LTREs, sLTREs, and MPM import with Anthyllis vulneraria
In this vignette, we will import a MPM created elsewhere into
lefko3 and analyze it via LTRE and sLTRE analysis. These
are inherently ahistorical MPMs, and so we do not include historical
analyses in this vignette. To reduce output size, we have prevented some
statements from running if they produce long stretches of output.
Examples include most summary() calls. Please run these
statements without hashtags to see the output. This vignette is only a
sample analysis. Detailed information and instructions on using
lefko3 are available through a free online e-book called
lefko3: a gentle introduction, available on
the projects
page of the Shefferson lab website.
ORGANISM AND POPULATIONS
Davison et al. (2010) reported stochastic contributions made by differences in vital rate means and variances among nine natural populations of the perennial herb Anthyllis vulneraria, also known as kidney vetch, occurring in calcareous grasslands in the Viroin Valley of southwestern Belgium. A. vulneraria is a grassland specialist and the unique host plant of the Red-listed blue butterfly (Cupido minimus). It is a short-lived, rosette-forming legume with a complex life cycle including stasis and retrogression between four stages but no seedbank (seedlings, juveniles, small adults and large adults; Figure 9.1).
Figure 9.1. Life
history model of Anthyllis vulneraria. Solid arrows indicate
survival transitions while dashed arrows indicate fecundity
transitions.
Nine populations (N = 27-50,000) growing in distinct grassland fragments were surveyed between 2003 and 2006, yielding three (4x4) annual transition matrices for each population. The populations occurred within grassland fragments, and were mostly managed as nature reserves through rotational sheep grazing. These surveys coincided with a summer heat wave (2003), followed by a spring drought (2005) and an even more extreme heat wave (2006). These populations have been subject to detailed study for aspects of their genetics and demography, and further details on the sites can be obtained through the resulting publications (Krauss, Steffan-Dewenter, and Tscharntke 2004; Honnay et al. 2006; Piessens et al. 2009).
BASIC WORKFLOW
Our goal in this exercise will be to import the published MPMs available for these nine populations of Anthyllis vulneraria, and to analyze the demographic differences between populations using deterministic and stochastic life table response experiments (LTRE and sLTRE). We will use the matrices analyzed in Davison et al. (2010), and attempt to reproduce their results.
Step 1. Life history model development
We will first describe the life history characterizing the dataset with
a stageframe. Since we do not have the original demographic
dataset that produced the published matrices, we do not need to know the
exact sizes of plants and so will use proxy values. These proxy values
need to be unique and non-negative, and they need non-overlapping bins
usable as size classes defining each stage. However, since we are not
analyzing size itself, they do not need any further basis in reality.
Other characteristics must be exact and realistic to make sure that the
analyses work properly, including all other stage descriptions such as
reproductive status, propagule status, and observation status. Note that
we are using the midpoint approach to determining the size bins here,
using the default bin halfwidths of 0.5. However, we could have used the
sizemin and sizemax options to more
deliberately set the size bin minima and maxima instead.
sizevector <- c(1, 1, 2, 3) # These sizes are not from the original paper
stagevector <- c("Sdl", "Veg", "SmFlo", "LFlo")
repvector <- c(0, 0, 1, 1)
obsvector <- c(1, 1, 1, 1)
matvector <- c(0, 1, 1, 1)
immvector <- c(1, 0, 0, 0)
propvector <- c(0, 0, 0, 0)
indataset <- c(1, 1, 1, 1)
binvec <- c(0.5, 0.5, 0.5, 0.5)
comments <- c("Seedling", "Vegetative adult", "Small flowering",
"Large flowering")
anthframe <- sf_create(sizes = sizevector, stagenames = stagevector,
repstatus = repvector, obsstatus = obsvector, matstatus = matvector,
immstatus = immvector, indataset = indataset, binhalfwidth = binvec,
propstatus = propvector, comments = comments)
#anthframeStep 2. Data organization
Next we will enter the data for this vignette. Our data is in the form
of matrices published in Davison et al.
