ll
OPEN ACCESSProtocolSystematic prioritization protocol applied to
wolverine habitat connectivityKathleen Anne
Carroll
kcarroll7@wisc.edu
Highlights
Prioritization for
future wolverine
habitat connectivity
conservation
Uses integer linear
programming (ILP)
Details all steps from
data acquisition to
implementationI describe a prioritization protocol for future wolverine habitat connectivity conservation using
integer linear programming. Conservation prioritization has broad applications across scales,
systems, and species. However, the process of preparing, generating, and analyzing the
necessary data can be complex. Thus, this protocol details the process from data acquisition to
implementation.Carroll, STAR Protocols 2,
100882
December 17, 2021 ª 2021
The Author(s).
https://doi.org/10.1016/
j.xpro.2021.100882
ll
OPEN ACCESSProtocolSystematic prioritization protocol applied to wolverine
habitat connectivity
Kathleen Anne Carroll1,2,3,4,*1Montana State University, Ecology Department, Montana State University, P.O. Box 173460, Bozeman, MT 59717, USA
2Present address: University of Wisconsin-Madison, Department of Forest andWildlife Ecology, 1630 Linden Dr, Madison,WI
53706, USA
3Technical contact
4Lead contact
*Correspondence: kcarroll7@wisc.edu
https://doi.org/10.1016/j.xpro.2021.100882SUMMARY
I describe a prioritization protocol for future wolverine habitat connectivity con-
servation using integer linear programming. Conservation prioritization has
broad applications across scales, systems, and species. However, the process
of preparing, generating, and analyzing the necessary data can be complex.
Thus, this protocol details the process from data acquisition to implementation.
For complete details on the use and execution of this protocol, please refer to
Carroll et al. (2020) and Carroll et al. (2021).BEFORE YOU BEGIN
The prioritization protocol below describes the specific steps for prioritizing future wolverine con-
nectivity on private land in western Montana. The prioritization methods have broad applications,
including single-species, multi-species, and biodiversity questions (Tack et al., 2019; Scriven
et al., 2020; Delso et al., 2021; Hanson et al., 2021) and are not limited to terrestrial systems (Pinsky
et al., 2020; Magris et al., 2021). This protocol has resulted in an ongoing collaboration between
managers and willing private landowners in Montana, which is unique given historical challenges
in science-management-stakeholder partnerships. There are many necessary steps to generate
the spatial data layer inputs before prioritizing connectivity (Figure 1). While I outline most steps
for data preparation here, there is additional information in both Inman et al. (2012) and Carroll
et al. (2020). It is also important to note that the timing of each step is highly dependent on data
quantity and quality, study system, study organism(s), availability of ecological datasets, and profi-
ciency with the software. I conducted this work as part of a doctoral dissertation, and thus there was a
steep learning curve for many steps; this is reflected in the highly variable timing provided.Steps to obtain wolverine location data
Timing: 8 years (varies by species abundance, life history, and necessary sample size for
questions of interest)
1. Live wildlife captures are typically necessary to place radio-transmitters on individuals for location
data. The steps detailed below were collected for previous work and later applied to the priori-
tization framework (Inman et al., 2012, 2013).
a. Develop a framework for wildlife capture and handling based on species behavior and habitat
use (e.g., for wolverines, seek animals in high elevation public land in the western United
States).STAR Protocols 2, 100882, December 17, 2021 ª 2021 The Author(s). 1
This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
ll
OPEN ACCESS Protocol
Figure 1. The three steps in the ‘‘before you begin’’ section detail how to generate the spatial layers based on
wildlife locations.b. Trap design and animal handling must follow sampling protocols and procedures approved
by the Animal Care and Use Committees associated with the researchers’ institution.
c. These wolverines were captured using box traps during the winters of 2001–2010.
d. All box traps had trap-transmitters (Telonics, TBT-500, Mesa, AZ) to minimize the time be-
tween capture and handling.
