In this vignette, we use the `lathyrus`

dataset to illustrate
the estimation of **age-by-stage function-based MPMs**.
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. In these
cases, we include hashtagged versions of these calls, and our text
assumes that the user runs these statements without hashtags. 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. This website also includes links
to long-format vignettes and other demonstrations of this package.

*Lathyrus vernus* (family Fabaceae) is a long-lived forest herb,
native to Europe and large parts of northern Asia. Individuals increase
slowly in size and usually flower only after 10-15 years of vegetative
growth. Flowering individuals have an average conditional lifespan of
44.3 years (Ehrlén and Lehtila 2002).
*L. vernus* lacks organs for vegetative spread and individuals
are well delimited (Ehrlén 2002). One or
several erect shoots of up to 40 cm height emerge from a subterranean
rhizome in March-April. Flowering occurs about four weeks after shoot
emergence. Shoot growth is determinate, and the number of flowers is
determined in the previous year (Ehrlén and Van
Groenendael 2001). Individuals may not produce aboveground
structures every year, but can remain dormant in one or more seasons.
*L. vernus* is self-compatible but requires visits from
bumble-bees to produce seeds. Individuals produce few, large seeds and
establishment from seeds is relatively frequent (Ehrlén and Eriksson 1996). The pre-dispersal
seed predator *Bruchus atomarius* often consumes a large fraction
of developing seeds, and roe deer (*Capreolus capreolus*)
sometimes consume the shoots (Ehrlén and
Munzbergova 2009).

Data for this study were collected from six permanent plots in a
population of *L. vernus* located in a deciduous forest in the
Tullgarn area, SE Sweden (58.9496 N, 17.6097 E), during 1988–1991 (Ehrlén 1995). The six plots were similar with
regard to soil type, elevation, slope, and canopy cover. Within each
plot, all individuals were marked with numbered tags that remained over
the study period, and their locations were carefully mapped. New
individuals were included in the study in each year. Individuals were
recorded at least three times every growth season. At the time of shoot
emergence, we recorded whether individuals were alive and produced
above-ground shoots, and if shoots had been grazed. During flowering, we
recorded flower number and the height and diameter of all shoots. At
fruit maturation, we counted the number of intact and damaged seeds. To
derive a measure of above-ground size for each individual, we calculated
the volume of each shoot as \(\pi ×
(\frac{1}{2} diameter)^2 × height\), and summed the volumes of
all shoots. This measure is closely correlated with the dry mass of
aboveground tissues (\(R^2 = 0.924\),
\(P < 0.001\), \(n = 50\), log-transformed values; Ehrlén
1995). Size of individuals that had been grazed was estimated based on
measures of shoot diameter in grazed shoots, and the relationship
between shoot diameter and shoot height in non-grazed individuals. Only
individuals with an aboveground volume of more than 230 mm^{3}
flowered and produced fruits during this study. Individuals that lacked
aboveground structures in one season but reappeared in the following
year were considered dormant. Individuals that lacked aboveground
structures in two subsequent seasons were considered dead from the year
in which they first lacked aboveground structures. Probabilities of
seeds surviving to the next year, and of being present as seedlings or
seeds in the soil seed bank, were derived from separate yearly sowing
experiments in separate plots adjacent to each subplot (Ehrlén and Eriksson 1996).

Our goal in this exercise will be to produce ahistorical, age-by-stage
function-based matrices using the *Lathyrus* dataset. We will use
a different approach to size classification than in previous vignettes,
using the natural log of size instead of the normal size shown in the
dataset. Our reasoning has to do with the fact that volume is used as
the size metric here, and so should have an allometric relationship to
some vital rates (note that all size metrics have allometric
relationships, but this is clearer when size is based on something more
strongly related to mass, as is volume). We will also create both
reproductive and non-reproductive stages of the same size classes.

The dataset that we have provided is organized in horizontal format,
meaning that each row holds all of the data for a single, unique
individual, and columns correspond to individual condition in particular
monitoring occasions (which we refer to as *years* here, since
there was one main census in each year). The original spreadsheet file
used to keep the dataset has a repeating pattern to these columns, with
each year having a similarly arranged group of variables. Package
`lefko3`

includes functions to handle data in horizontal
format, as well as vertically formatted data (i.e. data for individuals
is broken up across rows, where each row is a unique combination of
individual and year in occasion *t*).

First, let’s clear memory and load the dataset.

```
rm(list=ls(all=TRUE))
library(lefko3)
data(lathyrus)
#summary(lathyrus)
```

This dataset includes information on 1,119 individuals, so there are
1,119 rows with data (not counting the header). There are 38 columns.
The first two columns are variables identifying each individual
(`SUBPLOT`

refers to the patch, and `GENET`

refers
to individual identity), with each individual’s data entirely restricted
to one row. This is followed by four sets of nine columns, each named
`VolumeXX`

, `lnVolXX`

, `FCODEXX`

,
`FlowXX`

, `IntactseedXX`

, `Dead19XX`

,
`DormantXX`

, `Missing19XX`

, and
`SeedlingXX`

, where `XX`

corresponds to the year
of observation and with years organized consecutively. Thus, columns
3-11 refer to year 1988, columns 12-20 refer to year 1989, etc. This
strictly repeated pattern allows us to manipulate the original dataset
quickly and efficiently via `lefko3`

. We should know the
total number of years of data in the dataset, which is four years here
(includes all years from and including 1988 to 1991). Ideally, we should
also have arranged the columns in the same order for each year, with
years in consecutive order with no extra columns between them. Note that
this order is not required, but it makes life easier because following a
strictly repeating pattern allows us to skip inputting the names or
numbers of each column of data directly later during demographic data
formatting (step 2a below).

To begin, we will create a **stageframe** for this dataset.
A stageframe is a data frame that describes all stages in the life
history of the organism, in a way usable by the functions in this
package and using stage names and classifications that completely match
those used in the dataset. It links the dataset and our analyses to our
life history model. In this case, the life history model is a life cycle
graph (Figure 8.1). This model is based on the life history model
provided in Ehrlén (2000), but we utilize
a different size classification based on the log leaf volume to make the
size distribution more closely match a symmetric and somewhat normal
distribution.

Figure 8.1. Life history model of *Lathyrus vernus* using the
log leaf volume as the size classification metric.

Our stageframe needs to include complete descriptions of all stages that occur in the dataset, with each stage defined uniquely, and also needs to describe for each stage portrayed in our life history model. Since this object can be used for automated classification of individuals, all sizes, reproductive states, and other characteristics defining each stage in the dataset need to be accounted for explicitly. However, combinations of life history characteristics that appear to be outliers should be excluded, as they can lead to errors and issues in inference. So, great care must be taken to include all size values and values of other descriptor variables occurring within the dataset. The final description of each stage occurring in the dataset must not completely overlap with any other stage also found in the dataset, although partial overlap is allowed and expected.