(2010). We will enter these matrices as standard R
matrix class objects. All matrices are square with 4
columns, and we fill them by row. As an example, let’s load the first
matrix.
XC3 <- matrix(c(0, 0, 1.74, 1.74, # POPN C 2003-2004
0.208333333, 0, 0, 0.057142857,
0.041666667, 0.076923077, 0, 0,
0.083333333, 0.076923077, 0.066666667, 0.028571429), 4, 4, byrow = TRUE)
XC3
#> [,1] [,2] [,3] [,4]
#> [1,] 0.00000000 0.00000000 1.74000000 1.74000000
#> [2,] 0.20833333 0.00000000 0.00000000 0.05714286
#> [3,] 0.04166667 0.07692308 0.00000000 0.00000000
#> [4,] 0.08333333 0.07692308 0.06666667 0.02857143
These are A matrices, meaning that they include all
survival-transitions and fecundity for the population as a whole. The
corresponding U and F matrices were not
provided in that paper, although it is most likely that the elements
valued at 1.74 in the top-right corner are fecundity
elements while the rest of the matrix is composed only of survival
transitions. The order of rows and columns corresponds to the order of
stages in the stageframe anthframe. Let’s now input the
remaining matrices.
XC4 <- matrix(c(0, 0, 0.3, 0.6, # POPN C 2004-2005
0.32183908, 0.142857143, 0, 0,
0.16091954, 0.285714286, 0, 0,
0.252873563, 0.285714286, 0.5, 0.6), 4, 4, byrow = TRUE)
XC5 <- matrix(c(0, 0, 0.50625, 0.675, # POPN C 2005-2006
0, 0, 0, 0.035714286,
0.1, 0.068965517, 0.0625, 0.107142857,
0.3, 0.137931034, 0, 0.071428571), 4, 4, byrow = TRUE)
XE3 <- matrix(c(0, 0, 2.44, 6.569230769, # POPN E 2003-2004
0.196428571, 0, 0, 0,
0.125, 0.5, 0, 0,
0.160714286, 0.5, 0.133333333, 0.076923077), 4, 4, byrow = TRUE)
XE4 <- matrix(c(0, 0, 0.45, 0.646153846, # POPN E 2004-2005
0.06557377, 0.090909091, 0.125, 0,
0.032786885, 0, 0.125, 0.076923077,
0.049180328, 0, 0.125, 0.230769231), 4, 4, byrow = TRUE)
XE5 <- matrix(c(0, 0, 2.85, 3.99, # POPN E 2005-2006
0.083333333, 0, 0, 0,
0, 0, 0, 0,
0.416666667, 0.1, 0, 0.1), 4, 4, byrow = TRUE)
XF3 <- matrix(c(0, 0, 1.815, 7.058333333, # POPN F 2003-2004
0.075949367, 0, 0.05, 0.083333333,
0.139240506, 0, 0, 0.25,
0.075949367, 0, 0, 0.083333333), 4, 4, byrow = TRUE)
XF4 <- matrix(c(0, 0, 1.233333333, 7.4, # POPN F 2004-2005
0.223880597, 0, 0.111111111, 0.142857143,
0.134328358, 0.272727273, 0.166666667, 0.142857143,
0.119402985, 0.363636364, 0.055555556, 0.142857143), 4, 4, byrow = TRUE)
XF5 <- matrix(c(0, 0, 1.06, 3.372727273, # POPN F 2005-2006
0.073170732, 0.025, 0.033333333, 0,
0.036585366, 0.15, 0.1, 0.136363636,
0.06097561, 0.225, 0.166666667, 0.272727273), 4, 4, byrow = TRUE)
XG3 <- matrix(c(0, 0, 0.245454545, 2.1, # POPN G 2003-2004
0, 0, 0.045454545, 0,
0.125, 0, 0.090909091, 0,
0.125, 0, 0.090909091, 0.333333333), 4, 4, byrow = TRUE)
XG4 <- matrix(c(0, 0, 1.1, 1.54, # POPN G 2004-2005
0.111111111, 0, 0, 0,
0, 0, 0, 0,
0.111111111, 0, 0, 0), 4, 4, byrow = TRUE)
XG5 <- matrix(c(0, 0, 0, 1.5, # POPN G 2005-2006
