Ecological M o de llin g 221 (2010) 2 37 4-2387
C o n te n ts lis ts a v a ila b le a t S c ie n c e D ire c t
ECOLOGICAL
M O D E LLIN G
Ecological Modelling
journal
ELSEVIER
homepage: w w w .elsevier.com/locate/ecolmodel
How humans shape w o lf behavior in Banff and Kootenay National Parks, Canada
Marco Musiani®, Sk. Morshed A n w a rG re g o ry J. M cD erm id‘^, Mark Hebblewhite'^,
Danielle J. Marceau'^ *
“ Faculty o f Environm ental Design, University o f Calgary, Calgary, AB, T2N 1N4 Canada
I" Departm ent o f Geomatics Engineering, University o f Calgary, 2500 University D rive NW, Calgary, AB, T2N 1N4 Canada
D epartm ent o f Geography, University o f Calgary, Calgary, AB, T2N 1N4 Canada
'* Departm ent o f Ecosystem and Conservation Sciences, University o f Montana, Missoula, ZIP code 59812, USA
A R T I C L E
I N F O
A rticle history:
Received 2 June 2009
Received In revised fo rm 17 A p ril 2010
Accepted IS Ju n e 2010
A vailable o nline 23 July 2010
Keywords:
Agent-based m odel
H u m a n -w lld llfe Interaction
W o lf
B anff N ational Park
K ootenay N ational Park
H um an Im pact
Recreation
A B S T R A C T
This paper describes the conceptualization and im plem entation o f an agent-based m odel to investi­
gate ho w varying levels o f human presence could affect elements o f w o lf behavior, in cluding highw ay
crossings; use o f areas in p ro x im ity to roads and trails; size o f home ranges; activities, such as hunting,
patrolling, resting, and feeding pups; and survival o f individuals in Banff and Kootenay National Parks,
Canada. The m odel consists o f a w o lf m odule as the p rim a ry com ponent w ith five packs represented as
cognitive agents, and g rizzly bear, elk, and human modules th a t represent dynam ic components o f the
environm ent. A set o f environm ental data layers was used to develop a fric tio n m odel th a t serves as a base
map representing the landscape over w h ich wolves moved. A decision m odel was b u ilt to simulate the
sequence o f w o lf activities. The m odel was im plem ented in a Java Program ming Language using RePast,
an agent-based m odeling library. Six m onths o f w o lf activities were simulated from A p ril 16 to October
15 (i.e., a season coherent w ith regard to kn ow n w o lf behaviors), and calibrated w ith GPS data from
w o lf radiocollars (n= 15) deployed from 2002 to 2004. Results showed that the simulated trajectories o f
w o lf movements were correlated w ith the observed trajectories (Spearman’s rho 0.566, P< 0.001); other
critical behaviors, such as time spent a t the den and not traveling were also correlated. The sim ulations
revealed th a t w o lf movements and behaviors were noticeably affected by the in te n sity o f human pres­
ence. The packs’ home ranges shrank and wolves crossed highways less frequently w ith increased human
presence. In an extrem e example, a w o lf pack whose home range is traversed by a high-traffic-volum e
highw ay was extirpated due to in a b ility to hu n t successfully under a scenario w herein hum an presence
levels were increased 10-fold. The m odeling prototype developed in this study m ay serve as a tool to
test hypotheses about human effects on wolves and on other mammals, and guide decision-makers in
designing management strategies th a t m in im ize im pacts on wolves and on other species fu nctionally
related to wolves in the ecosystem.
© 2010 Elsevier B.V. A ll rights reserved.
1. Introduction
Managers o f protected areas m ust m eet the challenging m an­
date o f balancing dem and fo r p u b lic o p p o rtu n itie s to use and
en jo y the landscape w ith the ob lig a tio n to pro te ct ecological
In te g rity. As a result, there Is an on-golng need to develop research
tools to explore h u m a n -e n v lro n m e n t relationships and p re d ict the
Im pact o f hum an a ctivities on w ild life species. This Is the case
fo r B anff and Kootenay N a tion al Parks, w h ic h provide h a bita t to
a nu m be r o f charism atic w ild life species Inclu ding w olves (Canis
lupus), g riz z ly (t/rsus arctos) and black (Ursus americanus) bears, elk
{Cervus elaphus), w h ite -ta ile d deer (Odocoiteus virginianus), m ule
C orresponding a uthor. Tel.: +1 403 220 5314; fax: +1 403 284 1980.
