| Title: | Assess Mating Potential in Space and Time |
|---|---|
| Description: | Simulate, manage, visualize, and analyze spatially and temporally explicit datasets of mating potential. Implements methods to calculate synchrony, proximity, and compatibility.Synchrony calculations are based on methods described in Augspurger (1983) <doi:10.2307/2387650>, Kempenaers (1993) <doi:10.2307/3676415>, Ison et al. (2014) <doi:10.3732/ajb.1300065>, and variations on these, as described. |
| Authors: | Stuart Wagenius [cre, aut], Danny Hanson [aut], Amy Waananen [aut] |
| Maintainer: | Stuart Wagenius <[email protected]> |
| License: | GPL |
| Version: | 0.3.2 |
| Built: | 2025-02-22 03:54:59 UTC |
| Source: | https://github.com/stuartwagenius/mateable |
This package facilitates the investigation of three dimensions of mating potential. It provides a method for simulating populations and includes a dataset.
Stuart Wagenius, Danny Hanson, Amy Waananen
Background: https://echinaceaProject.org/
library(mateable) pop <- simulateScene() pop plotScene(pop) plotScene(pop, "t") plot3DScene(pop) sync <- synchrony(pop, "augs") prox <- proximity(pop, "maxPropSqrd") plotPotential(sync) plotPotential(prox, "ind") str(ech2012) ee <- makeScene(ech2012, FALSE, "firstDay", "lastDay", "Ecoord", "Ncoord", idCol = "tagNo")library(mateable) pop <- simulateScene() pop plotScene(pop) plotScene(pop, "t") plot3DScene(pop) sync <- synchrony(pop, "augs") prox <- proximity(pop, "maxPropSqrd") plotPotential(sync) plotPotential(prox, "ind") str(ech2012) ee <- makeScene(ech2012, FALSE, "firstDay", "lastDay", "Ecoord", "Ncoord", idCol = "tagNo")
Calculate one of several measures of mating compatibility.
compatibility(scene, method, subject = "all", averageType = "mean")compatibility(scene, method, subject = "all", averageType = "mean")
scene |
a matingScene object |
method |
either "si_echinacea" or "dioecious" see details for further description |
subject |
whether you want pair, individual, population, or all. Specifying more than one is allowed. |
averageType |
whether to calculate individual and population proximity using the mean or median |
When method is "si_echinacea" compatibility will be
calculated as sporophytic self incompatible (si) in the same manner as Echinacea
(and many other plants). For two individuals, they are incompatible if
they share any S alleles (columns s1 and s2) and they compatible otherwise.
When method is "dioecious" it is assumed that the column s1 will
contain either a 1 or 2 depending on the individual's sex. Thus, when
comparing two individuals, they are compatible if s1 of the first != s1
of the second, and s2 is ignored.
A potentials object containing one more more of the following, depending the
input for subject:
If subject is "population" the return list will contain a numeric
value that has a range depending on the method. If
subject is "pair" the return list will contain a matrix
with all pairwise compatibilities. If subject is "individual"
the return list will contain a dataframe with a column containing IDs and
a column containing compatibility averages. If subject is "all"
the return list will contain all three of the items above.
Danny Hanson
pop <- simulateScene() compatibility(pop, "si_echinacea")pop <- simulateScene() compatibility(pop, "si_echinacea")
dailyOMP generates an OMP object giving the daily outcrossed mating potential of individuals based on k nearest neighbors
dailyOMP( scene, k = 3, days = NULL, gamma = 1/13.3, nn.constant = FALSE, sum = FALSE, mean = FALSE )dailyOMP( scene, k = 3, days = NULL, gamma = 1/13.3, nn.constant = FALSE, sum = FALSE, mean = FALSE )
scene |
a matingScene object |
k |
integer, number of nearest neighbors to use in calculating OMP |
days |
the day or range of days to calculate OMP for, default is all days in a scene (see Details) |
gamma |
parameter of exponential decay to be used in calculating OMP (defaults to 1/13) |
nn.constant |
logical; indicates whether the nearest neighbors used in calculations should be the nearest on a given day ( |
sum |
logical; indicates if the return should be a sum of an individual's daily outcrossed mating potential over the range specified by days |
mean |
logical; indicates if the return should be the mean of an individual's daily outcrossed mating potential over the range of days that the individual was receptive to mating |
Daily outcrossed mating potential is a weighted average of an individual's distance to their nearest neighbors on a given day (Wagenius et al. 2007). The days to calculate OMP for should be input as integers relative to the first day of flowering, as they are in the start and end columns of a matingScene object. If the number of ids receptive on a day is less than k, OMP will be calculated for the maximum number of neighbors.