Before creating the stageframe, let’s explore the possible size variables. We will particularly look at summaries of the distribution of original and log sizes.

```
summary(c(lathyrus$Volume88, lathyrus$Volume89, lathyrus$Volume90, lathyrus$Volume91))
#> Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
#> 1.8 14.7 123.0 484.2 732.5 7032.0 1248
summary(c(lathyrus$lnVol88, lathyrus$lnVol89, lathyrus$lnVol90, lathyrus$lnVol91))
#> Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
#> 0.600 2.700 4.800 4.777 6.600 8.900 1248
```

The upper summary shows the original size, while the lower line shows the size given in logarithmic terms. We should note the size minima and maxima, because we have been using 0 as the size of vegetatively dormant individuals. The lowest uncorrected size is 1.8, with a maximum of 7032. The minimum corrected size is 0.6, and the maximum corrected size is 8.9. Since the minimum corrected size is positive, and since the number of NAs has not increased (increased NAs would suggest that some unusable log sizes occur in the dataset), we are still able to use the log size value 0 as an indicator of vegetative dormancy. Note, however, that vegetative dormancy is currently included in the many NAs that occur in size variables in this dataset.

It can also help to take a look at plots of these distributions. We will plot raw and log volume.

```
par(mfrow=c(1,2))
plot(density(c(lathyrus$Volume88, lathyrus$Volume89, lathyrus$Volume90,
$Volume91), na.rm = TRUE), main = "", xlab = "Volume", bty = "n")
lathyrusplot(density(c(lathyrus$lnVol88, lathyrus$lnVol89, lathyrus$lnVol90,
$lnVol91), na.rm = TRUE), main = "", xlab = "Log volume", bty = "n") lathyrus
```

Note how highly skewed the raw volume distribution is. This might cause some difficulty if we used raw size untransformed and with a Gaussian distribution. Certainly, a Gamma distribution would be perfectly justified, and users are urged to try that approach. We will use the log volume here, which looks ‘better’ than the raw volume distribution in the sense that it is closer to some semblance of a Gaussian distribution, mostly through an increased level of symmetry. We can then assume that log volume is Gaussian-distributed and that the mean bears no relationship to the variance.

We will now develop a stageframe that incorporates the log volume of size. We will build this by creating vectors of the values describing each stage, always in the same order.

```
<- c(0, 4.6, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 1, 2, 3, 4, 5, 6, 7, 8, 9)
sizevector <- c("Sd", "Sdl", "Dorm", "Sz1nr", "Sz2nr", "Sz3nr", "Sz4nr",
stagevector "Sz5nr","Sz6nr", "Sz7nr", "Sz8nr", "Sz9nr", "Sz1r", "Sz2r", "Sz3r", "Sz4r",
"Sz5r", "Sz6r", "Sz7r", "Sz8r", "Sz9r")
<- c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1)
repvector <- c(0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1)
obsvector <- c(0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1)
matvector <- c(1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0)
immvector <- c(1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0)
propvector <- c(0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1)
indataset <- c(0, 4.6, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5,
binvec 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5)
<- c(0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1)
minima <- c(NA, 1, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
maxima NA, NA, NA, NA, NA)
<- c("Dormant seed", "Seedling", "Dormant", "Size 1 Veg", "Size 2 Veg",
comments "Size 3 Veg", "Size 4 Veg", "Size 5 Veg", "Size 6 Veg", "Size 7 Veg",
"Size 8 Veg", "Size 9 Veg", "Size 1 Flo", "Size 2 Flo", "Size 3 Flo",
"Size 4 Flo", "Size 5 Flo", "Size 6 Flo", "Size 7 Flo", "Size 8 Flo",
"Size 9 Flo")
<- sf_create(sizes = sizevector, stagenames = stagevector,
lathframeln repstatus = repvector, obsstatus = obsvector, propstatus = propvector,
immstatus = immvector, matstatus = matvector, indataset = indataset,
binhalfwidth = binvec, minage = minima, maxage = maxima, comments = comments)
#lathframeln
```

Once the stageframe is created, we can standardize the dataset into
historically-formatted vertical (*hfv*) format. Here, ‘vertical’
format is a way of organizing demographic data in which each row
corresponds to the state of a single individual in two (if ahistorical)
or three (if historical) consecutive monitoring occasions. To handle
this, we will use the `verticalize3()`

function, which
creates historically-formatted vertical datasets, as below. We will also
replace NAs in size and fecundity variables with zeros for
`modelsearch`

to work properly when we build models of vital
rates, so we will now set `NAas0 = TRUE`

. Some care needs to
be taken with this last step, since some authors give missing values
extra meaning not present in a value of zero. In our case, a missing
value indicates that a plant was dead (both size and fecundity are
missing), was alive but not sprouting (size was missing), or was alive
but did not produce seed (fecundity was missing). In all cases, these NA
values may be replaced by 0, because other variables indicate those
conditions.

We also have two choices for use as our reproductive status and
fecundity variables. The first choice, `FCODE88`

indicates
whether a plant flowered. The second choice, `Intactseed88`

,
indicates the number of seed produced. Flowering plants typically have
different demographic characteristics than non-flowering plants, either
reflecting reproductive costs, or, conversely, because flowering plants
might have more resources and hence higher survival than non-flowering
plants. We should separate transitions among these two groups. So, let’s
use `FCODE88`

to indicate reproductive status, and
`Intactseed88`

to indicate fecundity.

Finally, note that in the input to the following function, we utilize a
strictly repeating pattern of variable names arranged in the same order
for each monitoring occasion. This arrangement allows us to enter only
the first variable in each set, as long as `noyears`

and
`blocksize`

are set properly and no gaps or shuffles appear
in the dataset. The data management functions that we have created for
`lefko3`

do not require such repeating patterns, but they do
make the required input in the function much shorter and more succinct.

```
<- verticalize3(lathyrus, noyears = 4, firstyear = 1988,
lathvertln patchidcol = "SUBPLOT", individcol = "GENET", blocksize = 9,
juvcol = "Seedling1988", sizeacol = "lnVol88", repstracol = "FCODE88",
fecacol = "Intactseed88", deadacol = "Dead1988",
nonobsacol = "Dormant1988", stageassign = lathframeln,
stagesize = "sizea", censorcol = "Missing1988", censorkeep = NA,
NAas0 = TRUE, censor = TRUE)
#summary(lathvertln)
```

