A map of Australia

Description

This map layer outlines the coast of Australia.

Usage

Australia

Format

A data.frame

A map of Sandy Strait, Australia

Description

This map layer outlines the coast around Sandy Strait, Australia.

Usage

SandyStrait

Format

A data.frame

Horizontal asymptotes of rational functions

Description

Function to find horizontal asymptotes of a rational function.

Usage

asymptote(
  data = NULL,
  x = NULL,
  y = NULL,
  degree = "optim",
  upper.degree = 5,
  d1 = NA,
  d2 = NA,
  threshold = 0.95,
  proportional = TRUE,
  max.asymptote = 1,
  estimator = "glm",
  ci.level = 0.95,
  ...
)

Arguments

Details

This function fits a rational function to the input data. When an output object from boot_overlap, combn_overlap or boot_area is supplied, a rational function is fit to the means or predicted values of the bootstrap results (e.g. mean overlap probability) as a function of x (e.g. sample size). It then estimates horizontal asymptotes and identifies the sample size when an asymptote is considered. If ci.level = NULL and threshold = 0.95, an asymptote is considered when the mean or predicted y value reaches above 95 If ci.level is specified (e.g. 0.95) and threshold = 0.95, an asymptote is considered when the mean or predicted y value AND the confidence interval are above 95 When the "PHR" method was used in boot_overlap, binomial is generally a sensible family object for the GLM. gaussian and Gamma are often good options when the maximum y value exceeds 1 (e.g. area size). Please caution if estimated horizontal asymptote is very different from the expected asymptote. For example, the estimated horizontal asymptote should be around 1 when overlaps between UDs are calculated using the "PHR" method. see boot_overlap.

Value

A list containing a data frame (rational function fit associated with x values), an estimated horizontal asymptote, the minimum sample size if an asymptote is reached, and the estimated optimal degree of numerator and denominator of the rational function.

Author(s)

Takahiro Shimada

References

Shimada T, Thums M, Hamann M, Limpus CJ, Hays GC, FitzSimmons N, Wildermann NE, Duarte CD, Meekan MG (2021) Optimising sample sizes for animal distribution analysis using tracking data. Methods in Ecology and Evolution 12(2):288-297 doi:10.1111/2041-210X.13506

Press, W. H., S. A. Teukolsky, W. T. Vetterling, and B. P. Flannery (2007). Numerical Recipes: The Art of Numerical Computing. Third Edition, Cambridge University Press, New York.

See Also

boot_overlap, combn_overlap, boot_area

Bathymetry model for Sandy Strait, Australia

Description

A high resolution bathymetry model (100 m) for the Sandy Strait region developed by Beaman, R.J. (2010).

Usage

bathymodel

Format

A stars

Source

https://www.deepreef.org/

References

Beaman, R.J. (2010) Project 3DGBR: A high-resolution depth model for the Great Barrier Reef and Coral Sea. Marine and Tropical Sciences Research Facility (MTSRF) Project 2.5i.1a Final Report, MTSRF, Cairns, Australia, pp. 13 plus Appendix 1.

Cumulative analysis of collective areas by bootstrapping

Description

Function to calculate collective areas (merged x% Utilisation Distributions or UDs) of n individuals by bootstrapping.

Usage

boot_area(
  data,
  cell.size = NA,
  R = 1000,
  percent = 50,
  quantiles = c(0.25, 0.5, 0.75)
)

Arguments

Details

This function calculates collective areas (e.g. 50% UDs) of 1 to n individuals by bootstrapping.

Value

A list containing two data frames - raw results and summary (mean, sd, sem and quantiles at each sample size).

Author(s)

Takahiro Shimada

References

Shimada T, Thums M, Hamann M, Limpus CJ, Hays GC, FitzSimmons N, Wildermann NE, Duarte CD, Meekan MG (2021) Optimising sample sizes for animal distribution analysis using tracking data. Methods in Ecology and Evolution 12(2):288-297 doi:10.1111/2041-210X.13506

See Also

boot_overlap, combn_overlap

Examples

## Not run: 

#1 Utilisation distributions of flatback turtles (n = 15).
data(ud_raster)

#2 Calculate collective areas from 3000 random permutation
area <- boot_area(ud_raster, R = 3000, percent = 50)

#3 Find the minimum sample size required to estimate the general distribution.
a <- asymptote(area, upper.degree = 10, estimator = 'glm', family = gaussian, max.asymptote = NA)

#4 Plot the mean collective area and rational function fit relative to the sample sizes.
ggplot(data = a$results, aes(x = x))+
  geom_pointrange(aes(y = y, ymin = y_lwr, ymax = y_upr)) + 
  geom_point(aes(y = y), size = 2) + 
  scale_x_continuous(breaks = seq(0, 15, 3), limits = c(2,15), name = "Animals tracked (n)") +
  scale_y_continuous(name = expression(Area~(km^2)), labels=function(x) x/1e6)

## End(Not run)

Bootstrap overlaps between Utilisation Distributions (UDs)

Description

Function to calculate overlaps between UDs relative to sample size by bootstrapping.