a named matrix with a row for each id and a column for each day, and entries corresponding to ids' OMP each day
Amy Waananen
Wagenius, S., E. Lonsdorf, and C. Neuhauser. 2007. Patch aging and the S-Allee effect: breeding system effects on the demographic response of plants to habitat fragmentation. American Naturalist 169:383-397.
makeScene, proximity, synchrony, receptivityByDay
pop <- simulateScene() omp <- dailyOMP(pop) omp.1 <- dailyOMP(pop, nn.constant = TRUE) # same nearest neighbors throughout the season omp.2 <- dailyOMP(pop, nn.constant = FALSE) # nearest flowering neighborspop <- simulateScene() omp <- dailyOMP(pop) omp.1 <- dailyOMP(pop, nn.constant = TRUE) # same nearest neighbors throughout the season omp.2 <- dailyOMP(pop, nn.constant = FALSE) # nearest flowering neighbors
This dataframe contains information about all 53 plants that flowered in 2012 at the sites eelr (East Elk Lake Road) and Northwest of Landfill (nwlf). Kelly Kapsar visited plants regularly to determine the starting and ending dates of flowering on every head of every plant. The metadata for the phenology dataset can be provide upon request to the maintainer. Plants were mapped with gps with better than 6 cm precision.
ech2012ech2012
A 53 x 6 data frame
Variables:
tag, unique identifier for each plant
pop, population corresponding to site
firstDay, the first day that any head on the plant shed pollen
lastDay, the last day that any head on the plant shed pollen
Ecoord, the x-coordinate of each plant in meters
Ncoord, the y-coordinate of each plant in meters
Wagenius, S. 2006. Scale-dependence of reproductive failure in fragmented Echinacea populations. Ecology 87: 931-941.
dim(ech2012) str(ech2012)dim(ech2012) str(ech2012)
Find the k nearest neighbors for all individuals in a population. This
function is simply a wrapper for FNN::knn.dist.
kNearNeighbors(scene, k)kNearNeighbors(scene, k)
scene |
a matingScene object |
k |
integer of how many nearest neighbors to get |
a matrix where the rows are all individuals and the columns are their k nearest neighbors
pop <- simulateScene(10) kNearNeighbors(pop, 3)pop <- simulateScene(10) kNearNeighbors(pop, 3)
Turns a data frame with information about temporal, spatial, or genetic mating data into a matingScene object using a standard format.
makeScene( df, multiYear = FALSE, startCol = "start", endCol = "end", xCol = "x", yCol = "y", s1Col = "s1", s2Col = "s2", idCol = "id", otherCols = NULL, dateFormat = "%Y-%m-%d", split = NULL )makeScene( df, multiYear = FALSE, startCol = "start", endCol = "end", xCol = "x", yCol = "y", s1Col = "s1", s2Col = "s2", idCol = "id", otherCols = NULL, dateFormat = "%Y-%m-%d", split = NULL )
df |
a data frame containing information about a mating scene, namely coordinate of individuals in space, time, and mating type. |
multiYear |
logical indicating whether or not to split the result into a list by year |
startCol |
character name of column with start dates |
endCol |
character name of column with end dates |
xCol |
character name of column with x or E coordinates |
yCol |
character name of column with y or N coordinates |
s1Col |
character name of one column with S-allele |
s2Col |
character name of another column with S-alleles |
idCol |
character name for column with unique identifier |
otherCols |
character vector of column(s) to include besides the necessary ones for the mating scene. If NULL, it will be ignored. |
dateFormat |
character indicating either (1) the format of the start and end date columns if those columns are characters or (2) the origin for the start and end date columns if those columns are numeric. It is used in as.Date |
split |
character name for a column with values by which the result should be split |
The input dataframe can contain information about locations of
individuals in 1, 2, or 3 dimensions of a mating scenes.
The function currently allows two spatial coordinates. The user specifies
the names of the columns and they will be saved xCol and yCol in the
matingScene object. MatingScene objects currently save temporal
coordinates for individuals as start and end date of mating activity
within a year. Mating type coordinates are saved as mating type alleles.