2. Fit individuals with transmitters
a. All individual wolverines were sedated for transmitter implants.
b. Each of the 38 individuals (23F, 15M) was fitted with an intra-peritoneal very high frequency
(VHF) radio transmitter (Advanced Telemetry Systems M1245, M1250, M1255, Isanti, MN; Te-
lonics Imp400L, Imp300L).
c. Eighteen (11F, 7M) individuals were also fitted with global positioning system (GPS) collars
(Televilt POSREC 300, Telemetry Solutions, Concord, CA; Lotek Wireless Inc. 3300SL,
Newmarket, Ontario, Canada). These collars weighed approximately 300 g and had a drop-
off mechanism.
d. Individual Attribute Collection2
i. Identification Number: each animal was given a unique identification number.
ii. Age class: individuals were classified as either juvenile, subadult, or adult.

 Age was estimated based on the first detection date, whether or not they had de-
scended testes, amount of toothwear, and cementum annuli (Inman et al., 2012).

 Juveniles were classified as < 1 year old, subadults as 1–2 years old, and adults as R 3
years old.
iii. Sex: individuals were identified as male or female.STAR Protocols 2, 100882, December 17, 2021
ll
Protocol OPEN ACCESS3. Monitoring
a. VHF telemetry points were collected from the air at approximately 10-day intervals (Inman
et al., 2013).
Note: For this dataset, 24 individuals weremonitored for over 1 year, 12 for over 3 years, and 6
for over 5 years (Inman et al., 2012).
b. GPS collars collected locations at 2-h intervals for approximately 3 months.i. Most GPS data were collected during February and March.4. Post-processing
a. Statusi. Data on age class and sex were added to each location point for each individual based on
their ID.
ii. Individuals were also identified as residents with established home ranges or dispersing in-
dividuals (Inman et al., 2012, 2013).
b. 2257 VHF radio telemetry locations were collected for resident individuals across four moun-
tain ranges.
c. 2835 GPS collar locations were collected for resident individuals in the same ranges.
d. 365 additional VHF and GPS locations from 5 individuals were collected from a disparate
mountain range.
e. Data from dispersing individuals (n = 1165 from both VHF and GPS) were kept but not used in
subsequent habitat selection models.
f. Some points were excluded due to post-handling movements or other errors.
g. Data Issues
i. Approximately 50% of GPS collar locations were not received, likely due to habitat features
(Inman et al., 2013).
Resource selection function (RSF) and data preparation for connectivity analysis
Timing: Days to Weeks
5. Collection or acquisition of ecological data
a. Relevant biotic and abiotic spatial data can be collected, acquired from external sources, or
leveraged from remotely sensed images as explanatory variables for RSF models (Inman
et al., 2013; Carroll et al., 2020). In both Carroll et al. (2020) and (Carroll et al., 2021), I used
pre-existing data layers.i. I used 2040 to 2069 RCP 8.5 snow water equivalent (SWE) projections at a 5-km resolution to
generate future connectivity across international borders (US-Canada) as the single explan-
atory variable (Carroll et al., 2021).6. Logistic regression model (RSF Model)
a. I used logistic regression in R Statistical Software 3.6.5, comparing predictors at ‘‘use’’ loca-
tions (n = 5439) to ‘‘available’’ or pseudo-absence points (herein available points; Carroll
et al., 2020).i. Available points were located within a kernel density estimate to ensure that these points
were within available habitat (Inman et al., 2013; Carroll et al., 2020).
Note: I used the ade4 and adehabitatHR packages – see cran.r-project.org for vignettes.ii. Available points were also at least 1 km apart (n = 16,317).
iii. The model was run on 70% of the use/availability data, and the rest was withheld for model
validation.STAR Protocols 2, 100882, December 17, 2021 3
ll
OPEN ACCESS Protocol7. Model validation
a. I used a random test dataset of 30% of the use/availability data for model validation.
b. The model predicted use and availability of withheld data with 78% accuracy (kappa = 32%).
8. Results
a. Habitat quality was extrapolated from the RSFmodel output to produce a single raster layer (1-
km resolution), representing the relative probability that wolverines could maintain a home
range at any given location across the study area (Carroll et al., 2020).REAGE
Softwa
Circuit
R Stati
4
i. The habitat quality was then used to generate resistance layers and core layers (see subse-
quent section).Modeling connectivity using Circuitscape 4.0
Timing: Hours to Weeks
9. Circuitscape 4.0 (McRae et al., 2008; McRae and Shah, 2009)a. Circuitscape is a circuit theorymodel and represents only one approach to connectivitymodeling.
