Title: | An Open-source Tool to Guide Decisions for Wildlife Conservation |
---|---|
Description: | Compare alternative recovery actions: Linear Feature Restoration, Linear Feature Deactivation, Maternal Penning, Conservation Breeding, Predator Exclosure, Wolf Reduction, and Moose Reduction. Quantifying the trade-offs associated with alternative recovery actions for threatened species. The tool consists of a generalized matrix population model that is parametrized based on information from the published literature or ongoing experiments. Users can input population parameters (e.g., population size and survival rates) or choose from pre-set caribou subpopulations to estimate changes to populations from implementing recovery actions as described in Nagy-Reis et al. (2020) <doi:10.3389/fevo.2020.564508>. |
Authors: | Peter Solymos [aut, cre] , Melanie Dickie [aut], Sophie Gilbert [aut] , Mariana Nagy-Reis [aut], Rob Serrouya [aut], Alberta Biodiversity Monitoring Institute [cph], Province of British Columbia [cph] |
Maintainer: | Peter Solymos <[email protected]> |
License: | Apache License (== 2.0) | file LICENSE |
Version: | 0.3.1 |
Built: | 2024-09-23 02:57:59 UTC |
Source: | https://github.com/psolymos/WildLift |
Functions evaluate different options for conservation breeding.
wildlift_matrix(settings, wild=TRUE, age.cens=3, age.1st.litter=3, age.calf.max=1) wildlift_breeding(settings, in.inds=10, out.prop=1, f.surv.trans=1, j.surv.trans=1, j.surv.red=1, tmax=20, pop.start=100, breed.early=FALSE, f.preg.capt.2=0.57) ## S3 method for class 'wildlift_breeding' print(x, ...) ## S3 method for class 'wildlift_breeding' summary(object, ...) ## S3 method for class 'wildlift_breeding' plot(x, plot = TRUE, ...)
wildlift_matrix(settings, wild=TRUE, age.cens=3, age.1st.litter=3, age.calf.max=1) wildlift_breeding(settings, in.inds=10, out.prop=1, f.surv.trans=1, j.surv.trans=1, j.surv.red=1, tmax=20, pop.start=100, breed.early=FALSE, f.preg.capt.2=0.57) ## S3 method for class 'wildlift_breeding' print(x, ...) ## S3 method for class 'wildlift_breeding' summary(object, ...) ## S3 method for class 'wildlift_breeding' plot(x, plot = TRUE, ...)
settings |
a settings object returned by |
wild |
logical, whether to use the penned or wild vital |
age.cens |
censoring age. The projection matrix will contain one-year
age classes up to |
age.1st.litter |
integer, female age at 1st litter; lower limit of the age class, default is 3 years, i.e. the [3, Inf] interval. |
age.calf.max |
integer, maximum age to be considered as calf; upper limit of the age class, default is 1 year, i.e. the [0, 1) interval. |
in.inds |
integer vector, number of adult females put into pen each year.
0s are appended to the value meaning that no females
are added after the last value (up until year |
out.prop |
numeric vector between 0 and 1, the proportion of juvenile females
transferred from the penned to the recipient herd.
The last value is repeated when length is less than |
f.surv.trans |
numeric between 0 and 1, adult female survival during capture and transportation into the penned population. |
j.surv.trans |
numeric between 0 and 1, juvenile female survival during capture and transportation from penned to recipient herd. |
j.surv.red |
numeric between 0 and 1, transported juvenile female survival reduction factor for 1 year following capture and traportation. |
tmax |
positive integer, number of years to forecast after initial year 0. |
pop.start |
positive integer, initial population size in year 0 for the recipient and status quo populations (wild not receiving females). |
breed.early |
logical, allow females in the facility (not in the wild)
to reproduce at a younger age (2) if they are well fed.
Defaults to |
f.preg.capt.2 |
numeric, fecundity rate for the 2 yrs old (default is 0.57
based on Adam et al. 2019). Only applies when |
x , object
|
an object to print, summarize, plot, etc. |
plot |
logical, whether a plot is to be produced. |
... |
additional arguments to functions. |
The conservation breeding functionality is based on vital rates
provided via the settings
argument.
These rates are turned into a projection matrix
using wildlift_matrix
. This projection matrix
reflects survival and reproduction rates based on
the settings, tracking females only based on a sex ratio of 0.5.
The wildlift_breeding
function tracks changes
in the conservation population starting with adding
females to produce offspring.
Juvenile females are captured and transferred to the recipient herd.
The proportion of juvenile females transferred can be changed through
out.prop
(0: none, 1: all).
The output object tracks the number of adult females added to the penned population, and the number of juvenile females transferred from the penned to the recipient herd.
Besides these, a status quo (wild without receiving juveniles) population trajectory is used as a reference. This status quo population uses the same vital rates as the recipient herd, but does not get the extra juvenile females from the penned population.
wildlift_matrix
returns a projection matrix.
wildlift_breeding
returns a conservation breeding object.
It contains age specific population sizes for the
penned, recipient, and status quo populations (females only).