Columns are named id, start, end, x, y, s1, and s2 for
idCol, startCol, endCol, xCol, yCol, s1Col, and s2Col respectively.
The attributes "t", "s", and "mt" will be set to TRUE if the data frame
has temporal, spatial, or mating type data, respectively and
will be FALSE otherwise. The attribute originalNames contains all the
names of the columns in the original data frame.
The start and end columns will be changed to integers relative to the start
day of the population. So the first day of the first individual to become
receptive will be 1 and so on. The attribute origin contains the
origin that can be used when converting the columns start and end
from integers to dates.
If no temporal data are available except the year in which it was
collected and df is a multi-year data set, put the collection year into the
column labeled as startCol and set dateFormat = "
the data appropriately.
a matingScene object, either a single dataframe in standard format or a list of dataframes. Attributes of the matingScene object indicate the type of information in the data frame, including the original column names, and the origin of the date columns. If multiYear = TRUE, the return value will be a list of matingScene data frames where each element in the list represents one year. If split is specified, the return value will be a list of matingScene data frames where each element in the list represents a value of the specified variable. See details for more information on attributes and how to work with multi-year data.
Danny Hanson
Create a summary of information contained within a matingScene object.
matingSummary( scene, type = "auto", k = 1, compatMethod = "si_echinacea", as.data.frame = FALSE )matingSummary( scene, type = "auto", k = 1, compatMethod = "si_echinacea", as.data.frame = FALSE )
scene |
a matingScene object |
type |
character. whether to do a temporal (t), spatial (s), or mating type (mt) summary. The default is "auto" which will automatically summarize all mating information in scene |
k |
integer. Which nearest neighbor to calculate (only for type == "s") |
compatMethod |
character indicating the method to use when calculating compatiblity. Defaults to "si_echinacea" |
as.data.frame |
logical. If TRUE, returns summary as a dataframe. |
a list, list of lists, or dataframe containing summary information
including:
temporal - year (year), population start date (popSt), mean individual start date
(meanSD), standard deviation of start (sdSD), mean duration (meanDur),
standard deviation of duration (sdDur), peakDay - day(s) on which highest
number of individuals were receptive (peak), mean end date (meanED),
standard deviation of end date (sdED), population end date (popEnd)
spatial - minimum x (minX), minimum y (minY), maximum x (maxX),
maximum y (maxY), average distance to kth nearest neighbor as specified
by k (k<n> where n is the input for k)
compatibility - number of mating types (nMatType), average number of
compatible mates (meanComp)
If scene is a multi-year matingScene, then the output will be a list
of lists, one list for each year.
If as.data.frame = TRUE, the output will be a dataframe with columns containing summary information and, if applicable, an 'id' column identifying what portion of the matingSummary object it summarized. If the scene is a multi-year matingScene, then the output will be a list of dataframes, one list for each year.
eelr <- makeScene(ech2012, startCol = "firstDay", endCol = "lastDay", xCol = "Ecoord", yCol = "Ncoord", idCol = "tagNo") eelrSum <- matingSummary(eelr) eelrSum[c("minX", "minY", "maxX", "maxY")]eelr <- makeScene(ech2012, startCol = "firstDay", endCol = "lastDay", xCol = "Ecoord", yCol = "Ncoord", idCol = "tagNo") eelrSum <- matingSummary(eelr) eelrSum[c("minX", "minY", "maxX", "maxY")]
Get comparisons of mating timing between all pairs
overlap(scene, overlapOrTotal = c("overlap", "total"), compareToSelf = FALSE)overlap(scene, overlapOrTotal = c("overlap", "total"), compareToSelf = FALSE)
scene |
a matingScene object |
overlapOrTotal |
whether to calculate the number of days that each pair was overlapping in mating receptivity or the total number of days that either individual was receptive |
compareToSelf |
whether or not to include self comparisons in the return value |
a matrix containing all pairwise comparisons. If compareToSelf
is FALSE then there will be n rows and n-1 columns.
To index result[i,j] where j > i, use result[i,j-1], where result
is the return value of overlap. There is one attribute "idOrder"
which holds the order of the id column in scene at the time of the function call.
This can be useful to find certain elements in the matrix (see examples).
If scene is a multi-year matingScene, then overlap will return a list of matrices
(as described above) where each matrix represents one year.