Usage

boot_overlap(
  data,
  R = 1000,
  method = "PHR",
  percent = 100,
  quantiles = c(0.25, 0.5, 0.75)
)

Arguments

Details

This function calculates and bootstraps overlap between UDs based on the areas ("HR"), areas of collective UDs and the probability distribution of each individual ("PHR"), or the probability distribution of an individual and an averaged probability distribution of collective individuals ("VI", "BA", "UDOI").

Value

A list containing two data frames - raw results and summary (mean, sd, sem and quantiles at each sample size).

Author(s)

Takahiro Shimada

References

Shimada T, Thums M, Hamann M, Limpus CJ, Hays GC, FitzSimmons N, Wildermann NE, Duarte CD, Meekan MG (2021) Optimising sample sizes for animal distribution analysis using tracking data. Methods in Ecology and Evolution 12(2):288-297 doi:10.1111/2041-210X.13506

Fieberg J & Kochanny CO (2005) Quantifying home-range overlap: The importance of the utilization distribution. The Journal of Wildlife Management, 69(4), 1346–1359. doi:10.2193/0022-541x(2005)69[1346:Qhotio]2.0.Co;2

See Also

combn_overlap, boot_area

Examples

## Not run: 

#1 Utilisation uistributions of flatback turtles (n = 15).
data(ud_matrix)

#2 Calculate overlap probability from 2000 random permutation.
overlap <- boot_overlap(ud_matrix, R = 2000, method = "PHR")

#3 Find the minimum sample size required to estimate the general distribution.
a <- asymptote(overlap, upper.degree = 10, estimator = 'glm', family = binomial)

#4 Plot the mean probability and rational function fit relative to the sample sizes.
ggplot(data = a$results, aes(x = x))+
  geom_pointrange(aes(y = y, ymin = y_lwr, ymax = y_upr)) + 
  geom_hline(yintercept = a$h.asymptote*0.95, linetype = 2) +
  scale_x_continuous(breaks = seq(0, 15, 3), limits = c(2,15), name = "Animals tracked (n)") +
  scale_y_continuous(limits = c(0.5,1), name = "Overlap probability")

## End(Not run)

Quantifying overlaps between all possible combination of Utilisation Distributions (UDs)

Description

Function to calculate overlaps between all possible combination of UDs relative to sample size.

Usage

combn_overlap(
  data,
  method = "PHR",
  percent = 100,
  quantiles = c(0.25, 0.5, 0.75)
)

Arguments

Details

This function calculates overlap between all possible combination of input UDs based on the areas ("HR"), areas of collective UDs and the probability distribution of each individual ("PHR"), or the probability distribution of an individual and an averaged probability distribution of collective individuals ("VI", "BA", "UDOI").

Value

A list containing two data frames - raw results and summary (mean, sd, sem and quantiles at each sample size).

Author(s)

Takahiro Shimada

References

Shimada T, Thums M, Hamann M, Limpus CJ, Hays GC, FitzSimmons N, Wildermann NE, Duarte CD, Meekan MG (2021) Optimising sample sizes for animal distribution analysis using tracking data. Methods in Ecology and Evolution 12(2):288-297 doi:10.1111/2041-210X.13506

Fieberg J & Kochanny CO (2005) Quantifying home-range overlap: The importance of the utilization distribution. The Journal of Wildlife Management, 69(4), 1346–1359. doi:10.2193/0022-541x(2005)69[1346:Qhotio]2.0.Co;2

See Also

boot_overlap, boot_area

Examples

## Not run: 

#1 Utilisation uistributions of flatback turtles (n = 15).
data(ud_matrix)

#2 Calculate overlap probability from all combination of the UDs.
overlap <- combn_overlap(ud_matrix, method = "PHR")

#3 Find the minimum sample size required to estimate the general distribution.
a <- asymptote(overlap, upper.degree = 10, ci.level = NULL)

#4 Plot the mean probability and rational function fit relative to the sample sizes.
ggplot(data = a$results, aes(x = x, y = y))+
  geom_point() +
  geom_hline(yintercept = a$h.asymptote*0.95, linetype = 2) +
  scale_x_continuous(breaks = seq(0, 15, 3), limits = c(2,15), name = "Animals tracked (n)") +
  scale_y_continuous(limits = c(0.5,1), name = "Overlap probability")

## End(Not run)

Filter locations using a data driven filter

Description

Function to remove locations by a data driven filter as described in Shimada et al. (2012).

