Download e Environmental parameters used to parameterize resistance surfaces.

Appendix 1: Attribute summary of papers reviewed.
a
Study
Species
Adriaensen et
al. 2003
Generic species based
on small forest
mammals
Biological
Data b
Expert
Opinion
Bartelt et al.
2010
Anaxyrus (=Bufo)
boreas)
Expert Opinion;
Detection
Bauer et al.
2010
Ambystomatidae
Expert Opinion
(Expert opinion
process developed in
Compton et al. 2007)
Beazley et al.
2005
Alces alces Americana;
Martes americana
(sep)
Expert Opinion;
Detection
Beier et al.
2009
Puma concolor;
Ococoileus hemionus;
Taxidea taxus; Vulpes
macrotis mutica; Sciurus
griseus; Dipodomys
Expert Opinion
nitratiodes nitratoides;
Perognathus alticola
inexpectatus; Strix
occidentalis occidentalis
(sep)
Resource
Analytical Selection
Process c Function d
One stage
EO
None
Two Stage
EGE
MSF
One stage
EO
None
One Stage
EO
Two Stage
EG E
One stage
EO
Environmental Data e
Parametersf Typeg
Grain
LU/LC
1m
Cat
Extent
i
~4km2
LU/LC; % Canopy;
DEM; Temp;
Vapor
Cat &
Cont
30m
8 km2
LU/LC
Cat
NP
~100 km2
LU/LC; Roads
Cat
2km and
10km
48,800 km2
LU/LC; DEM;
Roads;
Topographic
Position
Cat
100m
6,650 km2
None
PSF
None
h
Analytical Process
Resistance
Value Range j
None
0 - 200
Used Niche Mapper to
develop Evaporative
Water Loss RSurface as
well as HSI RSurface for
each individual.Model
selection was based on
toad movement.
Used resistance values
from Compton et al. 2007
except for one
development class where
authors assigned
resistance values.
Used literature to
develop HSIs. For moose
used presence data in a
logistic regression to
identify HSI that best
predicted presence.
Resistance values were
the inverse of HSI values.
Weighted layers from 0 –
100%
19 – 87 (cost
surface 1); NP
(cost surface2)
0-1
0-1
0-10
Study
Braunisch et al.
2010
Species
a
Biological
Data b
Resource
Analytical Selection
Process c Function d
Tetrao urogallus
Genetic Dist (Ind)
One stage
E
Broquet et al.
2006
Martes americana
Expert
OpinionGenetic
Dist (Ind)
Two Stage
EG E
MSF
Bunn et al.
2000
Mustela vison;
Protonotaria citrea
(comb)
Expert Opinion
One Stage
EO
None
MSF
Chardon et al.
2003
Pararge aegeria L.
Expert Opinion 
Detection
Two Stage
EG E
MSF
Chetkiewicz &
Boyce 2009
Ursus arctos; Puma
concolor (sep)
Detection (Telem)
One Stage
E
PSF
Clark et al. 2008
Crotalus horridus
Expert Opinion
Validated with
Genetic data
(Individ)
One Stage
EO*
None
Compton et al.
2007
Ambystoma opacum
Expert Opinion
One Stage
EO
None
Environmental Data e
Parametersf Typeg
LU/LC; Roads;
Settlements;
Topographic
Exposure
h
Grain
Extent
i
Cat
120m
510 km2
LU/LC
Cat
10m; 25m;
50m; 75m;
100m;
500m
500 km2;
800 km2
LU/LC; Hydro;
Roads
Cat
90m
5,800 km2
LU/LC
Cat
1m
~ 14km2; ~
23 km2
LU/LC; hydro;
slope; DEM; TRI;
NDVI; CTI; Roads
Cat &
Cont
30m
425 km2;
1,657 km2
Slope; Aspect
Cat
10m
~240km2
LU/LC; Roads;
Hydro; Slope
Cat
30m
27,336 km2
Analytical Process
Proportion of
environmental variable
within a 6 pixel strip
between individuals was
correlated to genetic
distances using Mantel
tests. Permeability value
for a pixel was calculated
by summing the
significant regression
coefficients for each
environmental variable.
Expert opinion was used
to develop a priori
resistance surfaces. LCP
lengths between pairs of
individuals were
correlated with genetic
distances using Mantel
tests.
None
Used Expert Opinion to
estimate a range of
resistance values for each
class. Used presence –
absence data and a
logistic regression
framework to determine
which resistance surface
had better predictive
power.
Seasonal RSF. Took
inverse of RSF values to
obtain resistance values
Expert opinion was used
to develop an a priori
resistance surface. LCP
lengths between pairs of
individuals were tested
for correlation with
genetic distances using a
non-parametric
permutation test.
Used trimmed average of
expert opinion to derive
final resistance values.
Resistance
Value Range j
-0.220 – 0.078
(permeability
values)
1 - 50
0.5 - 5
1 -100
1 – 4; 1 – 5; 1 – 6;
1 – 7; 1 - 9
NP
1 - 40
Study
Species
a
Biological
Data b
Resource
Analytical Selection
Process c Function d
Coulon et al.
2004
Capreolus capreolus
Expert Opinion
Validated with
Genetic data
(Individ)
One Stage
EO*
None
Cushman &
Lewis 2010
Ursus americanus
Expert Opinion 
Pathway
Two Stage
EG  E
PathSF
Cushman et al.
2006
Ursus americanus
Expert Opinion 
Genetic Dist (Ind)
Two Stage
EG  E
MSF
Cushman et al.
2009
Ursus americanus
Expert Opinion 
Genetic Dist (Ind)
(from Cushman et
al. 2006)
Two Stage
EG  E
Dedecker et al.