Danny Hanson
pop <- simulateScene() pop <- pop[order(pop$start),] daysSync <- overlap(pop) indices <- which(attr(daysSync, "idOrder") %in% c(1, 4)) if (indices[1] <= indices[2]) { daysSync[indices[1], indices[2]] } else { daysSync[indices[1], indices[2]-1] }pop <- simulateScene() pop <- pop[order(pop$start),] daysSync <- overlap(pop) indices <- which(attr(daysSync, "idOrder") %in% c(1, 4)) if (indices[1] <= indices[2]) { daysSync[indices[1], indices[2]] } else { daysSync[indices[1], indices[2]-1] }
Compute all pairwise distances for a population. This function
is simply a wrapper for dist that returns only a matrix
pairDist(scene)pairDist(scene)
scene |
a matingScene object |
a matrix of all pairwise comparisons with attributes for order of identifiers (idOrder)
pop <- simulateScene() distance <- pairDist(pop)pop <- simulateScene() distance <- pairDist(pop)
Visualize multiple dimensions of mating potential
plot3DPotential( matPots, subject = NULL, density = TRUE, sub.ids = NULL, N = 3, sample = NA, text.cex = 0.7, pt.cex = 0.7 )plot3DPotential( matPots, subject = NULL, density = TRUE, sub.ids = NULL, N = 3, sample = NA, text.cex = 0.7, pt.cex = 0.7 )
matPots |
list, contains one or multiple mating potential objects representing unique potential dimensions |
subject |
character, indicates whether the subject to be visualized is individuals ( |
density |
logical, if TRUE (default), plots probability density over histogram |
sub.ids |
vector, contains the IDs of individuals to be represented in pairwise potential plots |
N |
integer, indicates the number of individuals to sample if sub.ids = 'random' (default N = 3) |
sample |
character, specifies how to sample individuals to be represented in pairwise potential plots. Possible values are "random" (default) or "all". See details. |
text.cex |
specify text expansion factor (text size relative to device default) |
pt.cex |
specify point expansion factor (point size relative to device default) |
The individuals to be represented in the pairwise potential plots can either be specified explicitly
through sub.ids, chosen randomly (sample = 'random'), or all individuals can be selected (sample = 'all').
The default is to randomly select 9 individuals. If multiple years are being plotted, the subset is sampled from all years
and the same individuals will be represented in each year, if possible.
No return value, called to draw a plot
Amy Waananen
see generic function points for values of pch
pop <- simulateScene() sync <- synchrony(pop, "augs") prox <- proximity(pop, 'maxProp') compat <- compatibility(pop, 'si_echinacea') plot3DPotential(list(sync,prox,compat), subject = 'ind')pop <- simulateScene() sync <- synchrony(pop, "augs") prox <- proximity(pop, 'maxProp') compat <- compatibility(pop, 'si_echinacea') plot3DPotential(list(sync,prox,compat), subject = 'ind')
Visualize multiple dimensions of a mating scene
plot3DScene( scene, dimension = "auto", sub = NULL, N = 3, ycoord = "northing", xcoord = "easting", pch = 19, pt.cex = 0.7, label.cex = 0.7, mt1 = "F", mt2 = "M", plot.lim.zoom = FALSE, ... )plot3DScene( scene, dimension = "auto", sub = NULL, N = 3, ycoord = "northing", xcoord = "easting", pch = 19, pt.cex = 0.7, label.cex = 0.7, mt1 = "F", mt2 = "M", plot.lim.zoom = FALSE, ... )
scene |
a matingScene object |
dimension |
what dimension(s) of the mating scene should be visualized. Possible dimensions are 't' for temporal, 's' for spatial, 'mt' for mating type, and 'auto' (the default). For dimension = 'auto', all dimensions represented in the mating scene object will be plotted. |
sub |
a subset of the population to plot; either a character indicating whether to subset a random sample ( |
N |
if |
ycoord |
y-axis coordinate system label |
xcoord |
x-axis coordinate system label |
pch |
point type, defaults to pch = 19, solid filled in circle. If pch = NULL, individuals will be labeled by their id. |
pt.cex |
specify point expansion factor (point size relative to device default) |
label.cex |
specify text expansion factor (text size relative to device default) |
mt1 |
label for mating type '1', if dioecious; defaults to 'F' |
mt2 |
label for mating type '2', if dioecious; defaults to 'M' |
plot.lim.zoom |
if TRUE, spatial plot limits for lists of scenes are set by the maximum from all scenes |
... |
optional arguments for the plot function |
No return value, called to draw a plot
Amy Waananen
see generic function points for values of pch
pop <- simulateScene() plot3DScene(pop)pop <- simulateScene() plot3DScene(pop)
Visualize mating potential
plotPotential( matPot, subject = NULL, plotType = "auto", density = TRUE, sub.ids = NULL, N = 9, sample = "random", ... )plotPotential( matPot, subject = NULL, plotType = "auto", density = TRUE, sub.ids = NULL, N = 9, sample = "random", ... )
matPot |
a mating potential object |
subject |
character, either 'ind' or 'pair', indicating whether the subject being visualized is individuals or pairwise interactions |