0, 0, 0, 0,
0.090909091, 0, 0, 0,
0.545454545, 0.5, 0, 0.5), 4, 4, byrow = TRUE)
XL3 <- matrix(c(0, 0, 1.785365854, 1.856521739, # POPN L 2003-2004
0.128571429, 0, 0, 0.010869565,
0.028571429, 0, 0, 0,
0.014285714, 0, 0, 0.02173913), 4, 4, byrow = TRUE)
XL4 <- matrix(c(0, 0, 14.25, 16.625, # POPN L 2004-2005
0.131443299, 0.057142857, 0, 0.25,
0.144329897, 0, 0, 0,
0.092783505, 0.2, 0, 0.25), 4, 4, byrow = TRUE)
XL5 <- matrix(c(0, 0, 0.594642857, 1.765909091, # POPN L 2005-2006
0, 0, 0.017857143, 0,
0.021052632, 0.018518519, 0.035714286, 0.045454545,
0.021052632, 0.018518519, 0.035714286, 0.068181818), 4, 4, byrow = TRUE)
XO3 <- matrix(c(0, 0, 11.5, 2.775862069, # POPN O 2003-2004
0.6, 0.285714286, 0.333333333, 0.24137931,
0.04, 0.142857143, 0, 0,
0.16, 0.285714286, 0, 0.172413793), 4, 4, byrow = TRUE)
XO4 <- matrix(c(0, 0, 3.78, 1.225, # POPN O 2004-2005
0.28358209, 0.171052632, 0, 0.166666667,
0.084577114, 0.026315789, 0, 0.055555556,
0.139303483, 0.447368421, 0, 0.305555556), 4, 4, byrow = TRUE)
XO5 <- matrix(c(0, 0, 1.542857143, 1.035616438, # POPN O 2005-2006
0.126984127, 0.105263158, 0.047619048, 0.054794521,
0.095238095, 0.157894737, 0.19047619, 0.082191781,
0.111111111, 0.223684211, 0, 0.356164384), 4, 4, byrow = TRUE)
XQ3 <- matrix(c(0, 0, 0.15, 0.175, # POPN Q 2003-2004
0, 0, 0, 0,
0, 0, 0, 0,
1, 0, 0, 0), 4, 4, byrow = TRUE)
XQ4 <- matrix(c(0, 0, 0, 0.25, # POPN Q 2004-2005
0, 0, 0, 0,
0, 0, 0, 0,
1, 0.666666667, 0, 1), 4, 4, byrow = TRUE)
XQ5 <- matrix(c(0, 0, 0, 1.428571429, # POPN Q 2005-2006
0, 0, 0, 0.142857143,
0.25, 0, 0, 0,
0.25, 0, 0, 0.571428571), 4, 4, byrow = TRUE)
XR3 <- matrix(c(0, 0, 0.7, 0.6125, # POPN R 2003-2004
0.25, 0, 0, 0.125,
0, 0, 0, 0,
0.25, 0.166666667, 0, 0.25), 4, 4, byrow = TRUE)
XR4 <- matrix(c(0, 0, 0, 0.6, # POPN R 2004-2005
0.285714286, 0, 0, 0,
0.285714286, 0.333333333, 0, 0,
0.285714286, 0.333333333, 0, 1), 4, 4, byrow = TRUE)
XR5 <- matrix(c(0, 0, 0.7, 0.6125, # POPN R 2005-2006
0, 0, 0, 0,
0, 0, 0, 0,
0.333333333, 0, 0.333333333, 0.625), 4, 4, byrow = TRUE)
XS3 <- matrix(c(0, 0, 2.1, 0.816666667, # POPN S 2003-2004
0.166666667, 0, 0, 0,
0, 0, 0, 0,
0, 0, 0, 0.166666667), 4, 4, byrow = TRUE)
XS4 <- matrix(c(0, 0, 0, 7, # POPN S 2004-2005
0.333333333, 0.5, 0, 0,
0, 0, 0, 0,
0.333333333, 0, 0, 1), 4, 4, byrow = TRUE)
XS5 <- matrix(c(0, 0, 0, 1.4, # POPN S 2005-2006
0, 0, 0, 0,
0, 0, 0, 0.2,
0.111111111, 0.75, 0, 0.2), 4, 4, byrow = TRUE)
We will incorporate all of these matrices into a lefkoMat
object. To do so, we will first organize the matrices in the proper
order within a list. Then, we will create the lefkoMat
object to hold these matrices by calling function
create_lM() with the list object we created. We will also
include metadata describing the order of populations (here treated as
patches), and the order of monitoring occasions.