Usage

ddfilter(sdata, vmax = 8.9, vmaxlp = 1.8, qi = 4, ia = 90, method = 1)

Arguments

Details

Locations are removed if the speed from a previous and/or to a subsequent location exceeds vmax, or if all of the following criteria apply: the associated quality index is less than or equal to qi, the inner angle is less than or equal to ia and the speed either from a previous or to a subsequent location exceeds vmaxlp. If vmax and vmaxlp are unknown, they can be estimated using the functions vmax and vmaxlp respectively.

Value

The input data is returned without locations identified by this filter. The following columns are added: "pTime", "sTime", "pDist", "sDist", "pSpeed", "sSpeed", "inAng". "pTime" and "sTime" are hours from a previous and to a subsequent fix respectively. "pDist" and "sDist" are straight distances in kilometres from a previous and to a subsequent fix respectively. "pSpeed" and "sSpeed" are linear speed from a previous and to a subsequent fix respectively. "inAng" is the degree between the bearings of lines joining successive location points.

Author(s)

Takahiro Shimada

References

Shimada T, Jones R, Limpus C, Hamann M (2012) Improving data retention and home range estimates by data-driven screening. Marine Ecology Progress Series 457:171-180 doi:10.3354/meps09747

See Also

ddfilter_speed, ddfilter_loop, vmax, vmaxlp

Examples

#### Load data sets
## Fastloc GPS data obtained from a green turtle
data(turtle)

## A Map for the example site
data(Australia)
data(SandyStrait)


#### Filter temporal and/or spatial duplicates
turtle.dup <- dupfilter(turtle, step.time=5/60, step.dist=0.001)
 

#### ddfilter
## Using the built-in function to estimate the threshold speeds
V <- vmax(turtle.dup)
VLP <- vmaxlp(turtle.dup)
turtle.dd <- ddfilter(turtle.dup, vmax=V, vmaxlp=VLP)

## Or using user specified threshold speeds
turtle.dd <- ddfilter(turtle.dup, vmax=9.9, qi=4, ia=90, vmaxlp=2.0)


#### Plot data removed or retained by ddfilter
## Entire area
p1 <- to_map(turtle.dup, bgmap=Australia, point.size = 2, line.size = 0.5, axes.lab.size = 0, 
            multiplot = FALSE, point.bg = "red",
            title.size=15, title="Entire area")[[1]] + 
  geom_point(aes(x=lon, y=lat), data=turtle.dd, size=2, fill="yellow", shape=21)+
  geom_point(aes(x=x, y=y), data=data.frame(x=c(154, 154), y=c(-22, -22.5)), 
             size=3, fill=c("yellow", "red"), shape=21) + 
  annotate("text", x=c(154.3, 154.3), y=c(-22, -22.5), label=c("Retained", "Removed"), 
           colour="black", size=4, hjust = 0)

## Zoomed in
p2 <- to_map(turtle.dup, bgmap=SandyStrait, xlim=c(152.7, 153.2), ylim=(c(-25.75, -25.24)), 
            axes.lab.size = 0, point.size = 2, point.bg = "red", line.size = 0.5, 
            multiplot = FALSE, title.size=15, title="Zoomed in")[[1]] + 
geom_path(aes(x=lon, y=lat), data=turtle.dd, linewidth=0.5, colour="black", linetype=1) + 
geom_point(aes(x=lon, y=lat), data=turtle.dd, size=2, colour="black", shape=21, fill="yellow")

gridExtra::marrangeGrob(list(p1, p2), nrow=1, ncol=2)

Filter locations by quality index, inner angle, and speed

Description

A partial component of ddfilter, although works as a stand-alone function. This function removes locations by speed, inner angle, and quality index as described in Shimada et al. (2012).

Usage

ddfilter_loop(sdata, qi = 4, ia = 90, vmaxlp = 1.8)

Arguments

Details

This function removes locations if all of the following criteria apply: the number of source satellites are less than or equal to qi, the inner angle is less than and equal to ia and the speed either from a previous or to a subsequent location exceeds vmaxlp. If vmaxlp is unknown, it can be estimated using the function vmaxlp.

Value

The input data is returned without locations identified by this filter. The following columns are added: "pTime", "sTime", "pDist", "sDist", "pSpeed", "sSpeed", "inAng". "pTime" and "sTime" are hours from a previous and to a subsequent fix respectively. "pDist" and "sDist" are straight distances in kilometres from a previous and to a subsequent fix respectively. "pSpeed" and "sSpeed" are linear speed from a previous and to a subsequent fix respectively. "inAng" is the degree between the bearings of lines joining successive location points.