2007
Insecta,
Ephemeroptera;
Insecta, Trichoptera
(sep)
Expert Opinion
One Stage
EO
Environmental Data e
Parametersf Typeg
h
Grain
Extent
i
Analytical Process
Used FNM to quantify
the extent of wooded
habitat (distance to and
size of nearest patch)
around a pixel with an
800m moving window.
The lower the value the
more wooded habitat.
LCP lengths between
pairs of individuals were
correlated with genetic
distances using Mantel
tests.
Expert opinion was used
to develop an a priori
resistance surfaces. Path
Selection Function was
used to identify the most
supported resistance
surface. (Used path
compared with available
paths. Conditional logistic
regression was used to
identify the most
supported resistance
surface.)
Expert opinion was used
to develop a priori
resistance surfaces. LCP
costs between pairs of
individuals were
correlated with genetic
distances using Mantel
tests within a causal
modeling framework.
Resistance
Value Range j
LU/LC
Cat
20m
2,200 km2
Roads; % Canopy;
DEM; Hum Dev
Cat &
Cont
90m
1,500 km2
LU/LC; Roads;
Slope; DEM
Cat &
Cont
90m
3,000 km2
MSF
LU/LC; Roads;
DEM
Cat &
Cont
90m
~ 300,000
km2
Same analytical process
as Cushman et al. 2006
(see above)
1 – 63
None
LU/LC; Hydro;
Culvert; Weir
Cat
NP
116.5 km2
None
1 - 200
0 - 200
NP
1 - 63
Study
Desrochers et
al. 2011
Driezen et al.
2007
Emaresi et al.
2011
Species
a
Seiurus aurocapilla
Biological
Data b
Expert Opinion 
Relocation (homing
rates)
Resource
Analytical Selection
Process c Function d
Two Stage
EG  E
Erinaceus europaeus
Expert Opinion 
Detection along
path
Two Stage
EG  E
Mesotriton alpestris
Genetic Dist (Pop)
One Stage
E
MSF
Environmental Data e
Parametersf Typeg
LU/LC
Cat
h
Grain
25m
Extent
i
4,000 km2
PSF
LU/LC; Roads;
Hydro
Cat
10m
5 study sites
from 11.09
km2 to
20.59 km2
MSF
LU/LC; Roads;
Hydro
Cat
10m
672 km2
Analytical Process
Expert opinion was used
to develop a priori
resistance surfaces.
Homing rates were used
in Cox regression models
to identify most
supported resistance
surface.
Expert opinion and
frequency of telemetry
points in habitat types
were used to develop a
priori resistance surfaces.
Compared used points
along movement path
with available points at
same Euclidean distance
from source cell. The
relative cost at each fix
location was obtained
using a z-score.
Proportion of
environmental variable
within rectangular strips
of different widths
between populations was
used as a representation
of landscape feature
frequencies. These
frequencies were ranked
using AIC and the
regression coefficient and
Mantel tests were used
determine if a landscape
feature limits or favors
movement.
Resistance
Value Range j
1 - 30
1 - 150
NP
a
Biological
Data b
Resource
Analytical Selection
Process c Function d
Study
Species
Epps et al. 2011
Loxodonta africana;
Giraffa camelopardalis;
Tragelaphus
strepsiceros; Aepyceros
melampus; Panthera
leo; Crocuta crocuta;
Proteles cristata;
Orycteropus afer (sep)
Detection
None
NA
Epps et al. 2007
Ovis canadensis
Expert Opinion 
Genetic Dist (Pop)
Two stage
EG  E
MSF
Estrada-Pena
2003
Ixodes ricinus
Expert Opinion
One Stage
EO
None
Fall et al. 2007
Rangifer tarandus
caribou
Expert Opinion;
Detection(telem)
(from O’Brien et al.
2006)
One Stage
E
EO
PSF
None
Ferreras 2001
Lynx pardinus
Detection (telem)
One Stage
E
PSF
Environmental Data e
Parametersf Typeg
h
Grain
Extent
i
Distance Grid
(minimum
distance of each
pixel to nearest
detection
location)
Cont
1km
15,400 km2
Slope
Cat
90m
75,000 km2
DEM
Cont
10m
1,380 km2
LU/LC; Roads
Cat
100m
160,000
km2
LU/LC
Cat
50m
2,500 km2
Analytical Process
Using only species with
detection probabilities of
>0.65, calculated the
resistance value for each
cell as the minimum
distance of each pixel to
the nearest detection
location.
Expert opinion was used
to develop a priori
resistance surfaces with
different slopes
representing cutoffs. Log
transformed LC distances
between population pairs
were correlated with
genetic distances using
Mantel tests.
Assumed linear
relationship between
elevation and resistance
to movement. Applied
time weights to cost
surface with farther rings
denoting increasing
weights. (To emulate
feeding time on host)
Used previously
developed resistance
surface derived from RSF
(O’Brien et al. 2006, see
below). Used Expert
Opinion to apply
resistance values for
linear features.
Analyzed data within
Jacob’s Selection Index to
obtain values ranging
from -1 (maximum
avoidance) to +1
(maximum preference.
Rescaled from 1-10 and
used the inverse of the
preference values for the
resistance value.
Resistance
Value Range j
NP
0.01 - 1
NP
1-5
1 – 10
Study
Species a
Biological
Data b
Resource
Analytical Selection
Process c Function d
Flamm et al.
2005
Trichechus manatus
latirostris
Detection (Telem)
(from Weigle et al.
2001)
One Stage
E
PSF
Flesch et al.
2010
Ovis canadensis
mexicana
Expert Opinion 
Genetic Dist (Pop)
(from Epps et al.
2007)
Two Stage
EG  E
MSF
Foltete et al.
