An Open-source Tool to Guide Decisions for Wildlife Conservation
WildLift can be used to quantitatively compare the costs and demographic benefits of recovery actions for an iconic threatened species, woodland caribou (Rangifer tarandus caribou). While we use caribou as a case study, our approach to developing this management tool is transferable to other threatened taxa.
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.
The tool estimates the trade-offs associated with seven alternative recovery actions:
- Linear Feature Restoration,
- Linear Feature Deactivation,
- Maternal Penning,
- Conservation Breeding,
- Predator Exclosure,
- Wolf Reduction, and
- Moose Reduction.
Multiple recovery actions are compared allowing users to combine demographic augmentation (maternal penning, predator exclosure, and conservation breeding) with predator and primary prey reductions.
Try the Shiny app or
run locally as WildLift::run_app().
Nagy-Reis, M., Dickie, M., Sólymos, P., Gilbert, S., DeMars, C., Serrouya, R., and Boutin, S., 2020. WildLift: An open-source tool to guide decisions for wildlife conservation. Frontiers in Ecology and Evolution, 8:564508. DOI 10.3389/fevo.2020.564508
remotes::install_github("ABbiodiversity/WildLift")See user visible changes in the NEWS file.
library(WildLift)
#> Loading required package: popbio
#> WildLift 0.3.0 2020-09-07
## Predefined settings
(s1 <- wildlift_settings("mat.pen"))
#> Caribou settings - pen type: mat.pen
#>
#> - c.surv.wild :0.163
#> - c.surv.capt :0.598
#> - f.surv.wild :0.853
#> - f.surv.capt :0.903
#> - f.preg.wild :0.92
#> - f.preg.capt :0.92
#> - pen.cap :35
#> - pen.cost.setup:500
#> - pen.cost.proj :80
#> - pen.cost.maint:300
#> - pen.cost.capt :250
#> - pen.cost.pred :0
(s2 <- wildlift_settings("pred.excl"))
#> Caribou settings - pen type: pred.excl
#>
#> - c.surv.wild :0.163
#> - c.surv.capt :0.72
#> - f.surv.wild :0.853
#> - f.surv.capt :0.95
#> - f.preg.wild :0.92
#> - f.preg.capt :0.92
#> - pen.cap :35
#> - pen.cost.setup:1868
#> - pen.cost.proj :80
#> - pen.cost.maint:600
#> - pen.cost.capt :200
#> - pen.cost.pred :80
## Modifying predefined settings
wildlift_settings("mat.pen", c.surv.capt=0.65, pen.cap=30)
#> Caribou settings - pen type: mat.pen
#>
#> - c.surv.wild :0.163
#> - c.surv.capt :0.65
#> - f.surv.wild :0.853
#> - f.surv.capt :0.903
#> - f.preg.wild :0.92
#> - f.preg.capt :0.92
#> - pen.cap :30
#> - pen.cost.setup:500
#> - pen.cost.proj :80
#> - pen.cost.maint:300
#> - pen.cost.capt :250
#> - pen.cost.pred :0
## Forecast based on settings for 75% females penned
f1 <- wildlift_forecast(s1, fpen.prop = 0.75)
f2 <- wildlift_forecast(s2, fpen.prop = 0.75)
## Most important results summarized
summary(f1)
#> Caribou forecast - pen type: mat.pen
#>
#> - tmax :20
#> - pop.start:100
#> - fpen.prop:0.75
#>
#> - npens :3
#> - lam.pen :1.03
#> - lam.nopen :0.914
#> - Nend.nopen :17
#> - Nend.pen :195
#> - Nend.diff :178
#> - Cost.total :30.5
#> - Cost.percap:0.171
summary(f2)
#> Caribou forecast - pen type: pred.excl
#>
#> - tmax :20
#> - pop.start:100
#> - fpen.prop:0.75
#>
#> - npens :7
#> - lam.pen :1.09
#> - lam.nopen :0.914
#> - Nend.nopen :17
#> - Nend.pen :556
#> - Nend.diff :539
#> - Cost.total :87
#> - Cost.percap:0.161
## 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))
#> [1] 0.5171369
(b2 <- wildlift_breakeven(f2, lambda = 1))
#> [1] 0.3441178
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)
## Forecast using number of penned females
## - it can be an initial number
## - or a vector of individuals for subsequent years
wildlift_forecast(s1, fpen.inds = 5)
#> Caribou forecast - pen type: mat.pen
#>
#> - tmax :20
#> - pop.start:100
#> - fpen.inds:5
wildlift_forecast(s2, fpen.inds = c(5, 0, 4, 6))
#> Caribou forecast - pen type: pred.excl
#>
#> - tmax :20
#> - pop.start:100
#> - fpen.inds:5 0 4 6To report bugs/issues/feature requests, please file an issue.
If you would like to contribute to the package, please see our CONTRIBUTING guidelines.
Please note that this project is released with a Contributor Code of Conduct. By participating in this project you agree to abide by its terms.
Copyright 2018 Province of British Columbia
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and limitations under the License.
This project was created using the bcgovr package.
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