mats_list <- list(XC3, XC4, XC5, XE3, XE4, XE5, XF3, XF4, XF5, XG3, XG4, XG5,
XL3, XL4, XL5, XO3, XO4, XO5, XQ3, XQ4, XQ5, XR3, XR4, XR5, XS3, XS4, XS5)
anth_lefkoMat <- create_lM(mats_list, anthframe, hstages = NA,
historical = FALSE, poporder = 1,
patchorder = c(1, 1, 1, 2, 2, 2, 3, 3, 3, 4, 4, 4, 5, 5, 5, 6, 6, 6, 7, 7, 7,
8, 8, 8, 9, 9, 9),
yearorder = c(2003, 2004, 2005, 2003, 2004, 2005, 2003, 2004, 2005, 2003,
2004, 2005, 2003, 2004, 2005, 2003, 2004, 2005, 2003, 2004, 2005, 2003,
2004, 2005, 2003, 2004, 2005))
#> Warning: No supplement provided. Assuming fecundity yields all propagule and immature stages.
#anth_lefkoMat
The resulting object has all of the elements of a standard
lefkoMat object except for those elements related to
quality control in the demographic dataset and linear modeling. The
option UFdecomp was left at its default
(UFdecomp = TRUE), so create_lM() used the
stageframe to infer where fecundity values should be located and created
U and F matrices accordingly. The default
option for historical is set to FALSE,
yielding an NA in place of the hstages
element, which would typically list the order of historical stage pairs.
The summary of this new lefkoMat object shows us that we
have 27 matrices with 4 rows and columns each. The estimated numbers of
transitions corresponds to the non-zero entries in each matrix. We see
that we are covering 1 population, 9 patches, and 3 time steps. All of
the survival probabilities observed fell within the bounds of 0 to 1, so
everything seems alright.
Step 3. MPM analysis
Let’s develop arithmetic mean matrices and assess the deterministic and stochastic population growth rates, λ and a.