2008
Arvicola terrestris
Expert Opinion 
Detection
Two Stage
EG  E
MSF
Freeman & Bell
2011
Rana sylvatica
Expert Opinion
One Stage
EO
None
Environmental Data e
Parametersf Typeg
Grainh
Extenti
Bathymetry
Cat
25m
~800 km2
Slope
Cat
NP
~19,600
km2
LU/LC
Cont
7m
200 km2
LU/LC; Roads
Cat
5m
~76.7 km2
Analytical Process
Used previously
developed resistance
surface (Weigle et al.
2001) which was based
on deviation from
random distribution
analysis. When observed
percentage exceeded
expected, preference was
suggested and that class
was assigned a positive
sign. Scaled results by
subtracting from largest
quotient.
Used previously
developed resistance
surface from Epps et al.
2007. See above.
Developed a priori
resistance surfaces based
on different topological
functional forms of
distance from
habitat/non-habitat edge.
Used a Pearson
correlation coefficient to
compare least cost path
distances and least cost
path lengths with
distribution of vole
densities.
Several iterative rounds
of input and feedback
from experts.
Resistance
Value Range j
10-65
0.01 – 1.0
1 – 5; 1 – 10; 1 –
20; 1 – 60; 1 – 80;
1 – 100
1 - 100
Study
Species
a
Biological
Data b
Analytical
Process c
Resource
Selection
Function d
Graham 2001
Ramphastos sulfuratus
Relocation
(Telem/HR)
One Stage
E
HSF
Gurrutxaga et
al. 2010
Capreolus capreolus;
Sus scrofa; Cervus
elaphus; Martes
martes; Felis silvestris;
Genetta genetta;
Meles meles; Martes
foina (comb)
Expert Opinion
One Stage
EO
None
Hagerty et al.
2011
Gopherus agassizii
Detection (from
Nussear et al. 2009)
Validated with
Genetic Dist (pop)
Hepcan et al.
2009
Hyaena hyaena; Lynx
lynx; Caracal caracal;
Felis chaus (sep)
Expert Opinion
Hokit et al.
2010
Sceloporus woodi
Expert Opinion
Validated with
Genetic Dist (pop)
One Stage
E*
PSF
One Stage
EO
None
One Stage
EO*
None
Environmental Data e
Parametersf Typeg
h
Grain
Extent
i
Analytical Process
Resistance
Value Range j
LU/LC
Cat
5m
2.36 km2
Compositional analysis
was used on MCPs for
each individual to obtain
time spent in each
habitat relative to
availability. Author
converted results to
costs.
LU/LC; Zoning;
Traffic; Viaducts &
Tunnels
Cat
20m
7224 km2
None
1 - 1000
0-1
1-3
LU/LC; DEM;
Slope; Aspect;
TRI; Smoothness;
Soil Density;
Depth to bedrock;
Rock percentage;
Precip
Cat &
Cont
1 km
~160,000
km2
Generalized Regression
Analysis and Spatial
Prediction (GRASP) was
used to build models and
ROC was used to evaluate
models. Resultant model
had a floating point value
ranging from 0-1 for
suitability. Inverse of
these values were used
for RSurface (1-x). LCP
costs between
populations and
resistance distance
(CircuitScape) between
pairs of individuals were
correlated with genetic
distances using Mantel
tests within a causal
modeling framework.
LU/LC; Roads
Cat
100m
18,905 km2
Weighted LC at 55% and
Road density at 45%
NP
32.2 km2
Expert Opinion was used
to develop an a priori
resistance surface. LC
Distance and a pair-wise
isolation parameter
based on landscape
metrics between pairs of
individuals was
correlated with genetic
distances using Mantel
tests.
NP
LU/LC
Cat
2m
Study
Species
a
Biological
Data b
Analytical
Process c
Resource
Selection
Function d
Huber et al.
2010
Cervus elaphus
nanodes; lynx rufus;
Antilocapra
Americana;
Thamnophis gigas;
Vulpes macroitis
mutica (sep)
Expert Opinion
One Stage
EO
None
Hurme et al.
2007
Pteromys volans
Expert Opinion
One Stage
EO
None
Janin et al.
2009
Bufo bufo
Expert Opinion 
Detection
Two Stage
EG  E
PSF
Environmental Data e
Parametersf Typeg
h
Grain
Extent
i
Analytical Process
Resistance
Value Range j
LU/LC; Roads;
Human Dev;
Natural;
Management
Status
Cat &
Cont
356m (13.3
ha
hexagons)
~108,779
km2
Expert Opinion was used
to quantify the LU/LC
variable for the HSI.
Researcher opinion was
used for the other
variables. LU/LC was
given half the overall
weight of all parameters.
HSI was developed for
each species and scaled
from 0 – 1. Inverse of HSI
values used for resistance
values.
LU/LC
Cat
25m
374.5 km2
None
NP
NP (Study
area 1); 246
km2 (Study
area 2)
Expert opinion was used
to select bounded
parameter space and 5
resistance values for each
of the 4 environmental
variables that were
tested (forest, meadow,
crop, urban). The
combination of
parameters resulted in 54
a priori resistance
surfaces for each
migration distance.
Logistic regression was
used to test models with
occurrence data. Then
parameter space was
refined and process was
repeated.
1 – 6; 1 – 7;1 – 10;
1 – 15; 1 – 28;
LU/LC; Roads;
Hydro
Cat
15m
0-1
Study
Kautz et al.
