
 .y,
 .t,
 ...,
 crs = NA_crs_,
 order_by_ts = TRUE,
 check_duplicates = FALSE,
 all_cols = FALSE,
 verbose = FALSE
)
track(x, y, t, ..., crs = NULL)

Arguments

Value

If t was provided an object of class track_xyt is returned otherwise a track_xy.

Selects relocations that fit a new time series

Description

Functions to only selects relocations that can be aligned with a new time series (within some tolerance).

Usage

track_align(x, ...)
## S3 method for class 'track_xyt'
track_align(x, new.times, tolerance, ...)

Arguments

Value

A track_xyt.

Resample track

Description

Function to resample a track at a predefined sampling rate within some tolerance.

Usage

track_resample(x, ...)
## S3 method for class 'track_xyt'
track_resample(x, rate = hours(2), tolerance = minutes(15), start = 1, ...)

Arguments

Value

A resampled track_xyt.

Subset to tracking dates

Description

Subsets a track_xyt object

Usage

tracked_from_to(x, from = min(x$t_), to = max(x$t_))

Arguments

Transform CRS

Description

Transforms the CRS for a track.

Usage

transform_coords(x, ...)
## S3 method for class 'track_xy'
transform_coords(x, crs_to, crs_from, ...)
transform_crs(x, ...)

Arguments

Value

A track with transformed coordinates.

See Also

sf::st_transform

Examples

data(deer)
get_crs(deer)
# project to geographical coordinates (note the CRS is taken automatically from the object deer).
d1 <- transform_coords(deer, crs_to = 4326)

Create a template raster layer

Description

For some home-range estimation methods (e.g., KDE) a template raster is needed. This functions helps to quickly create such a template raster.

Usage

make_trast(x, ...)
## S3 method for class 'track_xy'
make_trast(x, factor = 1.5, res = max(c(extent_max(x)/100, 1e-09)), ...)

Arguments

Value

A RastLayer without values.

Summarize distribution of used and available

Description

Internal function to summarize distribution of numeric or factor variables

Usage

ua_distr(name, type, data, lims, resp, n_dens, avail = TRUE)

Arguments

Simulated habitat rasters for demonstrating UHC plots

Description

Simulated habitat rasters for demonstrating UHC plots

Usage

uhc_hab

Format

A RasterStack with 1600 cells and 7 variables:

forage

Forage biomass in g/m^2^ (resource)

temp

mean annual temperature in °C (condition)

pred

predator density in predators/100 km^2^ (risk)

cover

landcover (forest > grassland > wetland)

dist_to_water

distance to the wetland landcover (no effect)

dist_to_cent

distance to the centroid of the raster (no effect)

rand

random integers (no effect)

Simulated HSF location data for demonstrating UHC plots

Description

Simulated HSF location data for demonstrating UHC plots

Usage

uhc_hsf_locs

Format

A data.frame with 2000 rows and 2 variables:

x

x-coordinate in UTM Zone 12 (EPSG: 32612)

y

Y-coordinate in UTM Zone 12 (EPSG: 32612)

These data were simulated assuming an ordinary habitat selection function (HSF), i.e., all points are independent rather than arising from an underlying movement model.

True parameter values are:

• forage = log(5)/500 (resource)

• temp^2 = -1 * log(2)/36 (condition; quadratic term)

• temp = (log(2)/36) * 26 (condition; linear term)

• pred = log(0.25)/5 (risk)

• cover == "forest" = log(2) (grassland is intercept)

• cover == "wetland" = log(1/2) (grassland is intercept)

Note: temp is modeled as a quadratic term, with the strongest selection occurring at 13 °C and all other temperatures less selected.

Note: dist_to_water, dist_to_cent, and rand have no real effect on our animal's selection and are included for demonstration purposes.

Simulated iSSF location data for demonstrating UHC plots

Description

Simulated iSSF location data for demonstrating UHC plots

Usage

uhc_issf_locs

Format

A data.frame with 371 rows and 3 variables:

x

x-coordinate in UTM Zone 12 (EPSG: 32612)

y

Y-coordinate in UTM Zone 12 (EPSG: 32612)

t

timestamp of location (timezone "US/Mountain")

These data were simulated assuming an movement model, i.e., iSSA.

True movement-free habitat selection parameter values are:

• forage = log(8)/500 (resource)

• temp^2 = -1 * log(8)/36 (condition; quadratic term)

• temp = (log(8)/36) * 26 (condition; linear term)

• pred = log(0.2)/5 (risk)

• cover == "forest" = log(2) (grassland is intercept)

• cover == "wetland" = log(1/2) (grassland is intercept)

• dist_to_cent = -1 * log(10)/500 (keeps trajectory away from boundary)

Note: temp is modeled as a quadratic term, with the strongest selection occurring at 13 °C and all other temperatures less selected.

Note: dist_to_water and rand have no real effect on our animal's selection and are included for demonstration purposes.