anth_lmean <- lmean(anth_lefkoMat)
lambda2 <- lambda3(anth_lefkoMat)
lambda2m <- lambda3(anth_lmean)
set.seed(42)
sl2 <- slambda3(anth_lefkoMat) #Stochastic growth rate
sl2$expa <- exp(sl2$a)
plot(lambda ~ year2, data = subset(lambda2, patch == 1), ylim = c(0, 2.5),xlab = "Year",
ylab = expression(lambda), type = "l", col = "gray", lty= 2, lwd = 2, bty = "n")
lines(lambda ~ year2, data = subset(lambda2, patch == 2), col = "gray", lty= 2, lwd = 2)
lines(lambda ~ year2, data = subset(lambda2, patch == 3), col = "gray", lty= 2, lwd = 2)
lines(lambda ~ year2, data = subset(lambda2, patch == 4), col = "gray", lty= 2, lwd = 2)
lines(lambda ~ year2, data = subset(lambda2, patch == 5), col = "gray", lty= 2, lwd = 2)
lines(lambda ~ year2, data = subset(lambda2, patch == 6), col = "gray", lty= 2, lwd = 2)
lines(lambda ~ year2, data = subset(lambda2, patch == 7), col = "gray", lty= 2, lwd = 2)
lines(lambda ~ year2, data = subset(lambda2, patch == 8), col = "gray", lty= 2, lwd = 2)
lines(lambda ~ year2, data = subset(lambda2, patch == 9), col = "gray", lty= 2, lwd = 2)
abline(a = lambda2m$lambda[1], b = 0, lty = 1, lwd = 4, col = "orangered")
abline(a = sl2$expa[1], b = 0, lty = 1, lwd = 4, col = "darkred")
legend("topleft", c("det annual", "det mean", "stochastic"), lty = c(2, 1, 1),
col = c("gray", "orangered", "darkred"), lwd = c(2, 4, 4), bty = "n")
These populations exhibit highly variable growth across the short field study during which they were monitored. They also appear to be declining, as shown by λ for the overall mean matrix, and e to the power of the stochastic growth rate a = logλ.
Let’s conduct a life table response experiment (LTRE) next. This will be
a deterministic LTRE, meaning that we will assess the impacts of
differences in matrix elements between the core matrices input via the
lefkoMat object anth_lefkoMat and the overall
arithmetic grand mean matrix on differences in the deterministic growth
rate, λ. We could use a
different reference matrix if we wished, but the default is to use the
arithmetic grand mean. Here, because we cannot analyze temporal shifts
using a deterministic LTRE, we will utilize the mean matrix set instead.
trialltre_det <- ltre3(anth_lmean, sparse = "auto")
#> Warning: Matrices input as mats will also be used in reference matrix calculation.
#> Using all refmats matrices in reference matrix calculation.
#trialltre_det
The resulting lefkoLTRE object gives the LTRE contributions
for each matrix relative to the arithmetic grand mean matrix. While the
differences in LTRE contributions across space are of interest, we
cannot infer differences across time this way because matrices are not
related additively across time. To assess the contributions across space
while accounting for temporal shifts, we should conduct a stochastic
LTRE (sLTRE) or small-noise approximation LTRE (SNA-LTRE) on the
original annual matrices. Here, conduct a sLTRE.
trialltre_sto <- ltre3(anth_lefkoMat, stochastic = TRUE, times = 10000,
tweights = NA, sparse = "auto", seed = 42)
#> Warning: Matrices input as mats will also be used in reference matrix calculation.
#> Using all refmats matrices in reference matrix calculation.
#trialltre_sto
The sLTRE produces output that is a bit different from the deterministic
LTRE output. In the output, we see two lists of matrices prior to the
MPM metadata. The first, cont_mean, is a list of matrices
showing the contributions of differences in mean elements between the
patch-level temporal mean matrices and the reference temporal mean
matrix. The second, cont_sd, is a list of matrices showing
the contributions of differences in the temporal standard deviation of
each element between the patch-level and reference matrix sets. In other
words, while a standard LTRE shows the impact of changes in matrix
elements on λ, the sLTRE shows
the impacts of changes in the temporal mean and variability of matrix
elements on logλ. The
labels element shows the order of matrices with reference
to the populations or patches (remember that here, the populations are
referred to as patches).
The output objects are large and can take a great deal of effort to look over and understand. Therefore, we will show three approaches to assessing these objects, using an approach similar to that used to assess elasticities. These methods can be used to assess patterns in all 9 populations, but for brevity we will focus only on the first population here. First, we will identify the elements most strongly impacting the population growth rate in each case.