2006
Species
a
Puma concolor cForyi
Biological
Data b
Detection &
Relocation
(Telem/HR); Expert
Opinion
Analytical
Process c
One Stage
E
One Stage
E
One Stage
EO
Kindall &
VanManen
2007
Ursus americanus
Detection (Telem)
One Stage
E
Klug et al. 2011
Coluber constrictor
flaviventris
Expert Opinion 
Genetic Dist
(Individ)
Two Stage
EGE
Resource
Selection
Function d
Environmental Data e
Parametersf Typeg
h
Grain
Extent
i
PSF
HSF
LU/LC; Roads
Cat
30m
60,256 km2
LU/LC (converted
to landscape
metrics)
Cont
447 m
~ 900 km2;
and ~6,300
km2
LU/LC
Cat
150 m
13,500 km2
None
PSF
MSF
Analytical Process
Compositional analysis
was used to identify
proportions of land cover
types within individual
fixed kernel home ranges
that differed from
proportion of land cover
in the study area. Also
used Euclidean distance
analysis to identify
differences in mean
distances from each land
cover type to both radiotelemetry locations and
random locations in the
study area. Used results
of these 2 analyses to
rank environmental
parameters. Used
resistance values of 1 –
11 for the results from
the compositional
analysis and 1 – 10 for
the results of the
Euclidean distance
analysis. Researcher
opinion was used to
assign resistance values
to water and roads.
Used weights-of-Evidence
to model habitat
preferences. Took inverse
of these values for
resistance values.
Expert opinion was used
to develop a priori
resistance surfaces.
Resistance Distance
(CircuitScape) between
pairs of individuals was
correlated with genetic
distances using Mantel
tests.
Resistance
Value Range j
1 – 20
NP
1 - 100
Study
Species
a
Biological
Data b
Analytical
Process c
Resource
Selection
Function d
Koscinski et al.
2009
Hypsiboas andinus
Expert Opinion 
Genetic Dist (Pop)
Two Stage
EG  E
MSF
Kuemmerle et
al. 2011
Bison bonasus
Detection (telem)
(from Kuemmerle
et al. 2010)
One Stage
E
PSF
Kuroe et al.
2011
Reithrodontomys spp.
Detection
One Stage
E
MSF
Lada et al. 2008
Antechinus flavipes
Expert Opinion 
Genetic Dist (Pop)
Two Stage
EG  E
MSF
Environmental Data e
Parametersf Typeg
h
Grain
Extent
i
LU/LC
Cat
90m
~ 70,000
km2
LC; Slope; Setts;
Roads; Hydro
Cat &
Cont
500m
~ 210,000
km2
LU/LC
Cat
1m
~ 600 km2
LU/LC; Roads;
Hydro
Cat
50m
~2,000 km2
Analytical Process
Expert opinion was used
to develop a priori
resistance surfaces. LCP
Length between pairs of
individuals was
correlated with genetic
distances using Mantel
tests.
Used presence points to
develop a HSI using
MAXENT (from
Kuemmerle et al. 2010).
Took inverse of HSI
values for resistance
values. Scaled from 1 – 10
then assigned barriers to
roads, settlements, and
major water bodies.
Proportion of land use
type that is occupied
within a rectangular strip
between populations was
used in a Bayesian
estimation with a Markov
Chain Monte Carlo
algorithm to estimate
resistance for each land
use type within a
connectivity function.
Varied widths of
rectangular strip from 20
– 100m at 20m intervals.
Expert opinion was used
to develop a priori
resistance surfaces. LC
Cost and LP Length
between pairs of
individuals were
correlated with genetic
distances using Mantel
tests.
Resistance
Value Range j
1 and N/A; 1 - 80
1 – 200
0 – 50
0 - 100
Study
Species
a
Biological
Data b
Analytical
Process c
Resource
Selection
Function d
Environmental Data e
Parametersf Typeg
Laiolo & Tella
2006
Chersophilus duponti
Detection
Validated with
Vocal Dissimilarity
One Stage
E*
PSF
LU/LC; DEM;
Slope; Precip
Larkin et al.
2004
Ursus americanus
floridanus
Expert Opinion
One Stage
EO
None
LU/LC; Human
Dev; Roads
LaRue &
Nielsen 2008
Puma concolor
Lee-Yaw et al.
2009
LePichon et al.
2006
Li et al. 2010
Expert Opinion
One Stage
EO
None
Rana sylvatica
Expert Opinion 
Genetic Dist (Pop)
Two Stage
EG  E
MSF
Barbus barbus
Expert Opinion
One Stage
EO
Ailuropoda
melanoleuca
Expert Opinion
One Stage
EO
None
None
Cat
Cat
h
Grain
Extent
i
Analytical Process
100m
~360,000
km2
Used presence points and
pseudo-absence points
within a Bayesian
estimator to obtain HSI.
Multiplied inverse of HSI
values to obtain the
resistance values. Used
acoustic dissimilarity of
lark calls between
individual larks as a
measure of geographic
distance. LCP Cost and LP
Length between pairs of
individuals were
correlated with acoustic
dissimilarity using Mantel
tests.
30m
23,000 km2
None
Analytical Hierarchy
Process for pair-wise
comparison and
weighting of
environmental variables
for HSI. Values were
averaged across experts.
Used inverse of HSI for
resistance values.
Expert opinion was used
to develop a priori
resistance surfaces.
Resistance Distance
(CircuitScape) between
pairs of individuals was
correlated with genetic
distances using Mantel
tests.
Resistance
Value Range j
0 - 100
1 – 100 and
barrier
LU/LC; Roads;
Hydro; Slope;
Human
Population
Density
Cat
90m
3,182,294
km2
LU/LC; Slope; CTI
Cat &
Cont
2 km
1,600,000
km2
LU/LC; Substrate;
Current Velocity;
Water depth
Cat &
Cont
1m
NP
None
NP
2000 km2
AHP for pair-wise
comparison and
weighting of
environmental variables
for HSI. Used inverse of
HSI for resistance values.