Author(s)

Takahiro Shimada

References

Shimada T, Jones R, Limpus C, Hamann M (2012) Improving data retention and home range estimates by data-driven screening. Marine Ecology Progress Series 457:171-180 doi:10.3354/meps09747

See Also

ddfilter, ddfilter_speed, vmaxlp

Filter locations by speed

Description

A partial component of ddfilter, although works as a stand-alone function. This function removes locations by a given threshold speed as described in Shimada et al. (2012).

Usage

ddfilter_speed(sdata, vmax = 8.9, method = 1)

Arguments

Details

This function removes locations if the speed from a previous and/or to a subsequent location exceeds a given threshold speed. If vmax is unknown, it can be estimated using the function vmax.

Value

The input data is returned without locations identified by this filter. The following columns are added: "pTime", "sTime", "pDist", "sDist", "pSpeed", "sSpeed". "pTime" and "sTime" are hours from a previous and to a subsequent fix respectively. "pDist" and "sDist" are straight distances in kilometres from a previous and to a subsequent fix respectively. "pSpeed" and "sSpeed" are linear speed from a previous and to a subsequent fix respectively.

Author(s)

Takahiro Shimada

References

Shimada T, Jones R, Limpus C, Hamann M (2012) Improving data retention and home range estimates by data-driven screening. Marine Ecology Progress Series 457:171-180 doi:10.3354/meps09747

See Also

ddfilter, ddfilter_loop, vmax, track_param

Filter locations by water depth

Description

Function to filter locations according to bathymetry and tide.

Usage

depthfilter(
  sdata,
  bathymetry,
  bilinear = TRUE,
  qi = 4,
  tide,
  tidal.plane,
  type = "HT",
  height = 0,
  filter = TRUE
)

Arguments

Details

The function examines each location according to the water depth experienced by the animal or the water depth at the nearest high tide. The function looks for the closest match between each fix and tidal observations or predictions in temporal and spatial scales. When filter is disabled, the function does not filter locations but returns the estimated water depth of each location with the tide effect considered (bathymetry + tide).

Value

When filter option is enabled, this function filters the input data and returns with two additional columns; "depth.exp", "depth.HT". "depth.exp" is the estimated water depth at each location at the time of location fixing. "depth.HT" is the estimated water depth at the nearest high tide at each location.

Note

The input data must not contain temporal or spatial duplicates.

Author(s)

Takahiro Shimada

References

Shimada T, Limpus C, Jones R, Hazel J, Groom R, Hamann M (2016) Sea turtles return home after intentional displacement from coastal foraging areas. Marine Biology 163:1-14 doi:10.1007/s00227-015-2771-0

Beaman, R.J. (2010) Project 3DGBR: A high-resolution depth model for the Great Barrier Reef and Coral Sea. Marine and Tropical Sciences Research Facility (MTSRF) Project 2.5i.1a Final Report, MTSRF, Cairns, Australia, pp. 13 plus Appendix 1.

See Also

dupfilter, ddfilter

Examples

## Not run: 

#### Load data sets
## Fastloc GPS data obtained from a green turtle
data(turtle)

## Bathymetry model developed by Beaman (2010)
data(bathymodel)

## A tidal plane for the example site
data(tidalplane)

## Tidal observations and predictions for the example site
data(tidedata)

## Maps for the example site
data(SandyStrait)


#### Remove temporal and/or spatial duplicates and biologically unrealistic fixes 
turtle.dd <- ddfilter(dupfilter(turtle))


#### Apply depthfilter
turtle <- depthfilter(sdata = turtle.dd, bathymetry = bathymodel, 
tide = tidedata, tidal.plane = tidalplane)


#### Plot data removed or retained by depthfilter
to_map(turtle.dd, bgmap = SandyStrait, point.bg = "red", point.size = 2, line.size = 0.5, 
        axes.lab.size = 0, title.size = 0, multiplot = FALSE)[[1]] + 
geom_point(aes(x = lon, y = lat), data = turtle, size = 2, fill = "yellow", shape = 21)+
geom_point(aes(x = x, y = y), data = data.frame(x = c(152.68, 152.68), y = c(-25.3, -25.34)), 
           size = 3, fill = c("yellow", "red"), shape = 21) + 
annotate("text", x = c(152.7, 152.7), y = c(-25.3, -25.34), label = c("Retained", "Removed"), 
         colour = "black", size = 4, hjust = 0)

## End(Not run)          

Filter locations by distance

Description

This function removes locations that are located beyond a specified distance.

Usage

distfilter(sdata, max.dist = 100, method = 1, ia = NA)

Arguments

Details

This function removes locations if the distance from a previous and/or to a subsequent location exceeds max.dist and the inner angle is less than ia. If ia is NA (default), inner angles are not considered in the filtering.

Value

The input data is returned without locations identified by this filter. The following columns are added: "pDist", "sDist", 'inAng'. "pDist" and "sDist" are straight distances in kilometres from a previous and to a subsequent fix respectively. "inAng" is the degree between the bearings of lines joining successive location points.