Before we move on to the next key steps in analysis, let’s take a closer look at fecundity. In this dataset, fecundity is mostly a count of intact seeds, and only differs in six cases where the seed output was estimated based on other models. To see this, try the following code.

```
writeLines(paste0("Length of fecundity vaiable in t+1: ", length(lathvertln$feca3)))
#> Length of fecundity vaiable in t+1: 2552
writeLines(paste0("# non-integer entries: ", length(which(lathvertln$feca3 !=
round(lathvertln$feca3)))))
#> # non-integer entries: 0
writeLines(paste0("Length of fecundity variable in t: ", length(lathvertln$feca2)))
#> Length of fecundity variable in t: 2552
writeLines(paste0("# non-integer entries: ", length(which(lathvertln$feca2 !=
round(lathvertln$feca2)))))
#> # non-integer entries: 6
writeLines(paste0("Length of fecundity variable in t-1: ", length(lathvertln$feca1)))
#> Length of fecundity variable in t-1: 2552
writeLines(paste0("# non-integer entries: ", length(which(lathvertln$feca1 !=
round(lathvertln$feca1)))))
#> # non-integer entries: 6
```

We see that we have quite a bit of fecundity data, and that it is overwhelmingly but not exclusively composed of integer values. The six non-integer values force us to make a decision - should we treat fecundity as a continuous variable, or round the values and treat it as a count variable? This is actually a vital decision that will strongly affect our vital rate model of fecundity, because treating it as continuous will also imply particular relationships between the mean and the variance. Here, we will round fecundity so that we can treat fecundity as a count variable in the analysis.

```
$feca3 <- round(lathvertln$feca3)
lathvertln$feca2 <- round(lathvertln$feca2)
lathvertln$feca1 <- round(lathvertln$feca1) lathvertln
```

The fact that fecundity is now integer-based suggests that it can be treated as a count variable. This package currently allows eight choices of count distributions: Gaussian, Gamma, Poisson, negative binomial, zero-inflated Poisson, zero-inflated negative binomial, zero-truncated Poisson, and zero-truncated negative binomial. To assess which to use, we should first assess whether the mean and variance of the count are equal using a dispersion test. This test allows us to test whether the variance is greater than that expected under our mean value for fecundity using a chi-squared test. If it is not significantly different, then we may use some variant of the Poisson distribution. If the data are overdispersed, then we should use the negative binomial distribution. We should also test whether the number of zeroes is more than expected under these distributions, and make the distribution zero-inflated if so. Note that, because we have not excluded zeros from fecundity using reproductive status, we should not use a zero-truncated distribution.

Let’s start off by looking at a bar plot of the distribution of fecundity.

```
hist(subset(lathvertln, repstatus2 == 1)$feca2, main = "Fecundity",
xlab = "Intact seeds produced in occasion t")
```

We see that the distribution conforms to a classic count variable with a very low mean value. The first bar suggests that there may be too many zeroes, as well. Let’s now formally test our assumptions, that the mean equals the variance and that the number of zeroes meets expectation. Both tests use chi-squared distribution-based approaches, with the zero-inflation test in particular based on Broek (1995).

```
sf_distrib(lathvertln, sizea = c("sizea3", "sizea2"), fec = c("feca3", "feca2"),
obs3 = "obsstatus3", repst = c("repstatus3", "repstatus2"))
#> Non-integer values detected, so will not test for overdispersion and zero-inflation in sizea
#> Mean fec is 4.825
#>
#> The variance in fec is 72.74
#>
#> The probability of this dispersion level by chance assuming that
#> the true mean fec = variance in fec,
#> and an alternative hypothesis of overdispersion, is 0
#>
#> Fecundity is significantly overdispersed.
#>
#>
#> Mean lambda in fec is 0.008029
#> The actual number of 0s in fec is 302
#> The expected number of 0s in fec under the null hypothesis is 4.352
#> The probability of this deviation in 0s from expectation by chance is 0
#>
#> Fecundity is significantly zero-inflated.
```

Such significant results for fecundity show us that we should use a zero-inflated negative binomial distribution. We will also treat size as Gaussian.

Matrix estimation functions in package `lefko3`

are made to
work with **supplement tables**, which provide extra data
for matrix estimation that is not included in the main demographic
dataset. The `supplemental()`

function provides a means of
incorporating four additional kinds of data into MPM construction:

- fixed transition values derived from other studies and added as constants to matrices,
- proxy transition values when data for particular transitions do not exist and other, estimable transitions need to be used as proxies,
- reproductive multipliers indicating which stages lead to the production of which stages, and at what level relative to estimated fecundity, as well as multipliers for proxy transitions, and
- survival multipliers, in cases in which proxy survival transitions are used but must be set at elevated or reduced levels relative to the original transition.

Here, we will create an ahistorical supplemental table organizing some of these sorts of data. Each row refers to a specific transition. The first two of these transitions are set to specific probabilities, which are the probabilities of germination and seed dormancy, estimated from a separate study. The final two terms are fecundity multipliers, which mark which transitions correspond to fecundity and provide information on what multiple of fecundity estimated via linear modeling applies to each case.

```
<- supplemental(stage3 = c("Sd", "Sdl", "Sd", "Sdl"),
lathsupp2 stage2 = c("Sd", "Sd", "rep", "rep"),
givenrate = c(0.345, 0.054, NA, NA),
multiplier = c(NA, NA, 0.345, 0.054),
type = c(1, 1, 3, 3), stageframe = lathframeln, historical = FALSE)
#lathsupp2
```

These supplement tables provide the best means of adding external data
to our MPMs because they allow both specific transitions to be isolated,
and because they allow the use of shorthand to identify large groups of
transitions (e.g. using `mat`

, `immat`

,
`rep`

, `nrep`

, `prop`

,
`npr`

, `obs`

, `nobs`

,
`groupX`

, or `all`

to signify all mature stages,
immature stages, reproductive stages, non-reproductive stages, propagule
stages, non-propagule stages, observable stages, unobservable stages,
stages within group `X`

, or simply all stages, respectively).
They also allow more powerful changes to MPMs, such as block reset of
groups of transitions to zero or other constants.

Next we will run the `modelsearch`

function with the new
vertical dataset. This function will develop our best-fit vital rate
models for us. This function looks simple, but it automates several
crucial and complex tasks in MPM analysis. Specifically, it automates 1)
the building of global models for each vital rate requested, 2) the
exhaustive construction of all reduced models, and 3) the selection of
the best-fit models.

Let’s look at just some of the options that we can utilize for this purpose.

**historical**: Setting this to `TRUE`

or
`FALSE`

indicates to include state in occasion *t*-1
in the global models.

**approach**: Designates whether to use generalized
linear models (`glm`

), in which all factors are treated as
fixed, or mixed effects models (`mixed`

), in which factors
are treated as either fixed or random (most notably, time, patch, and
individual identity can be treated as random).