0.002 – 0.095
LU/LC; Slope;
Roads; Hydro
Cat
5m
0.05 – 0.2
NP (conductance
not resistance)
Study
Magle et al.
2009
McRae & Beier
2007
Species
a
Cynomys ludovivianus
Gulo gulo
Biological
Data b
Expert Opinion
One stage
EO
Expert Opinion
One Stage
EO
Michels et al.
2001
Daphnia ambigua
Expert Opinion and
Relocation
(dispersal rates) 
Genetic Dist (pop)
Murphy et al.
2010
Rana luteiventris
Expert Opinion 
Genetic Dist (Pop)
Murtskhvaladze
et al. 2010
Nichol et al.
2010
Nikolakaki 2004
O’Brien et al.
2006
Ursus arctos
Columbus livia; Passer
montanus; Milvus
lineatus; Orthotomos
sutorius; P. major;
Phylloscopus inornatus
(comb)
Phoenicurous
phoenicurous
Rangifer tarandus
caribou
Analytical
Process c
Two Stage
EE
Two Stage
EG  E
Resource
Selection
Function d
None
None
MSF
MSF
MSF
Expert Opinion 
Genetic Dist
(Individ)
Two Stage
EG  E
Expert Opinion
One Stage
EO
None
Expert Opinion
One Stage
EO
None
Detection (Telem)
One Stage
E
MSF
PSF
Environmental Data e
Parametersf Typeg
h
Grain
Extent
Resistance
Value Range j
i
Analytical Process
0.71 – 10.75
NP
LU/LC; Roads;
Hydro
Cat
10m
~ 374 km2
AHP for pair-wise
comparison and
weighting of
environmental variables
based on relative
impermeability of
variables.
Already
developed
Habitat/Nonhabitat map
Cat
5 km; 50
km (tested
both)
~ 1,200,000
km2
None
Anisotropic
surface; flow rate
Cont
0.5m
~ 6km2
LU/LC; DEM; CTI;
Temp
Cat &
Cont
30m
~ 54 km2
Expert opinion was used
to develop 2 a priori
resistance surfaces.
Dispersal rates were used
to create a third
resistance surface. LC
distance between pairs of
populations was
correlated with genetic
distances using Mantel
tests.
Expert opinion was used
to develop a priori
resistance surfaces for
use in gravity models.
Evaluated models with
measures of gene flow (1
– genetic distance).
Expert opinion was used
to develop a priori
resistance surfaces. LC
Distance between pairs of
individuals was
correlated with genetic
distances using Mantel
tests.
NP
NP
Used actual
values for
Elevation, Slope,
and density of
human
settlements. Used
reverse values for
% cover.
DEM; Slope;
Settlement; %
cover
Cat OR
Cont
500m
259,000
km2
LU/LC
Cat
4m; 16m;
20m
(different
scenarios)
160 km2
Calculated fraction of
each pixel covered by
trees and used that as
environmental variable.
5 – 50; 5 - 100
LU/LC; Hydro;
Roads
Cat
20m
101,227
km2
None
1 - 25
RSF used to compute
probability of occurrence.
Inverse of RSF values
used for resistance values
1 – 3.5952
LU/LC
Cat
50m
90 km
2
a
Biological
Data b
Study
Species
Patrick & Gibbs
2010
Chelydra serpentine;
Chrysemys picta picta
(comb)
Expert Opinion
Emys orbicularis
Expert Opinion
generic forest
dependent species
Expert Opinion
Pereira et al.
2011
Pinto & Keitt
2009
Pullinger &
Johnson
Purrenhage et
al. 2009
Rangifer tarandus
caribou
Ambystoma aculatum
Expert Opinion OR
Detection (Telem)
Analytical
Process c
One Stage
EO
One Stage
EO
One Stage
EO
One Stage
EO
One Stage
E
Expert Opinion 
Genetic Dist (Pop)
Two Stage
EG  E
Rabinowitz &
Zeller
Panthera onca
Expert Opinion
One Stage
EO
Ray & Burgman
2006
Cervus unicolor
Expert Opinion
One Stage
EO
Resource
Selection
Function d
None
None
None
Environmental Data e
Parametersf Typeg
h
Grain
Extent
i
Analytical Process
Resistance
Value Range j
LU/LC; Hydro
Cat
30m
~ 7 km2
None
0 - 50
LU/LC
Cat
NP
~ 170 km2
None
1 - 100
LU/LC
Cat &
Cont
500m
111 km2
None
0.057 - 1
For Expert Opinion, used
AHP for pair-wise
comparison and
weighting of
environmental variables
based on relative
impermeability of
variables. For Presence
data was analyzed within
an RSF (conditional fixed
effects logistic
regression). Linearly
transformed RSF to a 0-1
scale. Inverse of this scale
was used to generate
resistance values which
was then reclassified to 1
– 10.
Expert opinion was used
to develop a priori
resistance surfaces. LC
Distance between pairs of
individuals was
correlated with genetic
distances using Mantel
tests.
Cat &
Cont
25m
5,100 km2
PSF
LU/LC; Hydro;
Roads; Slope;
Aspect; DEM;
Predation risk
MSF
LU/LC; Roads;
Traffic
Cat
NP
2,100 km2
LC; DEM; % Cov;
Roads; Setts;
Human Pop
Density
Cat
1 km
1,900,000
km2
Averaged values across
experts
1 - 10
LU/LC; Solar and
Topographic exp
Cat &
Cont
100m
~240,000
km2
Created HSI. Inverse of
HIS values were used for
resistance values.