Author(s)

Takahiro Shimada

Examples

#### Load data sets
## Fastloc GPS data obtained from a green turtle
data(turtle)

## A Map for the example site
data(Australia)
data(SandyStrait)


#### Filter temporal and/or spatial duplicates
turtle.dup <- dupfilter(turtle, step.time=1/60, step.dist=0.001)
 

#### distfilter
turtle.dist <- distfilter(turtle.dup, max.dist = 50, ia = 20)


#### Plot data removed or retained by ddfilter
## Entire area
p1 <- to_map(turtle.dup, bgmap=Australia, point.size = 2, line.size = 0.5, axes.lab.size = 0, 
            multiplot = FALSE, point.bg = "red",
            title.size=15, title="Entire area")[[1]] + 
  geom_point(aes(x=lon, y=lat), data=turtle.dist, size=2, fill="yellow", shape=21)+
  geom_point(aes(x=x, y=y), data=data.frame(x=c(154, 154), y=c(-22, -22.5)), 
             size=3, fill=c("yellow", "red"), shape=21) + 
  annotate("text", x=c(154.3, 154.3), y=c(-22, -22.5), label=c("Retained", "Removed"), 
           colour="black", size=4, hjust = 0)

## Zoomed in
p2 <- to_map(turtle.dup, bgmap=SandyStrait, xlim=c(152.7, 153.2), ylim=(c(-25.75, -25.24)), 
            axes.lab.size = 0, point.size = 2, point.bg = "red", line.size = 0.5, 
            multiplot = FALSE, title.size=15, title="Zoomed in")[[1]] + 
geom_path(aes(x=lon, y=lat), data=turtle.dist, linewidth=0.5, colour="black", linetype=1) + 
geom_point(aes(x=lon, y=lat), data=turtle.dist, size=2, colour="black", shape=21, fill="yellow")

gridExtra::marrangeGrob(list(p1, p2), nrow=1, ncol=2)

Filter temporal and/or spatial duplicates

Description

Function to filter temporal and spatial duplicates in tracking data and retain only a single fix per time and location.

Usage

dupfilter(
  sdata,
  step.time = 0,
  step.dist = 0,
  conditional = FALSE,
  no.cores = 1
)

Arguments

Details

This function filters temporal and spatial duplicates in tracking data. It first filters temporally and spatially exact locations. It then looks for temporal duplicates and retains a fix with the highest quality index. When temporal or spatial duplicates are associated with the same quality index, the function retains a location that is nearest from a previous and to a subsequent location.

Value

The input data frame is returned containing only a single fix (latitude/longitude pair) per time and location. The following columns are added: "pTime", "sTime", "pDist", "sDist". "pTime" and "sTime" are hours from a previous and to a subsequent fix respectively. "pDist" and "sDist" are straight distances in kilometres from a previous and to a subsequent fix respectively.

Author(s)

Takahiro Shimada

References

Shimada T, Limpus C, Jones R, Hazel J, Groom R, Hamann M (2016) Sea turtles return home after intentional displacement from coastal foraging areas. Marine Biology 163:1-14 doi:10.1007/s00227-015-2771-0

See Also

dupfilter_exact, dupfilter_qi, dupfilter_time, dupfilter_space, track_param

Examples

#### Load data sets
## Fastloc GPS data obtained from a green turtle
data(turtle)


#### Apply dupfilter
turtle.dup <- dupfilter(turtle)

Filter temporally and spatially exact duplicates

Description

Function to filter temporally and spatially exact locations in tracking data.

Usage

dupfilter_exact(sdata)

Arguments

Details

This is a partial component of dupfilter, although works as a stand-alone function. It looks for temporally and spatially exact locations and retains only a single fix (latitude/longitude pair) per time and location.

Value

The input data frame is returned with temporally and spatially exact duplicates removed.

Author(s)

Takahiro Shimada

References

Shimada T, Limpus C, Jones R, Hazel J, Groom R, Hamann M (2016) Sea turtles return home after intentional displacement from coastal foraging areas. Marine Biology 163:1-14 doi:10.1007/s00227-015-2771-0

See Also

dupfilter, dupfilter_qi, dupfilter_time, dupfilter_space

Filter temporal duplicates by quality index

Description

Function to filter temporal duplicates in tracking data by quality index.

Usage

dupfilter_qi(sdata = sdata, step.time = 0)

Arguments

Details

This function is a partial component of dupfilter, although works as a stand-alone function. It looks for temporal duplicates and retains a fix with the highest quality index.

Value

The input data frame is returned with temporal duplicates removed by the quality index. The following columns are added: "pTime", "sTime". "pTime" and "sTime" are hours from a previous and to a subsequent fix respectively.