The print
method returns the input object x
invisibly.
The summary
method returns population summaries
for the conservation breeding object.
The plot
method return the plotted data invisibly
and produces a plot as a side effect.
wildlift_settings
, wildlift_forecast
## projection matrix s <- wildlift_settings() wildlift_matrix(s, wild=TRUE) # wild wildlift_matrix(s, wild=FALSE) # captive ## out.prop = 0.5: move half of the juveniles x0 <- wildlift_breeding(s, tmax = 20, # projection horizon in.inds = rep(10, 5), out.prop = 0.5) x0 summary(x0) ## out.prop = 1: move all juveniles x1 <- update(x0, out.prop = 1) x1 op <- par(mfrow=c(1, 2)) plot(x0, main="out.prop = 0") plot(x1, main="out.prop = 1") par(op)
## projection matrix s <- wildlift_settings() wildlift_matrix(s, wild=TRUE) # wild wildlift_matrix(s, wild=FALSE) # captive ## out.prop = 0.5: move half of the juveniles x0 <- wildlift_breeding(s, tmax = 20, # projection horizon in.inds = rep(10, 5), out.prop = 0.5) x0 summary(x0) ## out.prop = 1: move all juveniles x1 <- update(x0, out.prop = 1) x1 op <- par(mfrow=c(1, 2)) plot(x0, main="out.prop = 0") plot(x1, main="out.prop = 1") par(op)
Linear feature restoration (i.e. forest regeneration) and linear feature deactivation (e.g. blocking entrances) effects on lambda.
wildlift_linear(tmax=20, pop.start=100, area=10000, lin=0, seism=0, young=0, cost=12, yr_deact=5, yr_restor=15)
wildlift_linear(tmax=20, pop.start=100, area=10000, lin=0, seism=0, young=0, cost=12, yr_deact=5, yr_restor=15)
tmax |
positive integer, number of years to forecast after initial year 0. |
pop.start |
positive integer, initial population size in year 0. |
area |
positive numeric, area of range in km^2. |
lin |
non-negative numeric, linear features (total length in km)
including |
seism |
non-negative numeric, seismic lines (total length in km) to be restored or deactivated. |
young |
numeric between 0 and 100, percent of young (<30 yrs) forest. |
cost |
non-negative numeric, cost of management action (in canadian dollars) per km linear feature restored/deactivated. |
yr_deact |
non-negative numeric, years it takes for deactivation to reach a level where it begins to affect lambda. |
yr_restor |
non-negative numeric, years it takes for restoration to reach a level where it begins to affect lambda. |
Lambda is calculated as
1.0184 - 0.0234 * (lin
/ area
) - 0.0021 * young
.
A list with population table and costs.
## nothing to restore/deactivate wildlift_linear() ## all linear restored/deactivated wildlift_linear(lin=1000, seism=1000, young=10) ## half of linear is to be restored/deactivated wildlift_linear(lin=1000, seism=500, young=10)
## nothing to restore/deactivate wildlift_linear() ## all linear restored/deactivated wildlift_linear(lin=1000, seism=1000, young=10) ## half of linear is to be restored/deactivated wildlift_linear(lin=1000, seism=500, young=10)
Run the Shiny apps that are included in the package.
run_app(app = "WildLift")
run_app(app = "WildLift")
app |
character, which app to run. |
"WildLift"
: the default app.
Functions to set demographic and cost parameter, to perform forecasting, and to inspect the results.
wildlift_settings(pen.type = c("mat.pen", "pred.excl", "moose.red", "wolf.red", "cons.breed"), herd = NULL, ...) wildlift_forecast(settings, tmax = 20, pop.start = 100, fpen.prop, fpen.inds) wildlift_breakeven(forecast, lambda=1, type=c("prop", "inds"), max=10^4, tol=0.01) ## S3 method for class 'wildlift_settings' print(x, ...) ## S3 method for class 'wildlift_forecast' print(x, ...) ## S3 method for class 'wildlift_forecast' plot(x, plot = TRUE, ...) ## S3 method for class 'wildlift_forecast' lines(x, pen = TRUE, plot = TRUE, ...) ## S3 method for class 'wildlift_forecast' summary(object, ...) ## S3 method for class 'summary.wildlift_forecast' print(x, ...)
wildlift_settings(pen.type = c("mat.pen", "pred.excl", "moose.red", "wolf.red", "cons.breed"), herd = NULL, ...) wildlift_forecast(settings, tmax = 20, pop.start = 100, fpen.prop, fpen.inds) wildlift_breakeven(forecast, lambda=1, type=c("prop", "inds"), max=10^4, tol=0.01) ## S3 method for class 'wildlift_settings' print(x, ...) ## S3 method for class 'wildlift_forecast' print(x, ...) ## S3 method for class 'wildlift_forecast' plot(x, plot = TRUE, ...) ## S3 method for class 'wildlift_forecast' lines(x, pen = TRUE, plot = TRUE, ...) ## S3 method for class 'wildlift_forecast' summary(object, ...) ## S3 method for class 'summary.wildlift_forecast' print(x, ...)