E-m ail address: [email protected] (D.J. Marceau).
0304-3800/S - see fro n t m a tte r © 2010 Elsevier B.V. A ll rights reserved.
dol:10.1016/j.ecolm odel.2010.06.019
deer (Odocoileus hemionus), moose (Aloes aloes), bigh orn sheep (Ovis
oanadensis), and m o u n ta in goats (Oreamnos amerioanus). However,
Banff and Kootenay N ational Parks also receive m ore than fou r
m illio n hum an visito rs every year w h o partake In a v a rie ty o f recre­
ational activities Inclu ding skiing, camping, and hiking . Considering
these figures and the Increasing conflicts betw een w ild life and
hum ans In th is rich landscape. Parks Canada (2007) rece ntly stated
the need to understand the degree o f hum an Im pact on w ild life
m ovem ent and h a bita t In th is region.
Large carnivores are k n o w n to be susceptible to hum an Im pacts
due to th e ir lo w density, high tro p ic level, and lo w reproductive
rate (N ielson et al., 2004), and to the loss and fragm e ntatio n o f th e ir
habitats, hum an -ind uce d m o rta lity , and loss o f prey base (C arroll
et al., 2001; Larsen and Ripple, 2006). W olves, In pa rticular, can be
g re atly affected by the c um u la tive effects o f hum an a ctivities (F rltts
et ah, 2003), as has been dem onstrated by the h isto ry o f hum ans and
M. Musiani et al. / Ecological M o de llin g 221 (2010) 2 37 4-2387
w olves in N o rth Am erica, in large areas o f the Canadian Rockies, as
in m any o th e r parts o f N o rth Am erica, w o lf populations were sys­
te m a tica lly reduced b y pre da to r c o n tro l efforts fro m a p pro xim ately
1850 to 1930 (M usiani and Paquet, 2004). W olves were eve ntu­
a lly extirp a te d fro m Banff and Kootenay N ational Parks in the late
1940s; however, th e y la te r recoionized the m o u n ta in Parks w here
upon the p o p u la tio n gre w and extended south to M ontana, Idaho,
and W ashing to n in the USA (Paquet et ai., 1996).
Nowadays, w olves are s till im pacted by hum an activities.
Previous studies have dem onstrated th a t w olves avoid m anmade landscape features (e.g., roads) w here hum an use is high,
and in general w o lf m ovem ents are influenced b y hum an use
(W h ittin g to n et ai., 2004; Mech, 1995). However, w olves also use
features like tra ils and roads w here hum an a c tiv ity is lo w e r (Paquet
et ai., 1996; Callaghan, 2002). W olves show a strong a ffin ity to io w eievation v a lle y b o tto m s in the B anff and Kootenay N ational Parks
region due to the effects o f physiography, weather, prey abundance,
and prey d is trib u tio n (Paquet et ai., 1996). These valleys are also
used by humans fo r convenient transp ortatio n, high er p ro b a b ility
o f observing w ild life , and scenic beauty and thus the p o te n tia l fo r
hum an influence on w o lf behavior and survival is high.
A nu m be r o f approaches have been used to stud y the im p o r­
ta n t issue o f hum an im pacts on w olves using m u ltip le regression
m odels (M ia d e n o ff et ai., 1997; Purves and Doering, 1999; Larsen
and Ripple, 2006; Boyd-Heger, 1997), d iscrim in a n t fu n c tio n analy­
sis (Corsi et ai., 1999), hidd en M arkov m odels (Franke et ai., 2006),
spatial behavioral models (H aight et ai., 1998), stochastic d iffe re n ­
tia l equations (Boyce, 1995) and a fric tio n or m od ifie d “ least-cost
p a th” m odel (Paquet et ai., 1996). These established techniques to
m odel h u m a n -w o if in te ractio ns use a snapshot approach to anal­
ysis th a t is lim ite d by coarse tem po ral (seasonal va ria tio n ) and
spatial (landscape level) resolutions, o r are designed at the pop­
u la tio n level. Thus, th e y cannot p re d ict the behavior o f social u n its
such as w o lf packs. This is critical, since w o lf packs have in h e r­
ent behavioral v a ria b ility (Mech, 1970) and m ay react d iffe re n tly
to hu m an -ind uce d changes depending b o th on the tim in g o f the
in te ra ctio n (Boitani, 1982) and its location (M ia d e n o ff et ai., 1995).