Author(s)

Takahiro Shimada

References

Shimada T, Limpus C, Jones R, Hazel J, Groom R, Hamann M (2016) Sea turtles return home after intentional displacement from coastal foraging areas. Marine Biology 163:1-14 doi:10.1007/s00227-015-2771-0

See Also

dupfilter, dupfilter_exact, dupfilter_time, dupfilter_space, track_param

Filter spatial duplicates

Description

Function to filter spatial duplicates in tracking data.

Usage

dupfilter_space(
  sdata,
  step.time = 0,
  step.dist = 0,
  conditional = FALSE,
  no.cores = 1
)

Arguments

Details

This function is a partial component of dupfilter, although works as a stand-alone function. First it identifies spatial duplicates by searching for consecutive fixes that were located within step.dist. For each group of spatial duplicates, the function then retains a single fix that is nearest from a previous and to a subsequent location.

Value

The input data frame is returned with spatial duplicates removed. The following columns are added: "pTime", "sTime", "pDist", "sDist". "pTime" and "sTime" are hours from a previous and to a subsequent fix respectively. "pDist" and "sDist" are straight distances in kilometres from a previous and to a subsequent fix respectively.

Note

A minimum of two locations per id is required.

Author(s)

Takahiro Shimada

References

Shimada T, Limpus C, Jones R, Hazel J, Groom R, Hamann M (2016) Sea turtles return home after intentional displacement from coastal foraging areas. Marine Biology 163:1-14 doi:10.1007/s00227-015-2771-0

See Also

dupfilter, dupfilter_exact, dupfilter_time, dupfilter_qi, track_param

Filter temporal duplicates

Description

Function to filter temporal duplicates that are associated with the same quality index.

Usage

dupfilter_time(sdata, step.time = 0, no.cores = 1)

Arguments

Details

This is a partial component of dupfilter, although works as a stand-alone function. First it identifies temporal duplicates by searching for consecutive locations that were obtained within step.time. For each group of temporal duplicates, the function then retains a single fix that is nearest from a previous and to a subsequent location.

Value

The input data frame is returned with temporal duplicates removed. The following columns are added: "pTime", "sTime", "pDist", "sDist". "pTime" and "sTime" are hours from a previous and to a subsequent fix respectively. "pDist" and "sDist" are straight distances in kilometres from a previous and to a subsequent fix respectively.

Author(s)

Takahiro Shimada

References

Shimada T, Limpus C, Jones R, Hazel J, Groom R, Hamann M (2016) Sea turtles return home after intentional displacement from coastal foraging areas. Marine Biology 163:1-14 doi:10.1007/s00227-015-2771-0

See Also

dupfilter, dupfilter_exact, dupfilter_qi, dupfilter_space, track_param

Flatback turtle tracking data

Description

Satellite tracking data of 15 flatback turtles (Natator depressus) that nested in Curtis Island, Australia. This sample data is a subset of the tracking data used in Shimada et al. (2021).

Usage

flatback

Format

A data frame with 1020 rows and 4 variables:

id

identifier for each animal.

DateTime

GMT date & time of each location in class POSIXct.

x

longitude in UTM.

y

latitude in UTM.

Source

Shimada T, Thums M, Hamann M, Limpus CJ, Hays GC, FitzSimmons N, Wildermann NE, Duarte CD, Meekan MG (2021) Optimising sample sizes for animal distribution analysis using tracking data. Methods in Ecology and Evolution 12(2):288-297 doi:10.1111/2041-210X.13506

UD percent volume

Description

Function to calculate a percent volume on a utilisation distribution (UD)

Usage

percent_vol(x, percent = 100)

Arguments

Details

This function calculates a percent volume on a UD. The probability beyond the specified range will be assigned with a zero value.

Value

A vector containing the specified percent volume.

Author(s)

Takahiro Shimada

Tidal plane table for Sandy Strait, Australia

Description

A semidiurnal tidal plane table containing the height of the mean tidal planes and the average time differences of tide at different locations within Sandy Strait.

Usage

tidalplane

Format

A data frame with 2 rows and 6 variables:

standard.port

identifier for a tidal observation station.

secondary.port

identifier for a station at which tide is only predicted using the tidal records observed at the related standard port.

lat

latitude in decimal degrees.

lon

longitude in decimal degrees.

timeDiff

time difference between standard port and its associated secondary port.

datumDiff

baseline difference in metres between the bathymetry model and tidal observations/predictions, if each data uses different datum (e.g. LAT and MSL).

Source

The State of Queensland (Department of Transport and Main Roads), Tidal planes.

Tidal data for Sandy Strait, Australia

Description

A dataset containing tidal observations recorded at Bundaberg, Australia

Usage

tidedata

Format

A data frame with 26351 rows and 3 variables:

tideDT

GMT date & time of each observation in class POSIXct.

reading

observed tidal height in metres.

standard.port

identifier of the tidal station.