Alternative software can be more or less effective for modeling connectivity for different species,
and different modeling types have different assumptions, limitations, and caveats (McClure et al.,
2016). Special care should be given when selecting software. Additionally, memory limitations
and computation limits exist for Circuitscape; see McRae et al. (2008).10. Data Inputs for Circuitscape 4.0: Circuitscape requires inputs of ‘‘core’’ habitat patches and a
resistance layer representing its permeability to individuals.a. Scaled Resistance
i. Using the equation below, I generated a scaled habitat resistance raster from habitat quality,
where c = 8 and h = habitat quality matrix (Keeley et al., 2017; Carroll et al., 2020). This scaling
represented a negative inverse relationship between habitat quality and resistance.
100--99  ðð1  expð  c  hÞÞ =ð1  expð  cÞÞÞ = Scaled Resistance
Scaled resistance was used as wolverines have lower habitat quality sensitivity during dispersal
than within-home range movements (Carroll et al., 2020). The degree to which the resistance
was scaled was based on telemetry location from dispersing individuals in a previous analysis.
b. Cores, or the sources and destinations for individuals, were selected using a decision
threshold at a sensitivity of 0.95 (correct prediction of use) on withheld data (see Carroll
et al., 2020).11. Modeling Modea. There are several modeling mode options in Circuitscape. I chose to use a pairwise analysis,
connecting eight neighbors with current map outputs. Decisions on mode options should be
based on the information in the Circuitscape User Guide. Troubleshooting 112. Connectivity Output(s)a. Circuitscape can generate different output types, but I selected current map outputs. As
mentioned in Carroll et al. (2020), current maps represent the pathways animals are likely
to use and whether or not alternative pathways exist, resulting in current values used in con-
nectivity analyses.KEY RESOURCES TABLENT or RESOURCE SOURCE IDENTIFIER
re and algorithms
scape 4.0 McRae et al., 2008; https://circuitscape.org/
McRae and Shah, 2009
stical Software R Core Team https://www.r-project.org/
(Continued on next page)
STAR Protocols 2, 100882, December 17, 2021
ll
Protocol OPEN ACCESS
Continued
REAGENT or RESOURCE SOURCE IDENTIFIER
PrioritizR Hanson et al., 2021 https://prioritizr.net/
Gurobi Gurobi Optimization, LLC https://www.gurobi.com/
ArcGIS ESRI https://www.esri.com/
Deposited data
Prioritization Shapefiles Carroll et al., 2021 https://databasin.org/datasets/
1276c3bbc0e940ed836dcabdf46dc872/
Wolverine Telemetry Data Inman et al., 2012 bobinman@mt.gov
Wolverine Occurrence Data Lukacs et al., 2020 https://doi.org/10.1002/jwmg.21856
Wolverine Connectivity Data Carroll et al., 2020 https://doi.org/10.1016/j.gecco.2020.e01125
Wolverine Core Habitat Data Carroll et al., 2020 https://doi.org/10.1016/j.gecco.2020.e01125
Snow Water Equivalent Integrated Scenarios Group https://climate.northwestknowledge.net/
IntegratedScenarios/
Human Modification Theobald, 2013 https://databasin.org/datasets/
110a8b7e238444e2ad95b7c17e889b66/
Cadastral Data Montana Cadastral http://svc.mt.gov/msl/mtcadastralSTEP-BY-STEP METHOD DETAILS
Acquiring input data layers for prioritization
Timing: minutes to weeks
Several input data layers are needed to solve a systematic conservation planning problem success-
fully. This step details how I acquired spatial data layers for my wolverine conservation planning
problem (Figure 2).
1. Study Area
a. I selected areas with the highest connectivity value (20%) from the previous Circuitscape anal-
ysis (Carroll et al., 2021) in westernMontana. This boundary defined areas in which I conducted
all other analyses. For this analysis, having a smaller study area helped limit the prioritization
analysis to the region of highest interest to collaborators and reduced computation time.