**suite**: Designates which factors to include in the
global model. Possible values include `size`

, which includes
size only; `rep`

, which includes reproductive status only;
`main`

, which includes both size and reproductive status as
main effects only; `full`

, which includes both size and
reproductive status and all two-way interactions; and `cons`

,
which includes only an intercept. These factors are in addition to
individual identity, patch, and time.

**vitalrates**: Designates which vital rates to model.
The default is to model survival, size, and fecundity, but users can
also model observation status and reproduction status or drop some
terms.

**juvestimate**: Designates the name of the juvenile
stage transitioning to maturity, in cases where the dataset includes
data on juveniles.

**juvsize**: Denotes whether size should be used as an
independent term in models involving transition from the juvenile
stage.

**bestfit**: Denotes whether the best-fit model should
be chosen as the model with the lowest AICc (`AICc`

) or as
the most parsimonious model (`AICc&k`

), where the latter
is the model that has the fewest estimated parameters and is within 2
AICc units of the model with the lowest AICc.

**sizedist**: Designates the distribution used for size.
The options include `gaussian`

, `gamma`

,
`poisson`

, and `negbin`

, the last of which refers
to the negative binomial distribution (quadratic parameterization).
Versions also exist for up to two further size variables in addition to
primary size.

**ipm_method**: Designates whether to estimate size
transition probabilities using the cumulative density function (CDF)
method (the default), or the midpoint method. The CDF method is exact
while the midpoint method generally overestimates (see Doak et al. (2021) for further details).

**fecdist**: Designates the distribution used for
fecundity. The options include `gaussian`

,
`gamma`

, `poisson`

, and `negbin`

, the
last of which refers to the negative binomial distribution (quadratic
parameterization).

**fectime**: Designates whether fecundity is estimated
within occasion *t* (the default) or occasion *t*+1. Plant
ecologists are likely to choose the former, since fecundity is typically
estimated via a proxy such as flowers, fruit, or seed produced. Wildlife
ecologists might choose the latter, since fecundity may be best
estimated as a count of actual juveniles within nests, burrows, or other
family structures.

**size.zero**: Designates whether the size distribution
should be zero-inflated. Only applies to the Poisson and negative
binomial distributions. Versions also exist for up to two further size
variables in addition to primary size.

**size.trunc**: Designates whether to use a
zero-truncated distribution for size. Only applies to the Poisson and
negative binomial distributions, and cannot be applied if
`size.zero = TRUE`

. Versions also exist for up to two further
size variables in addition to primary size.

**fec.zero**: Designates whether the fecundity
distribution should be zero-inflated. Only applies to the Poisson and
negative binomial distributions.

**fec.trunc**: Designates whether to use a
zero-truncated distribution for fecundity. Only applies to the Poisson
and negative binomial distributions, and cannot be applied if
`fec.zero = TRUE`

.

**jsize.zero**: Designates whether the juvenile size
distribution should be zero-inflated. Only applies to the Poisson and
negative binomial distributions. Versions also exist for up to two
further size variables in addition to primary size.

**jsize.trunc**: Designates whether to use a
zero-truncated distribution for juvenile size. Only applies to the
Poisson and negative binomial distributions, and cannot be applied if
`jsize.zero = TRUE`

. Versions also exist for up to two
further size variables in addition to primary size.

**indiv**: Designates the variable corresponding to
individual identity in the vertical dataset.

**patch**: Designates the variable corresponding to
patch identity in the vertical dataset.

**year**: Designates the variable corresponding to
occasion *t* in the vertical dataset.

**age**: Designates the name of the variable
corresponding to age in occasion *t*. Should be set **only
if an age-by-stage model is desired**. Do not use this option to
produce a model that does not incorporate age.

**year.as.random**: Designates whether to treat year as
a random factor. Only used when a mixed effects modeling approach is
chosen (`approach = "mixed"`

).

**patch.as.random**: Designates whether to treat patch
identity as a random factor. Only used when a mixed effects modeling
approach is chosen (`approach = "mixed"`

).

**show.model.tables**: Designates whether to include the
full dredge model tables showing all models developed and their
associated AICc values.

**quiet**: Denotes whether to silence most guidepost
statements and warning messages during vital rate modeling. If
`TRUE`

, then only warnings incurred in model testing will be
visible, including warnings from the testing of global models and those
from the testing of reduced models produced during model dredging.

Here, we will create two ahistorical model sets. The first will be a
model set for the entire population, without separating patches. The
second will include patch as a random factor, and will thus allow us to
explore patch dynamics as well as population dynamics. We will not
create a historical set this time because we are producing an age x
stage MPM only - `lefko3`

does not currently estimate or
support historical age x stage MPMs.

```
<- modelsearch(lathvertln, historical = FALSE,
lathmodelsln2 approach = "mixed", suite = "main",
vitalrates = c("surv", "obs", "size", "repst", "fec"), juvestimate = "Sdl",
bestfit = "AICc&k", sizedist = "gaussian", fecdist = "negbin",
indiv = "individ", year = "year2", age = "obsage", year.as.random = TRUE,
patch.as.random = TRUE, show.model.tables = TRUE, fec.zero = TRUE, quiet = TRUE)
<- modelsearch(lathvertln, historical = FALSE,
lathmodelsln2p approach = "mixed", suite = "main",
vitalrates = c("surv", "obs", "size", "repst", "fec"), juvestimate = "Sdl",
bestfit = "AICc&k", sizedist = "gaussian", fecdist = "negbin",
indiv = "individ", patch = "patchid", year = "year2", age = "obsage",
year.as.random = TRUE, patch.as.random = TRUE, show.model.tables = TRUE,
fec.zero = TRUE, quiet = TRUE)
#summary(lathmodelsln2)
#summary(lathmodelsln2p)
```

The output can be rather verbose, and so we have limited it with the
`quiet = TRUE`

option. The function was developed to provide
text marker posts of what the function is doing and what it has
accomplished, as well as to show all warnings from all workhorse
functions used. Because we used the `mixed`

approach here,
this includes warnings originating from estimating mixed linear models
with package `lme4`

(Bates et al.
2015) and, in the case of fecundity, the package
`glmmTMB`

(Brooks et al. 2017).
It also shows warnings originating from the `dredge()`

function of package `MuMIn`

(Bartoń
2014), which is the core function used in model building and AICc
estimation. We encourage users to get familiar with interpreting these
warnings and assessing the degree to which they impact their own
analyses.