1 –200 (1 – 100
and 200)
None
None
None
1 - 10
NP
Study
Species
a
Richard &
Armstrong
2010
Petroica longipes
RichardsZawacki 2009
Atelopus varius
Biological
Data b
Expert Opinion 
Pathway
(Telem/Steps)
Detection OR Expert
Opinion  Genetic
Dist (pop)
Analytical
Process c
Resource
Selection
Function d
Two Stage
EG  E
SSF
One Stage
E*
EO*
PSF
None
Environmental Data e
Parametersf Typeg
h
Grain
Extent
i
LU/LC
Cat
15m
150 km2
LU/LC; Slope;
Climate
Cat &
Cont
90m; 1km
~4,800 km2
Analytical Process
Expert opinion was used
to develop a priori
resistance surfaces. Step
Selection Function was
used where used steps
were compared with
available. Cost distances
of all steps were
standardized by dividing
the step value by the
Euclidean distance
between the start and
end of the step (mean
resistance per meter).
Conditional logit models
were used to compare
used vs available.
Developed 3 a priori
resistance surfaces. One
was based on a MAXENT
analysis that resulted in a
climate suitability
gradient. Suitability
values were transformed
so the most suitable
variables had a resistance
of 1. The other two were
based on expert opinion
and literature. LCP Length
between pairs of
individuals was
correlated with genetic
distances using Mantel
tests.
Resistance
Value Range j
1 - 100
1 - 100
a
Study
Species
Ricketts 2001
Cercyonis sthenele;
Coenonympha tullia;
Oeneis chryxus; Erebia
epipsodea; Phyciodes
campestris; Chlosyne
palla; Speyeria
atlantis; Speyeria
mormonia; Pieris
callidice; Pieris
protodice; Euchloe
ausonia; Plebejus
acmon; Glaucopsyche
lygdamus; Everes
amyntula; Plebejus
saepiolus; Plebejus
glandon; Plebejus
Melissa; Lycaena
rubidus; Lycaena
helloides; Lycaena
nivalis; Lycaena
heteronea (grouped
species into taxa and
used 6 taxa
separately)
RodriguezFreire &
CrecenteMaseda 2008
Canis lupus
Rouget et al.
2006
Loxodonta africana
Savage et al.
2010
Ambystoma
macrodactylum
sigillatum
Biological
Data b
Relocation (markrelease recapture)
Analytical
Process c
One Stage
E
Resource
Selection
Function d
MSF
Expert Opinion
One Stage
EO
None
Expert Opinion
One Stage
EO
None
Expert Opinion 
Genetic Dist (Pop)
Two Stage
EG  E
MSF
Environmental Data e
Parametersf Typeg
LU/LC
Cat
h
Grain
1m
Extent
i
Analytical Process
Resistance
Value Range j
~.48 km2
Modeled frequency of
movement as an inverse
function of resistance
distance for various land
cover types and
combinations to obtain
resistance values.
1 – 12.6
1 - 255
LU/LC; Roads;
Setts; Human Pop
Dens; % Cov;
Visibility
Cat &
Cont
25m
30,000 km2
Assigned suitability
values for each
environmental
parameter. Scaled all to
same value range.
Combined with weights
derived from AHP
exercise. Subtracted
suitability values from
256 to obtain resistance
values.
LU/LC; Protected
Areas; Predicted
development;
Cat &
Cont
25m
aggregated
to 1000m
105,454
km2
None
0 – 5000; 1 –
12,000 (2 diff
surfaces)
~ 1,600 km2
Expert opinion was used
to develop a priori
resistance surfaces. LCC
COST (constrained to
lowest 2% of values) and
LCP Cost between pairs of
individuals was
correlated with genetic
distances using Mantel
tests.
NP
LU/LC; Hydro;
DEM
Cat &
Cont
30m
Study
Species
a
Biological
Data b
Analytical
Process c
Resource
Selection
Function d
Schadt et al.
2002
Lynx lynx
Expert Opinion
One Stage
EO
None
Schooley &
Branch 2009
Neofiber alleni
Expert Opinion 
Detection
Two Stage
EG  E
MSF
Schooley &
Wiens 2005
Chelinidea vittiger
Expert Opinion 
Detection
Two Stage
EG  E
MSF
Schwartz et al.
2009
Gulo gulo
Expert Opinion 
Genetic Dist
(Individ)
Two Stage
EG  E
Shanahan et al.
2011
Sericornis
citreogularis;
Sericornis frontalis
(sep)
Expert Opinion 
Genetic Dist
(Individ)
Two Stage
EG  E
Shen et al. 2008
Ailuropoda
melanoleuca
Expert Opinion
One Stage
EO
Environmental Data e
Parametersf Typeg
h
Grain
Extent
i
LU/LC
Cat
1 km
374,000
km2
LU/LC
Cat
30m
195 km2
Vegetation height
Cat
NP
500 m2
plots (2)
MSF
Persistent spring
snow cover
Cat
500m
~275,000
km2
MSF
LU/LC; DEM;
Human Dev
Cat
10m
~ 42 km2
LU/LC; DEM;
Slope; Aspect;
Human Dev
Cat
30m
34,623 km2
None
Analytical Process
Proportion of telemetry
fixes in each habitat type
helped inform researcher
decisions for selection of
resistance values.
Expert opinion was used
to develop a priori
resistance surfaces.
Developed incidence
function models and
determined which model
best explained patch
occupancy by using
logistic regression and
AIC weights.
Expert opinion was used
to develop a priori
resistance surfaces.
Developed incidence
function models and
determined which model
best explained patch
abundance by using
regression modeling and
AIC weights.
Expert opinion was used
to develop a priori
resistance surfaces. LC
Distance between pairs of
individuals was
correlated with genetic
distances using Mantel
tests.