2. Ecological Features
a. I included four input features to prioritize in the planning problem: current female presence,
core size, human modification, and habitat core centrality. I selected these variables based
on evidence in the literature and their importance for dispersal and population growth (Carroll
et al., 2021).i. Of the four input features, three were generated using wolverine camera trap data from Lu-
kacs et al. (2020) and a ‘‘core’’ habitat shapefile generated in Carroll et al. (2021).3. Planning Unit Cost
a. Using Montana cadastral data (www.cdastral.mt.gov), I generated a parcel-level estimate of
easement costs. The cadastral data provided a unique tax-assessed property value for each
parcel. Easement values were calculated by taking 50% of property value and adding $5000
(Ferraro, 2003).
4. Protected Areas
a. I downloaded shapefiles of protected areas from the Protected Areas Database of the US
(PAD-US 2.0) at usgs.gov (U.S. Geological Survey (USGS) Gap Analysis Project (GAP), 2018).
Note: There is a newer version (2.1) of the PAD-US dataset available.
b. A spatial layer or column in a spatial layer can be used to identify whether parcels are already
‘‘locked-in.’’ A locked-in parcel can also be referred to as a ‘‘fixed’’ parcel or protected area. I
designated conservation easements, state lands, and federal lands as fixed areas.STAR Protocols 2, 100882, December 17, 2021 5
ll
OPEN ACCESS Protocol
Figure 2. The steps of the prioritization framework needed to generate conservation planning solutions
6
c. It should be noted that the activities permitted on protected lands vary greatly, and research
goals should determine whether to treat a parcel as fixed or not. I treated all land with any de-
gree of protection as fixed because the goal in this study was to find private land where willing
landowners would consider easements.
CRITICAL: Spatial analysis is a growing field in ecology that requires understanding howany given feature was generated. All input feature layers should be thoroughly reviewed
based on their conservation relevance, resolution, and accuracy to ensure the success of
later steps.Integer linear programming with priortizr and a gurobi optimization solver
Timing: minutes to weeks
I recommend prior experience or practice with R Statistical Software for this section. For additional
examples, see prioritizr.net and Gurobi.com.
5. Install Gurobi
a. Gurobi is a commercial computer program and a powerful and fast solver that works well with
priortizr. There are several steps to use this software:i. Obtain a license from the Gurobi website. This license is free with an academic affiliation,
otherwise, it has an associated cost. Save the license number obtained from the webpage.STAR Protocols 2, 100882, December 17, 2021
ll
Protocol OPEN ACCESSii. Download the software. Troubleshooting 2
iii. Activate and verify the license.
Note: R Statistical Software (r-project.org) is required for the following steps; RStudio is highly
recommended as a user-friendly interface (rstudio.com). Both are free software.
6. Load necessary R Statistical Software Packages. Troubleshooting 3
a. Using the library function in R, call the following packages: prioritzr, sp, sf, raster, and rgdal.
7. Import all data layers into R Statistical Software.
a. All of the layers detailed above must be imported into R Statistical software or RStudio (recom-
mended). Shapefiles can be imported using readOGR and rasters using the raster function.
8. Create one or more planning problems using the example code provided at priortizr.net.
a. I created four planning problems.i. Each of the problems I created had a unique relative target. The targets I set were 0.10,
0.15, 0.20, and 0.50. These values (ranging from 0 to 1) tell the planning problem the
maximum level of representation I wanted for each feature within the study area.
ii. I included a minimum set objective in all problems to minimize the cost of my solutions.
iii. I added binary decisions so that each parcel was either prioritized or not (e.g., a binary so-
lution for each problem).
iv. I added the Gurobi solver to ensure the code called the Gurobi software for the problem.
v. I added my fixed or already protected parcels.
Optional: There are many additional options in the priortizr R package suite, including the
addition of boundary penalties to reduce fragmentation and contiguity constraints to bolster
connectivity. There is also an option to rank the irreplaceability of solutions. I chose not to
implement these given my study area’s interspersion of private and public land (Figure 3).