Source

The State of Queensland (Department of Transport and Main Roads), Tidal data.

Plot location data on a map

Description

Function to plot tracking data on a map or a satellite image.

Usage

to_map(
  sdata,
  xlim = NULL,
  ylim = NULL,
  margin = 10,
  bgmap = NULL,
  google.key = NULL,
  map.bg = "grey",
  map.col = "black",
  zoom = NULL,
  point.bg = "yellow",
  point.col = "black",
  point.symbol = 21,
  point.size = 1,
  line.col = "lightgrey",
  line.type = 1,
  line.size = 0.5,
  title = "id",
  title.size = 11,
  axes.text.size = 11,
  axes.lab.size = 11,
  multiplot = TRUE,
  nrow = 1,
  ncol = 1
)

Arguments

Value

An arrangelist is returned when multiplot is TRUE. Otherwise a list is returned.

Author(s)

Takahiro Shimada

See Also

dupfilter, ddfilter, vmax, vmaxlp

Examples

#### Load data sets
## Fastloc GPS data obtained from two green turtles
data(turtle)
data(turtle2)
turtles<-rbind(turtle, turtle2)

#### Filter temporal and/or spatial duplicates
turtle.dup <- dupfilter(turtles, step.time=5/60, step.dist=0.001)
 

#### ddfilter
V <- vmax(turtle.dup)
VLP <- vmaxlp(turtle.dup)
turtle.dd <- ddfilter(turtle.dup, vmax=V, vmaxlp=VLP)


#### Plot filtered data for each animal
## using the low-resolution world map
to_map(turtle.dd, point.size = 2, line.size = 0.5, axes.lab.size = 0, ncol=2, nrow=1)

## Not run: 
## using the high-resolution google satellite images
to_map(turtle.dd, bgmap = "satellite", google.key = "key", ncol=2)

## End(Not run)

Calculate parameters between locations

Description

Calculate time, distance, speed, and inner angle between successive locations

Usage

track_param(
  sdata,
  param = c("time", "distance", "speed", "angle", "mean speed", "mean angle"),
  days = 2
)

Arguments

Details

This function calculates various parameters of tracks. time (h), distance (km), speed (km/h) and inner angle (degrees) are calculated from each pair of successive locations. mean speed (km/h) and angle (degrees) are calculated from locations over a specified number of days.

Value

The input data is returned with new columns containing the requested parameters. "pTime" and "sTime" are hours from a previous and to a subsequent fix respectively. "pDist" and "sDist" are straight distances in kilometres from a previous and to a subsequent fix respectively. "pSpeed" and "sSpeed" are linear speed (km/h) from a previous and to a subsequent fix respectively. "inAng" is the degree between the bearings of lines joining successive location points. "meanSpeed" and "meanAngle" are the mean speed and degree over a specified number of days.

Author(s)

Takahiro Shimada

Examples

#### Load turtle tracking data
data(turtle)


#### Filter temporal and/or spatial duplicates
turtle.dup <- dupfilter(turtle, step.time=5/60, step.dist=0.001)


#### ddfilter
turtle.dd <- ddfilter(turtle.dup, vmax=9.9, qi=4, ia=90, vmaxlp=2.0)


#### Mean speed over 2 days
mean.speed <- track_param(turtle.dd, param = c('speed', 'mean speed'), days=2)


#### Plot data
ggplot(data = mean.speed, aes(x=lon, y=lat)) +
geom_path(colour = 'grey') +
geom_point(aes(colour=meanSpeed))

Green turtle tracking data

Description

A dataset containing Fastloc GPS locations of a green turtle tracked in Sandy Strait, Australia.

Usage

turtle

Format

A data frame with 429 rows and 5 variables:

id

identifier for each animal.

DateTime

GMT date & time of each location in class POSIXct.

lat

latitude in decimal degrees.

lon

longitude in decimal degrees.

qi

quality index associated with each location fix. The input values can be either the number of GPS satellites or Argos Location Classes. Argos Location Classes will be converted to numerical values, where "A", "B", "Z" will be replaced with "-1", "-2", "-3" respectively. The greater number indicates a higher accuracy.

Source

Shimada T, Jones R, Limpus C, Groom R, Hamann M (2016) Long-term and seasonal patterns of sea turtle home ranges in warm coastal foraging habitats: Implications for conservation. Marine Ecology Progress Series 562:163-179. doi:10.3354/meps11972

Green turtle tracking data 2

Description

A dataset containing Fastloc GPS locations of a green turtle tracked in Moreton Bay, Australia.

Usage

turtle2

Format

A data frame with 276 rows and 5 variables:

id

identifier for each animal.