In developing these linear models, the `modelsearch()`

function created a series of nested datasets to estimate vital rates as
conditional rates and probabilities. The exact subsets created depend on
which parameters were called for. It is important to consider which are
required, and particular attention needs to be paid if there are size
classes with sizes of 0. In these situations, it is best to consider a
size of 0 as unobservable, and so to introduce observation status as a
vital rate to absorb these sizes. This sets up datasets for size
estimation that do not include 0 in the response term, because all 0
responses are already absorbed by observation status.

A look at the summaries shows that the best-fit models vary in complexity. Age is important in four of the adult vital rates, in both the population-only model set as well as the patch model set. For example, survival is influenced by reproductive status and age in the current year, as well as by patch, individual identity, and year, while observation status is not influenced by age. We can see these models explicitly, as well as the model tables developed, by calling them directly from the lefkoMod object. The accuracy of our models has a tendency to be in the mid-range, meaning that it is likely that we have not included some important factors accounting for variability in at least some of our vital rates.

Next, we will estimate the ahistorical sets of matrices. We will match
the ahistorical age-by-stage matrix estimation function,
`aflefko2()`

, with the appropriate ahistorical input,
including the ahistorical lefkoMod objects `lathmodelsln2`

and `lathmodelsln2p`

. Model sets that include historical
terms should not be used to create ahistorical matrices, since the
coefficients in the best-fit models are estimated assuming a specific
model structure that either relies on historical terms or does not.
Historical vital rate models may yield biased results if used to
construct ahistorical matrices. Also note that `lefko3`

does
not currently allow the construction of historical age-by-stage MPMs. We
will assume a prebreeding model, and set the maximum age to 3, per our
dataset, but note that individuals may move past this age with
`continue = TRUE`

.

```
<- aflefko2(year = "all", stageframe = lathframeln,
lathmat2age modelsuite = lathmodelsln2, data = lathvertln, supplement = lathsupp2,
final_age = 3, continue = TRUE, reduce = FALSE)
<- aflefko2(year = "all", patch = "all", stageframe = lathframeln,
lathmat2agep modelsuite = lathmodelsln2p, data = lathvertln, supplement = lathsupp2,
final_age = 3, continue = TRUE, reduce = FALSE)
#summary(lathmat2age)
#summary(lathmat2agep)
```

The first model set led to the development of three matrices, reflecting
the four years of data. The second model set led to the development of
18 matrices, reflecting four years and six patches. The quality control
section gives us a sense of the amount of data used to model each vital
rate, and also shows us that the survival-transition (`U`

)
matrices are composed entirely of proper probabilities yielding stage
survival probability falling between 0.0 and 1.0. Matrix estimation
protocols can sometimes create spurious values, such as stage survival
greater than 1.0. Such values can occur for a variety of reasons, but
the most common is the inclusion through a supplement table of
externally-determined survival probabilities that are too high. Make
sure to check your matrix column sums each time you estimate MPMs to
prevent this problem. Survival greater than 1.0 can lead to strange
effects on metrics of population dynamics.

We can get a sense of what these matrices look like by visualizing them.
Let’s use the `image3()`

function to look at the first one.

`image3(lathmat2age, used = 1)`

```
#> [[1]]
#> NULL
```

The clear squares refer to zero elements, and the red elements refer to non-zero values corresponding to survival transitions and fecundity. The vast number of zeros may be surprising, but this matrix is a supermatrix organized by age first, with stage organizing within-age blocks. The first age is age 0, which cannot be adult, and age 1 corresponds to seedlings, leading to most non-zero elements in the adult portion. The adult block occurs from age 2, and this block can perpetuate indefinitely. The number of elements estimated is greater now than in the typical ahistorical MPM, because now we have added age as a major factor for analysis. This matrix is overwhelmingly composed of elements that must be 0, and so it is a rather sparse matrix ((726 + 36) / 3969 = 19.2% of elements).

We can see the order of ages and stages using the `agestages`

element of the lefkoMat object we produced, as below. Note that our
matrix is 63 rows by 63 columns, and this object gives us the exact
order used.

`#lathmat2age$agestages`

Now let’s estimate the element-wise arithmetic mean matrices. The first
`lefkoMat`

object created will include a single mean matrix,
while the second will include six patch-level mean matrices followed by
a grand mean matrix, yielding seven matrices (remember to look at the
`labels`

element of the output to see the exact order of
matrices).

```
<- lmean(lathmat2age)
lathmat2mean <- lmean(lathmat2agep)
lathmat2pmean #summary(lathmat2mean)
#summary(lathmat2pmean)
```

Now let’s estimate the deterministic population growth rate \(\lambda\), and the stochastic population growth rate, \(a = \text{log} \lambda _{S}\), in our age x stage MPM. We’ll plot them for comparison, making sure to take the natural exponent of the stochastic growth rate to make it comparable to \(\lambda\). We will show the patch means and the grand population mean.

```
<- lambda3(lathmat2age)
lambda2 <- lambda3(lathmat2mean)
lambda2m <- lambda3(lathmat2pmean)
lambda2mp set.seed(42)
<- slambda3(lathmat2age) #Stochastic growth rate
sl2 $expa <- exp(sl2$a)
sl2
par(mfrow = c(1,2))
plot(lambda ~ year2, data = lambda2, ylim = c(0.90, 0.98),xlab = "Year",
ylab = expression(lambda), type = "l", lty= 1, lwd = 2, bty = "n")
abline(a = lambda2m$lambda[1], b = 0, lty = 2, lwd= 2, col = "orangered")
abline(a = sl2$expa[1], b = 0, lty = 2, lwd= 3, col = "darkred")
legend("bottomleft", c("det annual", "det mean", "stochastic"), lty = c(1, 2, 2),
col = c("black", "orangered", "darkred"), lwd = c(2, 2, 3), bty = "n")
plot(lambda ~ patch, data = lambda2mp[1:6,], ylim = c(0.90, 0.98), xlab = "Patch",
ylab = expression(lambda), type = "l", lty= 1, lwd = 2, bty = "n")
abline(a = lambda2m$lambda[1], b = 0, lty = 2, lwd= 2, col = "orangered")
abline(a = sl2$expa[1], b = 0, lty = 2, lwd= 2, col = "darkred")
legend("bottomleft", c("patch det mean", "pop det mean", "pop sto"), lty = c(1, 2, 2),
col = c("black", "orangered", "darkred"), lwd = 2, bty = "n")
```

Deterministic and stochastic analyses suggest that the population and
all patches are declining, with all years and patches associated with
\(\lambda < 1\) and \(a = \text{log} \lambda _{S} < 0\).
Qualitatively, they agree with the \(\lambda\) and \(a
= \text{log} \lambda _{S}\) estimates from our other
*Lathyrus* vignettes.