For example, there is considerable v a ria b ility in distances covered
by w o lf packs due to the differences in a ctivities pe rfo rm ed on a
p a rticu la r tim e fram e (e.g., a day) such as hunting, p a tro llin g o f te r ­
rito ry , and m oving tow a rds the den (Mech, 1970; Van Baiienberghe
et ai., 1975; F rltts et ai., 1981; Jgdrzejew ski et ai., 2001). Such va ria ­
tio n o f pack behavior is n o t taken in to account in aggregated models
or fo rm e r m odels o f hum an im pacts.
From a m ethodological p o in t o f view , in a d d itio n to the need
to m odel in d ivid u a ls belonging to populations, there is a parallel
need to understand and m odel species w ith in ecological com m u­
nities com prised o f oth er in te ra ctin g species. Strong d ire ct im pacts
o f hum an presence on single species have been studied, and
such im pacts are fre q u e n tly m o n ito re d fo r conservation purposes,
although hum an im pacts are often h a bita t-m e dia ted (E hrlich and
Ehrlich, 1981). However, any species’ response, an d/or resilience
to responses, also depends upon interactions w ith o th e r species in
the ecosystem (M acA rthur, 1955).
in th is study, a spa tially and te m p o ra lly exp licit, in d iv id u a lbased m od eling approach (G rim m and Railsback, 2005), hereafter
called an agent-based m odel (ABM ) (Bousquet and Le Page, 2004;
Marceau, 2008) was used to investigate w o lf-h u m a n interactions
in B anff and Kootenay N ational Parks, in w e ste rn Canada. ABMs
are p a rtic u la rly suitable fo r fo rm u la tin g the processes o f a sys­
te m by ta kin g in to account the te m p o ra l and spatial dynam ics
at the in d iv id u a l level. They have been applied to stud y preda­
tio n and co m p e titio n dynam ics (Bousquet et ai., 1994), disease
spread (B arrett et ai., 2005), and p o p u la tio n dynam ics (Parry et ai.,
2004) and to investigate the effects o f changing landscape struc­
ture, hum an a ctivities o r m anagem ent practices on anim al species
2375
(Ahearn et al., 2001; D u m on t and H ill, 2001; Cramer and Portier,
2001; Topping et ai., 2003; A n et al., 2005; Bennett andTang, 2006;
A n w a r et ai., 2007; Bennett et ai., 2009).
This paper describes in de tail an ABM th a t has been designed
and im p lem e nted as a research and e xp lo ra tio n to o l to d yn am ically
sim ulate the behavior o f w o lf packs in the Canadian Rockies. Our
ABM considers hum an im pacts on w o lf packs and the in te ra ctio n
o f w olves w ith g riz z ly bears (used as an exam ple o f a po ten­
tia lly com peting predator), w ith elk (i.e., the m ost im p o rta n t prey
species), and w ith o th er h a bita t factors o f k n o w n im portance to the
species. The m odel was used spe cifically to investigate the im p act
o f increasing levels o f hum an presence on w o lf m ovem ent patterns
in B anff and Kootenay N ational Parks, it should be seen as a p ro to ­
type th a t focuses on answ ering questions regarding ho w humans
affect w o lf ha bita t selection around roads and trails, create dis­
turbances to crucial w o lf activities th a t could lead to shrinkage o f
home ranges, and im pact in d iv id u a l w o lf m o rta lity and survival.