pen.type |
character: maternity penning, predator exclusion, moose reduction, wolf reduction, or conservation breeding. It can also be a settings object. |
herd |
|
settings |
a settings object returned by |
tmax |
positive integer, number of years to forecast after initial year 0. |
pop.start |
positive integer, initial population size in year 0. |
fpen.prop , fpen.inds
|
|
forecast |
a forecast object returned by |
lambda |
numeric, annual intrinsic growth rate, must be >0. |
type |
what to provide as output: proportion ( |
max |
numeric (>0), maximum value for breakeven optimization when
|
tol |
numeric, tolerance limit, i.e. the maximum acceptable deviation
from |
x , object
|
an object to print, summarize, plot, etc. |
plot |
logical, whether a plot is to be produced. |
pen |
logical, whether to display the pen or no-pen (baseline) results. |
... |
additional arguments to functions. For |
The following cost parameters can be set by wildlift_settings
:
pen.cap
: how many adult females can live in a
single maternity pen.
pen.cost.setup
: initial cost in thousands to set up pen.
pen.cost.proj
: annual costs in thousands for project manager.
pen.cost.maint
: annual cost in thousands for patrolling
and repairing fence.
pen.cost.capt
: annual cost in thousands to capture cows, monitor,
survey, calf collar.
pen.cost.pred
: annual cost in thousands for removing predators.
The following demographic parameters can be set by wildlift_settings
:
c.surv.wild
: calf survival rate in the wild, annual.
c.surv.capt
: calf survival rate when captive, annual.
f.surv.wild
: adult female survival when wild, annual.
f.surv.capt
: adult female survival when captive, annual.
f.preg.wild
: pregnancy rate when wild.
f.preg.capt
: pregnancy rate when captive.
wildlift_settings
returns a settings object.
wildlift_forecast
returns a forecast object.
wildlift_breakeven
returns a numeric value representing the
'breakeven' proportion of females penned where lambda
is within tol
erance. It returns NA
when
proportion satisfying the lambda
criterion cannot be found,
alongside a warning.
The print
method returns the input object x
invisibly.
The summary
method returns population and cost summaries
for the forecast object (cost is in million dollars).
The plot
and lines
methods return the plotted data invisibly
(years, pen and no-pen population size for plot
;
years, pen or no-pen population size for lines
).
Both methods produce plots as a side effect.
wildlift_matrix
, wildlift_breeding
## Predefined settings (s1 <- wildlift_settings("mat.pen")) (s2 <- wildlift_settings("pred.excl")) ## Modifying predefined settings wildlift_settings("mat.pen", c.surv.capt=0.65, pen.cap=30) wildlift_settings(s1, c.surv.capt=0.65, pen.cap=30) ## Forecast based on settings for 75% females penned (f1 <- wildlift_forecast(s1, fpen.prop = 0.75)) (f2 <- wildlift_forecast(s2, fpen.prop = 0.75)) ## Get population and cost summaries summary(f1) summary(f2) ## Plot the results plot(f2) lines(f1, col = 2) legend("topleft", col = c(1,1,2), lty = c(2,1,1), legend = c("No pen", "Mat pen", "Pred excl")) ## Find 'breakeven' proportion of females penned where lambda=1 (b1 <- wildlift_breakeven(f1, lambda = 1)) (b2 <- wildlift_breakeven(f2, lambda = 1)) f3 <- wildlift_forecast(s1, fpen.prop = b1) f4 <- wildlift_forecast(s2, fpen.prop = b2) ## See that lines are truly flat op <- par(mfrow = c(1, 2)) plot(f3, main = "Mat pen") plot(f4, main = "Pred excl") par(op)
## Predefined settings (s1 <- wildlift_settings("mat.pen")) (s2 <- wildlift_settings("pred.excl")) ## Modifying predefined settings wildlift_settings("mat.pen", c.surv.capt=0.65, pen.cap=30) wildlift_settings(s1, c.surv.capt=0.65, pen.cap=30) ## Forecast based on settings for 75% females penned (f1 <- wildlift_forecast(s1, fpen.prop = 0.75)) (f2 <- wildlift_forecast(s2, fpen.prop = 0.75)) ## Get population and cost summaries summary(f1) summary(f2) ## Plot the results plot(f2) lines(f1, col = 2) legend("topleft", col = c(1,1,2), lty = c(2,1,1), legend = c("No pen", "Mat pen", "Pred excl")) ## Find 'breakeven' proportion of females penned where lambda=1 (b1 <- wildlift_breakeven(f1, lambda = 1)) (b2 <- wildlift_breakeven(f2, lambda = 1)) f3 <- wildlift_forecast(s1, fpen.prop = b1) f4 <- wildlift_forecast(s2, fpen.prop = b2) ## See that lines are truly flat op <- par(mfrow = c(1, 2)) plot(f3, main = "Mat pen") plot(f4, main = "Pred excl") par(op)