9. Evaluate Prioritization Performance
a. The performance of the solution can be evaluated by printing the solution cost summary as
illustrated on priortizr.net.
b. Solutions should be visually inspected before being exported or mapped using the spplot
function in R.EXPECTED OUTCOMES
The anticipated outcome of a planning problem is a mappable data layer. After solvingmy four plan-
ning problems, I exported all solutions (data layers) out of R using the > writeOGR function with an
ESRI Shapefile driver and brought them into ArcMap for final mapping. After importing files into Arc-
GIS (Figure 3), final products can be shared with managers and used for conservation planning. I ex-
ported each solution as a Layer File (.lyr) to upload them to DataBasin – see key resources table.
Note: ArcMap is not free, but maps can be made in R Statistical Software, QGIS, or other
programs.LIMITATIONS
I designed this specific protocol for single-species future connectivity conservation, so alternative
applications require modifications. Further, Montana has some of the most accurate and freely avail-
able cadastral data in the United States, so other cost-layer options may need to be considered in
different regions.TROUBLESHOOTING
Problem 1
Before you begin step 11: Determining which Circuitscape download to use.STAR Protocols 2, 100882, December 17, 2021 7
ll
OPEN ACCESS Protocol
Figure 3. Example of final prioritization mapPotential solution
Circuitscape is not run in R, but instead can be downloaded as an executable file (.exe), run as an
ArcGIS toolbox, or coded in Python or Julia (https://julialang.org/). For this work, I used the ArcGIS
toolbox version, which uses drop-down functions. The selected functions are detailed in the
protocol.Problem 2
Step 5: Gurobi installation and activation differ by operating systems.Potential solution
See https://prioritizr.net/articles/gurobi_installation.html for additional help.Problem 3
Steop 6: Warning or error messages reading: ‘‘there is no package called ‘package name’ ’’Potential solution
Use >install.packages(‘package name’) before calling package with library(). R provides error mes-
sages for each error, and solutions are often available within the online R community.RESOURCE AVAILABILITY
Lead contact
Further information and requests should be directed to and fulfilled by the lead contact, Dr. Kathleen
Carroll (kcarroll7@wisc.edu).Materials availability
The shapefiles generated in Carroll et al. (2021) have been deposited to Data Basin (https://
databasin.org/datasets/1276c3bbc0e940ed836dcabdf46dc872/).8 STAR Protocols 2, 100882, December 17, 2021
ll
Protocol OPEN ACCESSData and code availability
Restrictions apply to the availability of the wolverine data, which were used under contract for this
study. Requests to access the dataset should be directed to Dr. Bob Inman, bobinman@mt.gov.
The code from this paper is available at https://github.com/KCarrollEcology/
StarProtocols2021_Wolverine. However, this paper does not report original code; see (https://
prioritizr.net/) for R Statistical Software vignette on prioritization.
ACKNOWLEDGMENTS
While there were no funding sources for this protocol, I would like to thank the funding sources for
the original research in Carroll et al. (2021), including the National Fish and Wildlife Foundation, the
Great Northern Landscape Conservation Cooperative, Montana Fish, Wildlife and Parks, and Mon-
tana State University. Supervisors and/or collaborators from Carroll et al. (2021) that supported the
creation of this work include A. Hansen, B. Inman, R. Lawrence, and K. Barnett. Additionally, I would
like to thank P. Billman, J. Carroll, W. Carroll, and G. Carroll for their help, reviews, and support. All
figures were generated using BioRender.com.
AUTHOR CONTRIBUTIONS
Conceptualization, K.A.C.; methodology, K.A.C.; formal analysis, K.A.C.; data curation, K.A.C.;
writing, K.A.C.; visualization, K.A.C.; project administration, K.A.C.
DECLARATION OF INTERESTS
The author declares no competing interests.REFERENCESCarroll, K.A., Hansen, A.J., Inman, R.M., Lawrence,
R.L., and Hoegh, A.B. (2020). Testing landscape
resistance layers and modeling connectivity for
wolverines in the western United States. Glob. Ecol.
Conserv. 23, e01125.
Carroll, K.A., Inman, R.M., Hansen, A.J., Lawrence,
R.L., and Barnett, K. (2021). A framework for
collaborative wolverine connectivity conservation.
iScience 24, 102840.
Delso, Á., Fajardo, J., and Muñoz, J. (2021).
Protected area networks do not represent unseen
biodiversity. Sc. Rep. 11, 1–10.