True selection-free movement distributions are:

• Step length: gamma(shape = 3, scale = 25)

• Turn angle: vonMises(mu = 0, kappa = 0.5)

Manually update amt_distr

Description

Functions to update amt_distr from iSSF coefficients

Usage

update_gamma(dist, beta_sl, beta_log_sl)
update_exp(dist, beta_sl)
update_hnorm(dist, beta_sl_sq)
update_lnorm(dist, beta_log_sl, beta_log_sl_sq)
update_vonmises(dist, beta_cos_ta)

Arguments

Details

These functions are called internally by update_sl_distr() and update_ta_distr(). However, those simple functions assume that the selection-free step-length and turn-angle distributions are constant (i.e., they do not depend on covariates). In the case of interactions between movement parameters and covariates, the user will want to manually access these functions to update their selection-free movement distributions.

Value

A distribution

Examples

# sh_forest <- get_sh_forest()
# # Fit an SSF, then update movement parameters.
#
# #Prepare data for SSF
# ssf_data <- deer |>
# steps_by_burst() |>
# random_steps(n = 15) |>
# extract_covariates(sh_forest) |>
# mutate(forest = factor(forest, levels = 1:0,
# labels = c("forest", "non-forest")),
# cos_ta_ = cos(ta_),
# log_sl_ = log(sl_))
#
# # Check tentative distributions
# # Step length
# attr(ssf_data, "sl_")
# # Turning angle
# attr(ssf_data, "ta_")
#
# # Fit an iSSF (note model = TRUE necessary for predict() to work)
# m1 <- ssf_data |>
# fit_issf(case_ ~ forest * (sl_ + log_sl_ + cos_ta_) +
# strata(step_id_), model = TRUE)
#
# # Update forest step lengths (the reference level)
# forest_sl <- update_gamma(m1$sl_,
# beta_sl = m1$model$coefficients["sl_"],
# beta_log_sl = m1$model$coefficients["log_sl_"])
#
# # Update non-forest step lengths
# nonforest_sl <- update_gamma(m1$sl_,
# beta_sl = m1$model$coefficients["sl_"] +
# m1$model$coefficients["forestnon-forest:sl_"],
# beta_log_sl = m1$model$coefficients["log_sl_"] +
# m1$model$coefficients["forestnon-forest:log_sl_"])
#
# # Update forest turn angles (the reference level)
# forest_ta <- update_vonmises(m1$ta_,
# beta_cos_ta = m1$model$coefficients["cos_ta_"])
#
# # Update non-forest turn angles
# nonforest_ta <- update_vonmises(m1$ta_,
# beta_cos_ta = m1$model$coefficients["cos_ta_"] +
# m1$model$coefficients["forestnon-forest:cos_ta_"])
#

Update movement distributions

Description

Update tentative step length or turning angle distribution from a fitted iSSF.

Usage

update_sl_distr(
 object,
 beta_sl = "sl_",
 beta_log_sl = "log_sl_",
 beta_sl_sq = "sl_sq_",
 beta_log_sl_sq = "log_sl_sq_",
 ...
)
update_ta_distr(object, beta_cos_ta = "cos_ta_", ...)

Arguments

Value

An amt_distr object, which consists of a list with the name of the distribution and its parameters (saved in params).

Author(s)

Brian J. Smith and Johannes Signer

References

Fieberg J, Signer J, Smith BJ, Avgar T (2020). “A "How-to" Guide for Interpreting Parameters in Resource-and Step-Selection Analyses.” bioRxiv.

See Also

Wrapper to fit a distribution to data fit_distr()

Examples

# Fit an SSF, then update movement parameters.
data(deer)
mini_deer <- deer[1:100, ]
sh_forest <- get_sh_forest()
# Prepare data for SSF
ssf_data <- mini_deer |>
 steps_by_burst() |>
 random_steps(n = 15) |>
 extract_covariates(sh_forest) |>
 mutate(forest = factor(forest, levels = 1:0,
 labels = c("forest", "non-forest")),
 cos_ta_ = cos(ta_),
 log_sl_ = log(sl_))
# Check tentative distributions
# Step length
sl_distr_params(ssf_data)
attr(ssf_data, "sl_")
# Turning angle
ta_distr_params(ssf_data)
# Fit an iSSF
m1 <- ssf_data |>
 fit_issf(case_ ~ forest +
 sl_ + log_sl_ + cos_ta_ +
 strata(step_id_))
# Update step length distribution
new_gamma <- update_sl_distr(m1)
# Update turning angle distribution
new_vm <- update_ta_distr(m1)
# It is also possible to use different step length distributions
# exponential step-length distribution
s2 <- mini_deer |> steps_by_burst()
s2 <- random_steps(s2, sl_distr = fit_distr(s2$sl_, "exp"))
m2 <- s2 |>
 fit_clogit(case_ ~ sl_ + strata(step_id_))
update_sl_distr(m2)
# half normal step-length distribution
s3 <- mini_deer |> steps_by_burst()
s3 <- random_steps(s3, sl_distr = fit_distr(s3$sl_, "hnorm"))
m3 <- s3 |>
 mutate(sl_sq_ = sl_^2) |>
 fit_clogit(case_ ~ sl_sq_ + strata(step_id_))
update_sl_distr(m3)
# log normal step-length distribution
s4 <- mini_deer |> steps_by_burst()
s4 <- random_steps(s4, sl_distr = fit_distr(s4$sl_, "lnorm"))
m4 <- s4 |>
 mutate(log_sl_ = log(sl_), log_sl_sq_ = log(sl_)^2) |>
 fit_clogit(case_ ~ log_sl_ + log_sl_sq_ + strata(step_id_))
update_sl_distr(m4)