Expert opinion was used
to develop a priori
resistance surfaces. LC
Distance between pairs of
individuals was
correlated with genetic
distances using Mantel
tests.
AHP used for weighting
environmental variables.
Multiplied resistance
values by weights to
arrive at resistance
values.
Resistance
Value Range j
1 – 1000 (1, 20,
and 1000)
1 - 100
0.95 – 2.68
1 – 5; 1 – 10; 1 –
15; 1 – 20; 1 – 50;
and 1 - 100
1-8
1 - 50
Study
Shirk et al. 2010
Short Bull et al.
2011
Species
a
Oreamnos americanus
Ursus americanus
Biological
Data b
Expert Opinion 
Genetic Dist
(Individ)
Expert Opinion 
Genetic Dist (Ind)
Analytical
Process c
One Stage
E
Two Stage
EG  E
Resource
Selection
Function d
MSF
MSF
Environmental Data e
Parametersf Typeg
LU/LC; DEM;
Roads
LU/LC; DEM;
Roads
Cat &
Cont
Cat &
Cont
h
Grain
Extent
i
30m
aggregated
to 450m
36,500 km2
90m
11 study
sites: 842;
3662; 4696;
2761; 3616;
1605; 1287;
1049; 2168;
3864; 6574;
3000 km2
Analytical Process
Used expert opinion
consensus on resistance
values as a base line.
Resistance values of each
environmental variable
was evaluated
univariately by
systematically increasing
or decreasing resistance
values and testing for
correlation of Resistance
Distance (CircuitScape)
with genetic distance
using Mantel tests.
Summed results of
resistance values for each
parameter from the
univariate analysis to
create multivariate
resistance surface. Held 3
parameters constant and
systematically increased
and decreased values of
test parameter until a
peak of support was
reached. If resistance
values changed in
multivariate analysis, reran multivariate analysis
with new values. Causal
modeling was used to
compete models.
Expert opinion was used
to develop a priori
resistance surfaces. LCP
lengths between pairs of
individuals were
correlated with genetic
distances using Mantel
tests.
Resistance
Value Range j
1 – 80 and
100,000 for
barriers
1-10
Study
Species
a
Biological
Data b
Stevens et al.
2006
Bufo calamita
Relocation (from
Stevens et al. 2004,
2006b)  Genetic
Dist (pop)
Sutcliffe et al.
2003
H. virgaureae; A.
hyperantus (sep)
Expert Opinion 
Relocation
(Capture-MarkRecapture)
Analytical
Process c
Two Stage
EE
Two Stage
EG  E
Resource
Selection
Function d
MSF
MSF
MSF
Environmental Data e
Parametersf Typeg
h
Grain
Extent
LU/LC
Cat
3m
~ 98 km2
LU/LC
Cat
5m
2.4 km2
i
Analytical Process
Created one resistance
surface using effective
movement speed through
surrogates of various
environmental variables
(Stevens et al. 2004).
Created a second
resistance surface using a
measure of preference
for environmental
parameters (mean
percentage of individuals
entering an environment
from a different one)
(Stevens et al.2006b). For
the preference surface
subtracted preference
from 100 to obtain
resistance values. LCP
Length and LCP Cost
between pairs of
individuals were
correlated with genetic
distances using Mantel
tests.
Expert opinion was used
to develop a priori
resistance surfaces. Using
Capture-Mark-Recapture
data, exchange ratios
between habitat patches
were calculated. LCP Cost
between habitat patches
was correlated with
exchange ratios between
populations using Mantel
tests.
Resistance
Value Range j
1 – 100 and
10,000 as barrier
1 - 100
Study
Species
a
Biological
Data b
Analytical
Process c
Resource
Selection
Function d
Thatcher et al.
2009
Puma concolor coryi
Relocation
(Telem/HR)
One Stage
E
Verbeylen et al.
2003
Sciurus vulgaris L.
1758
Expert Opinion 
Detection
Two Stage
EG  E
MSF
Vignieri 2005
Zapus trinotatus
Expert Opinion 
Genetic Dist (pop)
Two Stage
EG  E
MSF
Vos et al. 2001
Rana arvalis
Genetic Dist (pop)
One Stage
E
MSF
HSF
Environmental Data e
Parametersf Typeg
h
Grain
Extent
i
LU/LC; Roads;
Human Pop Dens;
Hydro
Cont
(proporti
ons)
30m
78,427 km2
LU/LC; Human
Dev; Roads;
Canals
Cat
2m
163 km2
DEM; Hydro
Cont &
Cat
10m
945 km2
LU/LC; Roads;
Traffic
Cat
NP
~ 36km2
Analytical Process
Percentage of each
environmental variable in
the area was calculated
with a moving window
(3,280 m radius which is
equivalent to the mean
daily movement rate).
Fixed kernel home ranges
were used to calculate
the Mahalanobis
distance. Small values of
Mahalanobis distance
represent landscape
conditions similar to
those in HRs, large values
represent different
conditions. Assumed
higher Mahalanobis
distances would increase
resistance to movement.
Rescaled distances from 0
-1.
Expert opinion was used
to develop a priori
resistance surfaces.
Presence surveys were
categorized into 5
classes. Logistic
regression and AIC values
were used to identify
models that most
predicted presence state.
Expert opinion was used
to develop a priori
resistance surfaces. LCP
Distance between pairs of
individuals was
correlated with genetic
distances using Mantel
tests.
Proportion of
environmental variable
within a 200m wide strip
between populations
was correlated to genetic
distances using Mantel
tests.
Resistance
Value Range j
0-1
1 – 1000 and
10,000 as barrier
NP
0 - 142
Study
Walker et al.
2007
Wang et al.