Now let’s look at the stable stage distribution, using the population
matrices rather than the patch matrices. The output for the ahistorical
MPM is a data frame with matrix of origin, stage name, and stable stage
proportion in each row. Because the default output for the
`stablestage3()`

function will give us the stable
distribution of age-stages, we will collapse the output across age to
produce a true stable stage distribution.

```
<- stablestage3(lathmat2mean)
ehrlen2ss <- stablestage3(lathmat2age, stochastic = TRUE, seed = 42)
ehrlen2ss_s
<- cbind.data.frame(ehrlen2ss$ss_prop, ehrlen2ss_s$ss_prop)
ss_props names(ss_props) <- c("det", "sto")
rownames(ss_props) <- paste(ehrlen2ss$age, ehrlen2ss$stage)
<- apply(as.matrix(c(1:21)), 1, function(X) {
det_dist <- ss_props$det[X] + ss_props$det[21 + X] + ss_props$det[42 + X]
ss_sum return(ss_sum)
})<- apply(as.matrix(c(1:21)), 1, function(X) {
sto_dist <- ss_props$sto[X] + ss_props$sto[21 + X] + ss_props$sto[42 + X]
ss_sum return(ss_sum)
})
barplot(t(cbind.data.frame(det_dist, sto_dist)), beside = T, ylab = "Proportion",
xaxt = "n", ylim = c(0, 0.2), col = c("black", "red"), bty = "n")
text(cex=1, x=seq(from = 0.5, to = 3 * length(lathframeln$stage), by = 3),
y=-0.055, lathframeln$stage, xpd=TRUE, srt=45)
legend("topright", c("deterministic", "stochastic"), col = c("black", "red"),
pch = 15, bty = "n")
```

The stable stage distribution shows high levels of dormant seeds and mid- to large-sized adults, followed by dormant adults. Small-sized adults comprise the smallest part of the stable age-stage structure. Deterministic and stochastic analyses show more or less the same results.

Now let’s take look at the reproductive values. We’ll go straight to the plots, as with the stable stage distribution.

```
<- repvalue3(lathmat2mean)
ehrlen2rv <- repvalue3(lathmat2age, stochastic = TRUE, seed = 42)
ehrlen2rv_s
barplot(t(cbind.data.frame(ehrlen2rv$rep_value, ehrlen2rv_s$rep_value)),
beside = T, ylab = "Relative rep value", xlab = "Age-Stage", ylim = c(0, 1.5),
col = c("black", "red"), bty = "n")
text(cex=0.5, y = -0.06, x = seq(from = 0, to = 2.98*length(rownames(ss_props)),
by = 3), rownames(ss_props), xpd=TRUE, srt=45)
legend("topleft", c("deterministic", "stochastic"), col = c("black", "red"),
pch = 15, bty = "n")
```

We see reproductive values above 0 starting with dormant adults, and generally increasing into larger sizes with some interesting patterns in ages 2 and 3. These patterns hold in both deterministic and stochastic analyses.

We will next assess to which matrix elements \(\lambda\) is most sensitive and elastic to changes in. As before, we will look at both deterministic and stochastic sensitivities.

```
<- sensitivity3(lathmat2mean)
lathmat2msens set.seed(42)
<- sensitivity3(lathmat2age, stochastic = TRUE)
lathmat2msens_s
writeLines(paste0("The highest deterministic sensitivity value: ",
max(lathmat2msens$ah_sensmats[[1]][which(lathmat2mean$A[[1]] > 0)])))
#> The highest deterministic sensitivity value: 0.121270537991596
writeLines(paste0("This value is associated with element: ",
which(lathmat2msens$ah_sensmats[[1]] ==
max(lathmat2msens$ah_sensmats[[1]][which(lathmat2mean$A[[1]] > 0)]))))
#> This value is associated with element: 3208
writeLines(paste0("The highest stochastic sensitivity value: ",
max(lathmat2msens_s$ah_sensmats[[1]][which(lathmat2mean$A[[1]] > 0)])))
#> The highest stochastic sensitivity value: 0.125742280796789
writeLines(paste0("This value is associated with element: ",
which(lathmat2msens_s$ah_sensmats[[1]] ==
max(lathmat2msens_s$ah_sensmats[[1]][which(lathmat2mean$A[[1]] > 0)]))))
#> This value is associated with element: 3838
```

The highest deterministic sensitivity value in both analyses appears to
be associated with the transition from non-flowering six-sprouted adults
in age 3 or higher to flowering four-sprouted adult (element 3208, in
column 51, row 58). The highest stochastic sensitivity value is
assocaited with the transition from flowering seven-sprouted adult in
age 3 or higher to flowering five-sprouted adult (element 3838, in
column 61, row 59). Inspecting the sensitivity matrix (type
`lathmat2msens$ah_sensmats[[1]]`

to view the full
deterministic sensitivity matrix, or
`lathmat2msens_s$ah_sensmats[[1]]`

to view the full
stochastic sensitivity matrix) also shows that transitions near these
elements are associated with rather high sensitivities.

Let’s now assess the elasticity of \(\lambda\) or \(\text{log} \lambda\) to matrix elements.

```
<- elasticity3(lathmat2mean)
lathmat2melas set.seed(42)
<- elasticity3(lathmat2age, stochastic = TRUE)
lathmat2melas_s
writeLines(paste0("The highest deterministic elasticity value: ",
max(lathmat2melas$ah_elasmats[[1]][which(lathmat2mean$A[[1]] > 0)])))
#> The highest deterministic elasticity value: 0.0275294186982584
writeLines(paste0("The largest determnistic elasticity is associated with element: ",
which(lathmat2melas$ah_elasmats[[1]] == max(lathmat2melas$ah_elasmats[[1]]))))
#> The largest determnistic elasticity is associated with element: 2820
writeLines(paste0("The highest stochastic elasticity value: ",
max(lathmat2melas_s$ah_elasmats[[1]][which(lathmat2mean$A[[1]] > 0)])))
#> The highest stochastic elasticity value: 0.0274915321942021
writeLines(paste0("The largest stochastic elasticity is associated with element: ",
which(lathmat2melas_s$ah_elasmats[[1]] == max(lathmat2melas_s$ah_elasmats[[1]]))))
#> The largest stochastic elasticity is associated with element: 2820
```

Both deterministic and stochastic analyses show that population growth rate is most elastic to element 2820, which is in column 45 and row 48. Checking our stageframe shows that this element is associated with transition from dormant adult age 3 or higher to non-flowering three-sprouted adult.