# Highest (i.e most positive) deterministic LTRE contribution:
max(trialltre_det$cont_mean[[1]])
#> [1] 0.02099388
# Highest deterministic LTRE contribution is associated with element:
which(trialltre_det$cont_mean[[1]] == max(trialltre_det$cont_mean[[1]]))
#> [1] 3
# Lowest (i.e. most negative) deterministic LTRE contribution:
min(trialltre_det$cont_mean[[1]])
#> [1] -0.2786045
# Lowest deterministic LTRE contribution is associated with element:
which(trialltre_det$cont_mean[[1]] == min(trialltre_det$cont_mean[[1]]))
#> [1] 13
# Highest stochastic mean LTRE contribution:
max(trialltre_sto$cont_mean[[1]])
#> [1] 0.01788772
# Highest stochastic mean LTRE contribution is associated with element:
which(trialltre_sto$cont_mean[[1]] == max(trialltre_sto$cont_mean[[1]]))
#> [1] 3
# Lowest stochastic mean LTRE contribution:
min(trialltre_sto$cont_mean[[1]])
#> [1] -0.3700463
# Lowest stochastic mean LTRE contribution is associated with element:
which(trialltre_sto$cont_mean[[1]] == min(trialltre_sto$cont_mean[[1]]))
#> [1] 13
# Highest stochastic SD LTRE contribution:
max(trialltre_sto$cont_sd[[1]])
#> [1] 0.00952507
# Highest stochastic SD LTRE contribution is associated with element:
which(trialltre_sto$cont_sd[[1]] == max(trialltre_sto$cont_sd[[1]]))
#> [1] 13
# Lowest stochastic SD LTRE contribution:
min(trialltre_sto$cont_sd[[1]])
#> [1] -0.01135741
# Lowest stochastic SD LTRE contribution is associated with element:
which(trialltre_sto$cont_sd[[1]] == min(trialltre_sto$cont_sd[[1]]))
#> [1] 16
# Total positive deterministic LTRE contributions:
sum(trialltre_det$cont_mean[[1]][which(trialltre_det$cont_mean[[1]] > 0)])
#> [1] 0.06338566
# Total negative deterministic LTRE contributions:
sum(trialltre_det$cont_mean[[1]][which(trialltre_det$cont_mean[[1]] < 0)])
#> [1] -0.4079573
# Total positive stochastic mean LTRE contributions:
sum(trialltre_sto$cont_mean[[1]][which(trialltre_sto$cont_mean[[1]] > 0)])
#> [1] 0.04459663
# Total negative stochastic mean LTRE contributions:
sum(trialltre_sto$cont_mean[[1]][which(trialltre_sto$cont_mean[[1]] < 0)])
#> [1] -0.5085542
# Total positive stochastic SD LTRE contributions:
sum(trialltre_sto$cont_sd[[1]][which(trialltre_sto$cont_sd[[1]] > 0)])
#> [1] 0.01026457
# Total negative stochastic SD LTRE contributions:
sum(trialltre_sto$cont_sd[[1]][which(trialltre_sto$cont_sd[[1]] < 0)])
#> [1] -0.02232758The output for the deterministic LTRE shows that element 13, which is the fecundity transition from large flowering adult to seedling (column 4, row 1), has the strongest influence. This contribution is negative, so it reduces λ. The most positive contribution to λ is from element 3, which is the growth transition from seedling to small flowering adult (column 1, row 3). We also see the sLTRE produced the same results in contributions of shifts in the mean as the deterministic LTRE. Variability in elements also contributes to shifts in logλ, though less so than shifts in mean elements. The strongest positive contribution is from variation in the fecundity transition from large flowering adult to seedling (element 13, column 4, row 1), while the most negative contribution is from stasis as a large flowering adult (element 16, row and column 4). A comparison of summed LTRE elements shows that negative contributions of mean elements were most influential in the stochastic case, while variability of elements had little overall influence.