plotType |
character, indicating what plots are to be displayed. See details. Options are histogram ('hist'), network diagram ('net'), and heatmap ('heat'). If mating potential object |
density |
logical. If TRUE (default), plots probability density over histogram. |
sub.ids |
a vector containing the ids of individuals to be represented in pairwise potential plots |
N |
a positive number indicating the number of individuals to sample if sub.ids = 'random' |
sample |
a character string specifying how to choose a subset of individuals to be represented in pairwise potential plots. Possible values are "random" (default) or "all" (see details). |
... |
optional arguments for the plot function |
Options for plotType are 'hist' (histogram), 'net' (network diagram), 'heat' (heatmap), and 'auto'. Default value is 'auto':
if the mating potential object contains pairwise potential, 'auto' returns all plot types, otherwise it returns histograms of individual potential.
The individuals to be represented in the pairwise potential plots can either be specified explicitly through sub.ids, chosen randomly
(sample = 'random'), or all individuals can be selected (sample = 'all'). The default is to randomly select 9 individuals.
If multiple years are being plotted, the subset is sampled from all years and the same individuals will be represented in each year, if possible.
If fewer than three individuals from the subset are available in a year, no network diagram or heatmap will be returned for that year.
No return value, called to draw plots
Amy Waananen
see generic function points for values of pch
pop <- simulateScene() sync <- synchrony(pop, "augs") plotPotential(sync)pop <- simulateScene() sync <- synchrony(pop, "augs") plotPotential(sync)
Visualize a mating scene
plotScene( scene, dimension = "auto", dailyPoints = TRUE, drawQuartiles = TRUE, sortBy = c("start", "end"), colorBy = NULL, sub = NULL, N = 3, label.sub = TRUE, xlab.spat = NULL, ylab.spat = NULL, pch = 19, pt.cex = 0.75, label.cex = 0.8, plot.lim.zoom = FALSE, quartile.lwd = 1, quartile.col = "gray55", peak.col = "gray27", labelID = FALSE, mt1 = "F", mt2 = "M", leg.ncol = 1, ... )plotScene( scene, dimension = "auto", dailyPoints = TRUE, drawQuartiles = TRUE, sortBy = c("start", "end"), colorBy = NULL, sub = NULL, N = 3, label.sub = TRUE, xlab.spat = NULL, ylab.spat = NULL, pch = 19, pt.cex = 0.75, label.cex = 0.8, plot.lim.zoom = FALSE, quartile.lwd = 1, quartile.col = "gray55", peak.col = "gray27", labelID = FALSE, mt1 = "F", mt2 = "M", leg.ncol = 1, ... )
scene |
a matingScene object |
dimension |
what dimension(s) of the mating scene should be visualized. Possible dimensions are 't' for temporal, 's' for spatial, 'mt' for mating type, and 'auto' (the default). For dimension = 'auto', all dimensions represented in the mating scene object will be plotted. |
dailyPoints |
logical indicating whether daily counts of individuals should be displayed for plots of the temporal dimension |
drawQuartiles |
logical indicating whether vertical lines should be drawn at population peak (see details) or quartiles |
sortBy |
character indicating which columns to sort segments of flowering schedule by. Defaults to 'start', then 'end'. Up to three variables may be specified. |
colorBy |
character optional, the name of a variable to use to assign color to segments or points. |
sub |
a vector containing the ids of individuals to be highlighted in the plots or a character string specifying how to choose individuals to highlight. Possible values are "random" or "all". If NULL, no subset will be identified in the plots. |
N |
a positive number, the number of individuals to sample if |
label.sub |
logical, indicating whether specified subset should be labeled |
xlab.spat |
character label for x-axis of spatial dimension plots. If NULL, defaults to 'easting'. |
ylab.spat |
character label for y-axis of spatial dimension plots. If NULL, defaults to 'northing'. |
pch |
specify point type to be used in plots. Defaults to pch = 19 (filled-in circle). If NULL, points will be labeled with their id. |
pt.cex |
specify point expansion factor (point size relative to device default) |
label.cex |
specify text expansion factor (text size relative to device default) |
plot.lim.zoom |
if TRUE, spatial plot limits for lists of scenes are set by the maximum from all scenes |
quartile.lwd |
if |
quartile.col |
if |
peak.col |
if |
labelID |
if TRUE, the y-axis will be labeled with the id of the corresponding segment. |
mt1 |
label for mating type '1', if dioecious |
mt2 |
label for mating type '2', if dioecious |
leg.ncol |
number of columns to include in legend, if colorBy is not NULL |
... |
standard graphical parameters |
Population peak is defined by when maximum number individuals were reproductively receptive on one day. If multiple days had the same maximum number, peak is defined as the median of these dates.