Elasticity values can be treated as additive and sum to 1.0 within single matrices. This allows us to make interesting comparisons, such as to compare the elasticity of population growth rate to changes in transitions associated with specific stages. Let’s conduct such an analysis, and plot the results.

```
<- cbind.data.frame(colSums(lathmat2melas$ah_elasmats[[1]]),
elas_put_together colSums(lathmat2melas_s$ah_elasmats[[1]]))
names(elas_put_together) <- c("det", "sto")
rownames(elas_put_together) <- apply(as.matrix(c(1:dim(lathmat2melas$agestages)[1])),
1, function(X) {
paste(lathmat2melas$agestages$stage[X], lathmat2melas$agestages$age[X])
})
barplot(t(elas_put_together), beside=T, names.arg = rep(NA,
length(rownames(elas_put_together))), ylab = "Elasticity", xlab = "Stage",
col = c("black", "orangered"), bty = "n")
text(cex=0.5, y = -0.007, x = seq(from = 0, to = 2.98*length(rownames(elas_put_together)),
by = 3), rownames(elas_put_together), xpd=TRUE, srt=45)
legend("topleft", c("deterministic", "stochastic"), col = c("black", "orangered"),
pch = 15, bty = "n")
```

We see in the analysis above that in both the deterministic and stochastic cases, the population growth rate is most elastic to changes associated with flowering and non-flowering mid-sized adults, and with vegetatively dormant adults, in the highest ages. Juvenile stages and dormant seeds have little influence.

Our estimated elasticities can also be summarized by transition type.
Let’s take a look at a plot, using the `summary.lefkoElas()`

function to help us assess these transition types.

```
<- summary(lathmat2melas)
lathmat2m_sums <- summary(lathmat2melas_s)
lathmat2m_s_sums
<- cbind.data.frame(lathmat2m_sums$ahist[,2],
elas_sums_together $ahist[,2])
lathmat2m_s_sumsnames(elas_sums_together) <- c("det", "sto")
rownames(elas_sums_together) <- lathmat2m_sums$ahist$category
barplot(t(elas_sums_together), beside=T, ylab = "Elasticity", xlab = "Transition",
col = c("black", "orangered"), bty = "n")
legend("topright", c("deterministic", "stochastic"), col = c("black", "orangered"),
pch = 15, bty = "n")
```

We see in the plot above that deterministic and stochastic analyses agree strongly about the importance and influence of transition types to population growth rate. Specifically, the population growth rate is most strongly elastic in response to changes in growth transitions, and almost completely inelastic to changes in fecundity. Shrinkage is also fairly influential.

In addition to sensitivity and elasticity analyses, we can use package
`lefko3`

to assess the actual impacts of demographic shifts
or differences on the population growth rate. Two tools for this purpose
are the life table response experiment (LTRE), and its stochastic
version the stochastic life table response experiment (sLTRE). Both are
available via the `ltre3()`

function. Below, we perform a
standard deterministic LTRE to assess the impact of space on the
population growth rate. This is done via a comparison of patch-level
demography to the grand mean matrix, which is the default comparison if
no reference matrices are provided. Remove the hashtag from the second
line to see the full structure of the resulting object. Particularly,
you will notice that the first element, `ltre_det`

, is a list
of six matrices. These matrices show the actual impact of the difference
in elements between the respective patch-level matrix and the reference
matrix (here, the grand population mean matrix) on the population growth
rate \(\lambda\). After this, we see
similar structure to the input `lefkoMat`

object, including
the stageframe and order of matrices.

```
<- ltre3(lathmat2pmean)
lathmat2m_ltre #> Warning: Matrices input as mats will also be used as reference matrices.
#> Using all refmats matrices as reference matrices.
#lathmat2m_ltre
```

The sLTRE produces output that is a bit different. Here, we see two
lists of matrices prior to the MPM metadata. The first,
`ltre_mean`

, is a list of matrices showing the impact of
differences in mean elements between the patch-level temporal mean
matrices and the reference temporal mean matrix. The second,
`ltre_sd`

, is a list of matrices showing the impact 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 \(\lambda\), the sLTRE shows the impacts of
changes in the mean and the variability of matrix elements on \(\text{log} \lambda\).

```
<- ltre3(lathmat2agep, stochastic = TRUE, rseed = 42)
lathmat2m_sltre #> Warning: Matrices input as mats will also be used as reference matrices.
#> Using all refmats matrices as reference matrices.
#lathmat2m_sltre
```

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 six patches, but for brevity we will focus only on the first matrix here. First, we will identify the elements most strongly impacting the population growth rate in each case.

```
writeLines(paste0("Highest (i.e most positive) deterministic LTRE contribution: ",
max(lathmat2m_ltre$ltre_det[[1]])))
#> Highest (i.e most positive) deterministic LTRE contribution: 0.00181737035024266
writeLines(paste0("Highest deterministic LTRE contribution is associated with element: ",
which(lathmat2m_ltre$ltre_det[[1]] == max(lathmat2m_ltre$ltre_det[[1]]))))
#> Highest deterministic LTRE contribution is associated with element: 3841
writeLines(paste0("Lowest (i.e. most negative) deterministic LTRE contribution: ",
min(lathmat2m_ltre$ltre_det[[1]])))
#> Lowest (i.e. most negative) deterministic LTRE contribution: -0.00324398944756521
writeLines(paste0("Lowest deterministic LTRE contribution is associated with element: ",
which(lathmat2m_ltre$ltre_det[[1]] == min(lathmat2m_ltre$ltre_det[[1]]))))
#> Lowest deterministic LTRE contribution is associated with element: 3825
writeLines(paste0("Highest stochastic mean LTRE contribution: ",
max(lathmat2m_sltre$ltre_mean[[1]])))
#> Highest stochastic mean LTRE contribution: 0.00168036799561467
writeLines(paste0("Highest stochastic mean LTRE contribution is associated with element: ",
which(lathmat2m_sltre$ltre_mean[[1]] == max(lathmat2m_sltre$ltre_mean[[1]]))))
#> Highest stochastic mean LTRE contribution is associated with element: 3841
writeLines(paste0("Lowest stochastic mean LTRE contribution: ",
min(lathmat2m_sltre$ltre_mean[[1]])))
#> Lowest stochastic mean LTRE contribution: -0.00373375466694055
writeLines(paste0("Lowest stochastic mean LTRE contribution is associated with element: ",
which(lathmat2m_sltre$ltre_mean[[1]] == min(lathmat2m_sltre$ltre_mean[[1]]))))
#> Lowest stochastic mean LTRE contribution is associated with element: 3195
writeLines(paste0("Highest stochastic SD LTRE contribution: ",
max(lathmat2m_sltre$ltre_sd[[1]])))
#> Highest stochastic SD LTRE contribution: 4.55638946540519e-05
writeLines(paste0("Highest stochastic SD LTRE contribution is associated with element: ",
which(lathmat2m_sltre$ltre_sd[[1]] == max(lathmat2m_sltre$ltre_sd[[1]]))))
#> Highest stochastic SD LTRE contribution is associated with element: 3138
writeLines(paste0("Lowest stochastic SD LTRE contribution: ",
min(lathmat2m_sltre$ltre_sd[[1]])))
#> Lowest stochastic SD LTRE contribution: -3.32295933559817e-05
writeLines(paste0("Lowest stochastic SD LTRE contribution is associated with element: ",
which(lathmat2m_sltre$ltre_sd[[1]] == min(lathmat2m_sltre$ltre_sd[[1]]))))
#> Lowest stochastic SD LTRE contribution is associated with element: 3843
writeLines(paste0("Total positive deterministic LTRE contributions: ",
sum(lathmat2m_ltre$ltre_det[[1]][which(lathmat2m_ltre$ltre_det[[1]] > 0)])))
#> Total positive deterministic LTRE contributions: 0.0397322318589641
writeLines(paste0("Total negative deterministic LTRE contributions: ",
sum(lathmat2m_ltre$ltre_det[[1]][which(lathmat2m_ltre$ltre_det[[1]] < 0)])))
#> Total negative deterministic LTRE contributions: -0.0314597246919546
writeLines(paste0("\nTotal positive stochastic mean LTRE contributions: ",
sum(lathmat2m_sltre$ltre_mean[[1]][which(lathmat2m_sltre$ltre_mean[[1]] > 0)])))
#>
#> Total positive stochastic mean LTRE contributions: 0.0397749033517226
writeLines(paste0("Total negative stochastic mean LTRE contributions: ",
sum(lathmat2m_sltre$ltre_mean[[1]][which(lathmat2m_sltre$ltre_mean[[1]] < 0)])))
#> Total negative stochastic mean LTRE contributions: -0.0365662783651413
writeLines(paste0("\nTotal positive stochastic SD LTRE contributions: ",
sum(lathmat2m_sltre$ltre_sd[[1]][which(lathmat2m_sltre$ltre_sd[[1]] > 0)])))
#>
#> Total positive stochastic SD LTRE contributions: 0.00048187454044065
writeLines(paste0("Total negative stochastic SD LTRE contributions: ",
sum(lathmat2m_sltre$ltre_sd[[1]][which(lathmat2m_sltre$ltre_sd[[1]] < 0)])))
#> Total negative stochastic SD LTRE contributions: -0.00060173340431452
```