Second, we will identify which stages exerted the strongest impact on the population growth rate.
ltre_pos <- trialltre_det$cont_mean[[1]]
ltre_neg <- trialltre_det$cont_mean[[1]]
ltre_pos[which(ltre_pos < 0)] <- 0
ltre_neg[which(ltre_neg > 0)] <- 0
sltre_meanpos <- trialltre_sto$cont_mean[[1]]
sltre_meanneg <- trialltre_sto$cont_mean[[1]]
sltre_meanpos[which(sltre_meanpos < 0)] <- 0
sltre_meanneg[which(sltre_meanneg > 0)] <- 0
sltre_sdpos <- trialltre_sto$cont_sd[[1]]
sltre_sdneg <- trialltre_sto$cont_sd[[1]]
sltre_sdpos[which(sltre_sdpos < 0)] <- 0
sltre_sdneg[which(sltre_sdneg > 0)] <- 0
ltresums_pos <- cbind(colSums(ltre_pos), colSums(sltre_meanpos), colSums(sltre_sdpos))
ltresums_neg <- cbind(colSums(ltre_neg), colSums(sltre_meanneg), colSums(sltre_sdneg))
ltre_as_names <- trialltre_det$ahstages$stage
barplot(t(ltresums_pos), beside = T, col = c("black", "grey", "red"),
ylim = c(-0.50, 0.10))
barplot(t(ltresums_neg), beside = T, col = c("black", "grey", "red"), add = TRUE)
abline(0, 0, lty= 3)
text(cex=1, y = -0.57, x = seq(from = 2, to = 3.98*length(ltre_as_names),
by = 4), ltre_as_names, xpd=TRUE, srt=45)
legend("bottomleft", c("deterministic", "stochastic mean", "stochastic SD"),
col = c("black", "grey", "red"), pch = 15, bty = "n")
The plot shows that large flowering adults exerted the strongest influence on both λ and logλ, with the latter influence being through the impact of shifts in the mean. This impact is overwhelmingly negative. The next largest impact comes from small flowering adults, in both cases the influence being negative on the whole.
Finally, we will assess what transition types exert the greatest impact on population growth rate.
det_ltre_summary <- summary(trialltre_det)
sto_ltre_summary <- summary(trialltre_sto)
ltresums_tpos <- cbind(det_ltre_summary$ahist_mean$matrix1_pos,
sto_ltre_summary$ahist_mean$matrix1_pos,
sto_ltre_summary$ahist_sd$matrix1_pos)
ltresums_tneg <- cbind(det_ltre_summary$ahist_mean$matrix1_neg,
sto_ltre_summary$ahist_mean$matrix1_neg,
sto_ltre_summary$ahist_sd$matrix1_neg)
barplot(t(ltresums_tpos), beside = T, col = c("black", "grey", "red"),
ylim = c(-0.55, 0.10))
barplot(t(ltresums_tneg), beside = T, col = c("black", "grey", "red"),
add = TRUE)
abline(0, 0, lty = 3)
text(cex=0.85, y = -0.64, x = seq(from = 2,
to = 3.98*length(det_ltre_summary$ahist_mean$category), by = 4),
det_ltre_summary$ahist_mean$category, xpd=TRUE, srt=45)
legend("bottomleft", c("deterministic", "stochastic mean", "stochastic SD"),
col = c("black", "grey", "red"), pch = 15, bty = "n")
The overall greatest impact on the population growth rate is from fecundity, and these contributions are overwhelmingly negative. Clearly temporal variation has strong effects here that deserve to be assessed properly.
LTREs and sLTREs are powerful tools, and more complex versions of
both analyses exist. Please consult Caswell
(2001) and Davison et al. (2013)
for more information. Also see Davison et al.
(2013) for information on the small noise approximation LTRE,
which is also possible in lefko3. Package
lefko3 also includes functions to conduct complex analyses,
including two general projection functions that can be used for analyses
such as quasi-extinction analysis as well as for density dependent
analysis. Users wishing to conduct these analyses should see our free
e-manual called lefko3: a gentle introduction
and other vignettes, including long-format and video vignettes, on
the projects
page of the Shefferson lab website.