No return value, called to draw a plot
Amy Waananen
see plot3DScene to visualize multiple dimensions on one plot
pop <- simulateScene() plotScene(pop)pop <- simulateScene() plotScene(pop)
Calculate one of several measures of spatial proximity
proximity( scene, method, averageType = "mean", subject = "all", zeroPotDist = NULL, k = 6 )proximity( scene, method, averageType = "mean", subject = "all", zeroPotDist = NULL, k = 6 )
scene |
a matingScene object |
method |
one of "maxProp", "maxPropSqrd", or 'knn.dist'; see details for further description |
averageType |
whether to calculate individual and population proximity using the mean or median |
subject |
whether you want pair, individual, population, or all. Specifying more than one is allowed. |
zeroPotDist |
the distance at which potential should be equal to zero |
k |
the number of the nearest neighbor to search, if |
If method is "maxProp" then proximity between two
individuals will be calculated as 1 - distance/max(distance).
If method is "maxPropSqrd" then proximity between two
individuals will be calculated as (1 - distance/max(distance))^2. If method is
"knn.dist" then the function This uses FNN::knn.dist to return the Euclidian distance of the kth nearest neighbor.
A potentials object containing one more more of the following, depending the
input for subject:
If subject is "population" the return list will contain a numeric
value that has a range depending on the method. If
subject is "pair" the return list will contain a matrix
with all pairwise proximity comparisons. If subject is "individual"
the return list will contain a dataframe with a column containing IDs and
a column containing proximity values. If subject is "all"
the return list will contain all three of the items above.
Danny Hanson
pop <- simulateScene() proximity(pop, "maxProp")pop <- simulateScene() proximity(pop, "maxProp")
Create a matrix showing which individuals are receptive on a given day.
receptivityByDay(scene, summary = FALSE, nameDate = TRUE)receptivityByDay(scene, summary = FALSE, nameDate = TRUE)
scene |
a matingScene object |
summary |
logical, summarizes number of receptive individuals on each day |
nameDate |
logical, if summary = TRUE, option to name indices of the vector by the date they represent (rather than named relative to first day of receptivity in a season) |
if summary = FALSE (default), a matrix where the columns represent all mating days and the rows
represent all individuals in the population. If summary = TRUE, a named vector where each index gives the
number of receptive individuals on a given day and is named by the day it represents. If a matrix, the value at position
[i,j] will be TRUE if individual j was receptive on day i
If scene is a multi-year matingScene, then receptivityByDay will return a list of matrices
(as described above) where each matrix represents one year.
Danny Hanson, Amy Waananen
pop <- simulateScene(size = 10) receptivityByDay(pop)pop <- simulateScene(size = 10) receptivityByDay(pop)
simulateScene generates a matingScene object – a simulated population in a standard format with individuals randomly assigned a mating schedule, a location, and S-alleles
simulateScene( size = 30, meanSD = "2012-07-12", sdSD = 6, meanDur = 11, sdDur = 3, skSD = 0, xRange = c(0, 100), yRange = c(0, 100), distro = "unif", sAlleles = 10 )simulateScene( size = 30, meanSD = "2012-07-12", sdSD = 6, meanDur = 11, sdDur = 3, skSD = 0, xRange = c(0, 100), yRange = c(0, 100), distro = "unif", sAlleles = 10 )
size |
integer number of plants |
meanSD |
date mean start date |
sdSD |
date standard deviation of start date |
meanDur |
numeric duration in days |
sdDur |
standard deviation of duration in days |
skSD |
skew of the start date of the population |
xRange |
range of spatial extent of individuals along x-axis |
yRange |
range of spatial extent of individuals along y-axis |
distro |
unimplemented |
sAlleles |
integer count of S-Alleles that could be in the population |
matingScene data frame – see makeScene
Stuart Wagenius
simulateScene()simulateScene()
Calculate one of a variety of measures of mating synchrony.