All LTRE outputs show very small contributions from individual elements. However, element 3841 appears to be the most positive in both the deterministic and stochastic mean cases. This is the stasis transition for seven-sprouted flowering adults. Variability in elements appears to have much smaller impact on \(log \lambda\) than shifts in the means do.

Second, we will identify which age-stages exert the strongest impact on the population growth rate.

```
<- lathmat2m_ltre$ltre_det[[1]]
ltre_pos <- lathmat2m_ltre$ltre_det[[1]]
ltre_neg which(ltre_pos < 0)] <- 0
ltre_pos[which(ltre_neg > 0)] <- 0
ltre_neg[
<- lathmat2m_sltre$ltre_mean[[1]]
sltre_meanpos <- lathmat2m_sltre$ltre_mean[[1]]
sltre_meanneg which(sltre_meanpos < 0)] <- 0
sltre_meanpos[which(sltre_meanneg > 0)] <- 0
sltre_meanneg[
<- lathmat2m_sltre$ltre_sd[[1]]
sltre_sdpos <- lathmat2m_sltre$ltre_sd[[1]]
sltre_sdneg which(sltre_sdpos < 0)] <- 0
sltre_sdpos[which(sltre_sdneg > 0)] <- 0
sltre_sdneg[
<- cbind(colSums(ltre_pos), colSums(sltre_meanpos), colSums(sltre_sdpos))
ltresums_pos <- cbind(colSums(ltre_neg), colSums(sltre_meanneg), colSums(sltre_sdneg))
ltresums_neg
<- apply(as.matrix(c(1:length(lathmat2agep$agestages$stage))), 1,
ltre_as_names function(X) {
paste(lathmat2agep$agestages$stage[X], lathmat2agep$agestages$age[X])
})barplot(t(ltresums_pos), beside = T, col = c("black", "grey", "red"),
ylim = c(-0.006, 0.006))
barplot(t(ltresums_neg), beside = T, col = c("black", "grey", "red"), add = TRUE)
text(cex=0.5, y = -0.0065, x = seq(from = 0, to = 3.98*length(ltre_as_names),
by = 4), ltre_as_names, xpd=TRUE, srt=45)
legend("topleft", c("deterministic", "stochastic mean", "stochastic SD"),
col = c("black", "grey", "red"), pch = 15, bty = "n")
```

The output above shows that mid-sized non-flowering and flowering adults and seedlings exert strong influences on both \(\lambda\) and \(\text{log} \lambda\).

Finally, we will assess what transition types exert the greatest impact on population growth rate.

```
<- summary(lathmat2m_ltre)
lathmat2m_ltre_summary <- summary(lathmat2m_sltre)
lathmat2m_sltre_summary
<- cbind(lathmat2m_ltre_summary$ahist_det$matrix1_pos,
ltresums_tpos $ahist_mean$matrix1_pos,
lathmat2m_sltre_summary$ahist_sd$matrix1_pos)
lathmat2m_sltre_summary<- cbind(lathmat2m_ltre_summary$ahist_det$matrix1_neg,
ltresums_tneg $ahist_mean$matrix1_neg,
lathmat2m_sltre_summary$ahist_sd$matrix1_neg)
lathmat2m_sltre_summary
barplot(t(ltresums_tpos), beside = T, col = c("black", "grey", "red"),
ylim = c(-0.03, 0.03))
barplot(t(ltresums_tneg), beside = T, col = c("black", "grey", "red"),
add = TRUE)
abline(0, 0, lty = 3)
text(cex=0.85, y = -0.040, x = seq(from = 2,
to = 3.98*length(lathmat2m_ltre_summary$ahist_det$category), by = 4),
$ahist_det$category, xpd=TRUE, srt=45)
lathmat2m_ltre_summarylegend("topleft", c("deterministic", "stochastic mean", "stochastic SD"),
col = c("black", "grey", "red"), pch = 15, bty = "n")
```

The overall greatest impact on the population growth rate appears to come from growth and shrinkage transitions, with shrinkage transitions having strongly negative influences in general.

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. Package `lefko3`

also includes
functions 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.

We are grateful to two anonymous reviewers whose scrutiny improved the quality of this vignette. The project resulting in this package and this tutorial was funded by Grant-In-Aid 19H03298 from the Japan Society for the Promotion of Science.

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