Ferraro, P.J. (2003). Assigning priority to
environmental policy interventions in a
heterogeneous world. J. Policy Anal. Manage. 22,
27–43.
Hanson, J.O., Schuster, R., Morrell, N., Strimas-
Mackey, M., Watts, M.E., Arcese, P., Bennett, J.,
and Possingham, H.P. (2021). Prioritizr: systematic
conservation prioritization in R. R package version
7.0.1. https://CRAN.R-project.org/
package=prioritizr.
Inman, R.M., Brock, B.L., Inman, K.H., Sartorius,
S.S., Aber, B.C., Giddings, B., Cain, S.L., Orme,
M.L., Fredrick, J.A., Oakleaf, B.J., and Alt, K.L.
(2013). Developing priorities for metapopulation
conservation at the landscape scale: wolverines in
the western United States. Biol. Conserv. 166,
276–286.Inman, R.M., Packila, M.L., Inman, K.H., Mccue,
A.J., White, G.C., Persson, J., Aber, B.C., Orme,
M.L., Alt, K.L., Cain, S.L., and Fredrick, J.A. (2012).
Spatial ecology of wolverines at the southern
periphery of distribution. J. Wildl. Manage. 76,
778–792.
Keeley, A.T., Beier, P., Keeley, B.W., and Fagan,
M.E. (2017). Habitat suitability is a poor proxy for
landscape connectivity during dispersal and
mating movements. Lands. Urban Plan. 161,
90–102.
Lukacs, P.M., EvansMack, D., Inman, R., Gude, J.A.,
Ivan, J.S., Lanka, R.P., Lewis, J.C., Long, R.A.,
Sallabanks, R., Walker, Z., and Courville, S. (2020).
Wolverine occupancy, spatial distribution, and
monitoring design. J. Wildl. Manage. 84, 841–851.
Magris, R.A., Costa, M.D., Ferreira, C.E., Vilar, C.C.,
Joyeux, J.C., Creed, J.C., Copertino, M.S., Horta,
P.A., Sumida, P.Y., Francini-Filho, R.B., and Floeter,
S.R. (2021). A blueprint for securing Brazil’s marine
biodiversity and supporting the achievement of
global conservation goals. Divers. Distrib. 27,
198–215.
McClure, M.L., Hansen, A.J., and Inman, R.M.
(2016). Connecting models to movements: testing
connectivity model predictions against empirical
migration and dispersal data. Landsc. Ecol. 31,
1419–1432.
McRae, B.H., Dickson, B.G., Keitt, T.H., and Shah,
V.B. (2008). Using circuit theory to modelSTAconnectivity in ecology, evolution, and
conservation. Ecology 89, 2712–2724.
McRae, B.H., and Shah, V.B. (2009). Circuitscape
User’s Guide (The University of California).
Pinsky, M.L., Rogers, L.A., Morley, J.W., and
Frölicher, T.L. (2020). Ocean planning for species
on the move provides substantial benefits and
requires few trade-offs. Sci. Adv. 6, eabb8428.
Scriven, S.A., Williams, S.H., Ghani, M.A., Agama,
A.L., Benedick, S., Brodie, J.F., Hamer, K.C.,
McClean, C.J., Reynolds, G., and Hill, J.K. (2020).
Assessing the effectiveness of protected areas for
conserving range-restricted rain forest butterflies in
Sabah, Borneo. Biotropica 52, 380–391.
Tack, J.D., Jakes, A.F., Jones, P.F., Smith, J.T.,
Newton, R.E., Martin, B.H., Hebblewhite, M., and
Naugle, D.E. (2019). Beyond protected areas:
private lands and public policy anchor intact
pathways for multi-species wildlife migration. Biol.
Conserv. 234, 18–27.
Theobald, D.M. (2013). A general model to quantify
ecological integrity for landscape assessments and
US application. Landsc. Ecol. 28, 1859–1874.
U.S. Geological Survey (USGS) Gap Analysis
Project (GAP) (2018). Protected areas Database of
the United States (PAD-US): U.S. Geological Survey
data release. https://doi.org/10.5066/P955KPLE.R Protocols 2, 100882, December 17, 2021 9