2008
Wang et al.
2009
Species
a
Lagidium viscacia
Biological
Data b
Expert Opinion 
Genetic Dist (pop)
Analytical
Process c
Two Stage
EG  E
Resource
Selection
Function d
MSF
Niviventer coninga
Detection 
Genetic Dist
(individ)
Two Stage
EE
PSF
MSF
Ambystoma
californiense
Expert Opinion 
Genetic Dist (pop)
One Stage
E
MSF
Environmental Data e
Parametersf Typeg
h
Grain
332m
Extent
12,000 km2
Relief; Hydro
Cat
LU/LC; NDVI;
Aspect; Slope;
Hum Dev; Roads;
Hydro
Cont
(converted
categorical
variables to
continuous 10m
by
calculating
‘distance
to’)
100 km2
LU/LC
Cat
10 km2
1m
i
Analytical Process
Used presence-absence
data to inform resistance
values for the relief
variable. Expert opinion
was used to develop
resistance values for the
hydro variable. LC
Distance between pairs of
individuals was
correlated with genetic
distances using Mantel
tests.
Produced HSI from
presence points using
ENFA. Created a priori
resistance surfaces by
altering representation of
HSI variables. LCP
Distance between pairs of
individuals was
correlated with genetic
distances using Mantel
tests.
Expert opinion was used
to develop a priori
resistance surfaces for
use in gravity models.LCP
Cost between population
pairs were compared to
measures of gene flow.
Assumed rate of gene
flow is inversely
proportional to cost. If a
LCP Cost fell within the
95% confidence interval
of the gene flow estimate
then that resistance
surface was considered
biologically accurate.
Resistance
Value Range j
1 - 1000
0 - 1000
1 - 10
Study
Species
a
Biological
Data b
Analytical
Process c
Resource
Selection
Function d
Environmental Data e
Parametersf Typeg
h
Grain
Extent
i
Analytical Process
Wasserman et
al. 2010
Martes americana
Expert Opinion 
Genetic Dist
(individ)
Two Stage
EG  E
MSF
DEM; Roads; Seral
stage; % Canopy
Cat &
Cont
30m
~5,400 km2
Used expert opinion to
develop a priori
resistance values for each
environmental variable.
Resistance values of each
environmental variable
were evaluated
univariately using LCP
Costs and Mantel tests.
Univariate results were
combined into multiple
multivariate resistance
surfaces. These
resistance surfaces were
evaluated for correlation
with genetic distances
using LCP Costs and
Mantel tests in a Causal
modeling framework.
Watts et al.
2010
Generic focal species
(sep)
Expert Opinion
One Stage
EO
None
LU/LC; Roads;
Buildings
Cat
NP
60 km2
None
Wikramanayake
et al. 2004
Panthera tigris
Expert Opinion
One Stage
EO
None
LU/LC; DEM
Cat
30m
~75,000
km2
Zalewski et al.
2009
Neovison vison
Expert Opinion 
Genetic Dist (pop)
Two Stage
EG  E
MSF
Zetterberg et
al. 2010
Bufo bufo
Expert Opinion
One Stage
EO
None
Zimmermann &
Breitenmoser
2007
a
Lynx lynx
Expert Opinion
One Stage
EO
None
Larger habitat patches
were given lower
resistance values.
Expert opinion was used
to develop a priori
resistance surfaces. LC
Distance between pairs of
individuals was
correlated with genetic
distances using Mantel
tests.
Resistance
Value Range j
1 - 10
1 - 50
1 - 25
DEM; Hydro
Cat
5km
500 km2
(Study area
1); 750 km2
(Study area
2)
LU/LC
Cat
30m
~900 km2
None
1 - 50
14,000 km2
Presence points from
dispersing individuals
from previous study
(Zimmermann 2004) was
used to guide expert
assignment of resistance
values.
1 - 1000
LU/LC
Cat
1km
1 - 100
Target species. Parenthetical notations refer to if the resistance values for multiple species were combined into a single surface (comb) or modeled separately (sep).
Biological input data used to parameterize resistance surfaces includes expert opinion, detection, movement, or genetic data.
c Analytical process used: One Stage refers to processes that develop a resistance surface in a single analytical step, Two Stage refers to processes that develop multiple resistance surfaces in the first step for
testing with empirical data in the second step. Analytical Process types: EO, One-stage expert; EG  E, Two-stage expert-guided empirical; E  One-stage empirical; EE, Two-stage empirical.
d
Resource Selection Function used: PSF, Point Selection Function, MSF, Matrix Selection Function, SSF, Step Selection Function, PathSF, Path Selection Function.
e Environmental parameters used to parameterize resistance surfaces.
f Environmental data type. Untransformed base layers are listed. LU/LC, land use/land cover; Hydro, hydrologic features including rivers and wetlands; DEM, elevation; TRI, topographic ruggedness index; NDVI,
greenness index; CTI, compound topographic index; Roads, roads and other linear features like railways and ditches; % canopy, Percent canopy cover; Vapor, Water vapor density; Temp, temperature; Hum
b
Dev, Buildings and other human development features; Traffic, Traffic data such as volume or frequency; Precip, Precipitation; Solar and topographic exp, solar and topographic exposure; Climate, climactic
variables combined into one parameter; Human pop dens, Human population density.
g Representation of environmental parameters. Cat, Categorical; Cont, Continuous.
h Resolution of environmental parameters used. Multiple grain sizes indicate more than one resolution was used.
I
Extent of study area. Multiple study area sizes indicate more than one study area was used.
j Range of resistance values provided. Multiple ranges indicate, a) more than one parameterization was performed for a single target species, b) more than one model was considered appropriate, or c) values
are for different target species in same study area.
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