synchrony( scene, method, subject = "all", averageType = "mean", syncNN = 1, compareToSelf = FALSE, frame = "within", resolution = "daily" )synchrony( scene, method, subject = "all", averageType = "mean", syncNN = 1, compareToSelf = FALSE, frame = "within", resolution = "daily" )
scene |
a matingScene object that includes the flowering schedule for the scene of interest. |
method |
character, partial matching allowed, describing what type
of synchrony will be calculated. "augspurger" is based on the method
described in Augspurger (1983). "kempenaers" is based on the method
described in Kempenaers (1993). "sync_prop" will calculate individual
synchrony based on the proportion of the sum of all individuals' days
available to mate that coincided with the individual's days available for mating.
"overlap" is based on the method described in Ison et al. (2014) and will
calculate a synchrony value based on the number of days both
individuals were flowering divided by the number of days either individual
was available for mating. "sync_nn" gives the average of the kth nearest
neighbor, or rather the kth most synchronous individual. "peak-n" will
calculate the number of individuals receptive on the peak day
(day with highest mating receptivity) divided by the number of individuals
in the population. "peak-observations" will calculate the number of individuals
receptive on the peak day divided by the total number of observations -
this method is useful for comparing to data that has no information on
individuals. "average-peak" calculates the average (determined by argument
|
subject |
one of "population", "pairwise", "individual", or "all" - see Value for more details. |
averageType |
character. Identifies whether to take the mean or median when calculating averages |
syncNN |
integer between 1 and n-1 (inclusive) or numeric between 0 and 1 (exclusive). The kth nearest neighbor to be averaged when calculating population synchrony. If k is in (0,1) then the k*nth nearest neighbor will be found |
compareToSelf |
logical. Whether or not to include self comparisons when calculation synchrony. Defaults to FALSE. |
frame |
the timeframe that synchrony is to be calculated over; options are 'within,' for synchrony within a season, or 'between,' for synchrony across multiple seasons. Defaults to 'within'. |
resolution |
if |
Measures of synchrony are based on methods described in Augspurger (1983), Kempenaers (1983), and from Ison et al. (2014), as well as variations on different factors of those measures.
A potentials object containing one more more of the following, depending the
input for subject:
If subject is "population" synchrony will return a numeric
value that has a range depending on the method. If
subject is "pairwise" synchrony will return a matrix
with all pairwise synchrony comparisons. It is important to note two things:
[1] if method is set to "sync_nn" then the pairwise comparisons will
be in descending order and cannot be indexed by ID order. [2] if
compareToSelf is set to FALSE, the matrix will have dimensions 100
rows by 99 columns. Similar to overlap, indexing will be
affected. If subject is "individual" synchrony will
returns a data frame with a row for id and a row for individual synchrony.
If subject is "all" synchrony will return a list
containing the values described above for population, pairwise, and
individual synchrony.
Danny Hanson, Amy Waananen
Augspurger, C.K. (1983) Phenology, flowering synchrony, and fruit set of
six neotropical shrubs. Biotropica 15, 257-267.
Ison, J.L., S. Wagenius, D. Reitz., M.V. Ashley. (2014) Mating between
Echinacea angustifolia (Asteraceae) individuals increases with
their flowering synchrony and spatial proximity.
American Journal of Botany 101, 180-189
Kempenaers, B. (1993) The use of a breeding synchrony index.
Ornis Scandinavica, 24, 1.
pop <- simulateScene(size = 150) synchrony(pop, "augs") pop2 <- simulateScene(size = 1234, sdDur = 5, sk = 1) syncVals <- synchrony(pop2, "sync_nn", "all", "median", 123)pop <- simulateScene(size = 150) synchrony(pop, "augs") pop2 <- simulateScene(size = 1234, sdDur = 5, sk = 1) syncVals <- synchrony(pop2, "sync_nn", "all", "median", 123)