DateTime

GMT date & time of each location in class POSIXct.

lat

latitude in decimal degrees.

lon

longitude in decimal degrees.

qi

quality index associated with each location fix. The input values can be either the number of GPS satellites or Argos Location Classes. Argos Location Classes will be converted to numerical values, where "A", "B", "Z" will be replaced with "-1", "-2", "-3" respectively. The greater number indicates a higher accuracy.

Source

Shimada T, Jones R, Limpus C, Groom R, Hamann M (2016) Long-term and seasonal patterns of sea turtle home ranges in warm coastal foraging habitats: Implications for conservation. Marine Ecology Progress Series 562:163-179. doi:10.3354/meps11972

A matrix containing probability distributions of flatback turtles

Description

Inter-nesting utilisation distributions of 15 flatback turtles (Natator depressus) that nested in Curtis Island, Australia. The UDs were calculated using the sample tracking data flatback and reduced grid resolution (1 km) instead of 50m as used in Shimada et al. (2021). See GitHub for an example code of UD estimation.

Usage

ud_matrix

Format

A matrix

Source

Shimada T, Thums M, Hamann M, Limpus CJ, Hays GC, FitzSimmons N, Wildermann NE, Duarte CD, Meekan MG (2021) Optimising sample sizes for animal distribution analysis using tracking data. Methods in Ecology and Evolution 12(2):288-297 doi:10.1111/2041-210X.13506

A list of raster data containing probability distributions of flatback turtles

Description

Inter-nesting utilisation distributions of 15 flatback turtles (Natator depressus) that nested in Curtis Island, Australia. The UDs were calculated using the sample tracking data flatback and reduced grid resolution (1 km) instead of 50m as used in Shimada et al. (2021). See GitHub for an example code of UD estimation.

Usage

ud_raster

Format

A list of 15 stars objects

Source

Shimada T, Thums M, Hamann M, Limpus CJ, Hays GC, FitzSimmons N, Wildermann NE, Duarte CD, Meekan MG (2021) Optimising sample sizes for animal distribution analysis using tracking data. Methods in Ecology and Evolution 12(2):288-297 doi:10.1111/2041-210X.13506

Maximum linear speed

Description

Function to estimate the maximum linear speed between two consecutive locations.

Usage

vmax(sdata, qi = 5, method = "ML", prob = 0.99, ...)

Arguments

Details

The function first calculates the linear speed between each pair of two consecutive locations. Some of the calculated linear speed can be inaccurate when the input data contains inaccurate locations (e.g. outliers). The function can discard the implausible outliers by excluding extreme values using either the "sample" or "ML" method. The "sample" method simply discards values that lie beyond the specified quantile. If the "ML" method is selected, it is assumed that the linear speed follow a Gamma distribution. The distribution parameters are derived via maximum likelihood estimation using the optim function. The linear speed at the given quantile or cumulative probability (e.g. 0.99) represents the maximum linear speed at which an animal would travel between two consecutive locations.

Value

Maximum linear speed (vmax) estimated from the input data. The unit is km/h.

Author(s)

Takahiro Shimada

References

Shimada T, Jones R, Limpus C, Hamann M (2012) Improving data retention and home range estimates by data-driven screening. Marine Ecology Progress Series 457:171-180 doi:10.3354/meps09747

See Also

ddfilter, ddfilter_speed, track_param, dupfilter

Maximum one-way linear speed of a loop trip

Description

Function to estimate the maximum one-way linear speed of a loop trip.

Usage

vmaxlp(sdata, qi = 4, nloc = 5, method = "ML", prob = 0.99, ...)

Arguments

Details

The function first detects a "loop trip". Loop trip behaviour is represented by spatial departure and return involving more than 3 consecutive locations (Shimada et al. 2012). The function calculates the net (i.e. straight-line) distance between the departure and turning point as well as the turning point and return location of a loop trip. It then calculates the one-way travelling speed to or from each turning point for each loop trip. To exclude implausible outliers, the function discards extreme values based on the specified quantile or an estimated probability distribution for the loop trip speed, depending on the selected method. If the "ML" method is selected, a Gamma distribution is assumed and the shape and scale parameters are estimated via maximum likelihood estimation using the optim function. The maximum value within a given quantile or probability range (e.g. 0.99) represents the maximum one-way linear speed at which an animal would travel during a loop trip.

Value

Maximum one-way linear speed of a loop trip (vmaxlp) estimated from the input data. The unit km/h.

Note

The input data must not contain temporal or spatial duplicates. A minimum of 8 locations are required.

Author(s)

Takahiro Shimada

References

Shimada T, Jones R, Limpus C, Hamann M (2012) Improving data retention and home range estimates by data-driven screening. Marine Ecology Progress Series 457:171-180 doi:10.3354/meps09747

See Also

ddfilter, ddfilter_loop, track_param, dupfilter