This m odel m ay serve as a useful to o l to test hypotheses about
h u m a n -w o if interactions, investigate fu tu re scenarios, and guide
decision-m akers in designing conservation m anagem ent strate­
gies.
2. Methodology
in th is section, a d e scrip tion o f the study area and the datasets
used to calibrate the agent-based m odel is provided, it is fo llo w e d
b y a presentation o f the conceptual m odel and its im p le m e n ta tio n .
2.1. Study area
The 6641 km^ stud y area includes po rtion s o f B anff N ational
Park in A lb erta and Kootenay N ational Park in B ritish Columbia,
about 130 km w est o f the C ity o f Calgary (Eig. 1). T opography is
rugged, ranging fro m 1000 to 3500 m in elevation. The clim ate
is stro n g ly seasonal, and characterized by long, cold w in te rs, and
short, w a rm sum m ers w ith m ost p re c ip ita tio n occurring in spring.
Lo w -e leva tion va lle y-b o tto m s contain the h ig h e st-q u a lity habitat
fo r w olves and elk, and are characterized by lodgepole pine (Pinus
contorta) and D ouglas-fir (Pseudotsuga menziesii) forests in te r­
spersed w ith rip a ria n w h ite spruce (Picea g la u c a )-w illo w {Satix
spp.), aspen (Populus tremuloides) parkland, and grassland systems.
Elk are the p rim a ry prey o f wolves, although alternate prey species
include w h ite -ta ile d deer, m ule deer, moose, big h o rn sheep, and
m o u n ta in goats. O ther large m am m als include g riz z ly and black
bears, w h ic h are om nivorous and feed on vegetation and on the
same prey item s used b y wolves.
The B anff and Lake Louise to w n sites are tw o ve ry popular
destinations located w ith in the study area th a t d ra w to u rists com ­
ing fro m Canada and in te rn a tio n a lly year-round. The stud y area is
bisected b y three m a jo r highways, nam ely the Trans-Canada H igh­
w a y 1, H ighw ay lA , and H ighw ay 93, and includes several other
tra n sp o rta tio n corridors. The Trans-Canada H ighw ay 1 has high
volum es o f tra ffic year-round. A d d itio n a l details o f the stud y area
can be found in Paquet et al. (1996) and Callaghan (2002).
2.2. Environm ental data
Several spatial datasets, in clu d in g elevation, aspect, slope, land
cover, and tra il and road ne tw orks w ere used to characterize the
study area’s physical and b io tic environm ents. A 30 m^ spatial
reso lu tion d ig ita l elevation m odel (DEM) and the tra il and road
n e tw o rk datasets w ere provided by B anff and Kootenay N ational
Parks. Aspect and slope were derived fro m the DEM using the Spa­
tia l A nalyst extension o f ArcM ap 9.2 (ESRi, 2009). A 30 m^ spatial
reso lu tion vegetation iand-cover m ap in clu d in g 16 classes gener­
ated fro m Landsat T hem atic M apper rem ote sensing images was
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M. M usiani et al. / Ecological M odelling 221 (2010) 2 37 4-2387
Alberta
British
C o lum bia
S
r ' \ '
B anff N ational Park x
Lake Louise
fro m fu rth e r analyses (M a n ly et al., 2002). Eor each species (e lk and
bear), a lo gistic regression analysis was conducted on a set o f can­
didate m odels th a t included d iffe re n t com binations o f the a b o v e described h a bita t variables. A kaike’s In fo rm a tio n C riteria (AIC), an
in fo rm a tio n -th e o re tic approach, was used to select the logistic
regression m odel th a t best predicted species occurrence (Burnham
and Anderson, 2002; Anderson and Burnham , 2002; Johnson and
Omland, 2004; Stephens et al., 2005).
The elk and g riz z ly bear RSE models were validated. Eor both
species, linea r regression R^ values calculated betw een each w ith ­
held subset o f data and the m odel predictions, using 4/5 o f data,
were sig nifica nt (n = 5 iterations, P< 0.001), and close to 1 (aver­
age value o f 0.993 fo r elk and 0.899 fo r grizzlies) in d ica tin g th a t the
m odels were valid according to jo h n s o n et al.’s (2006) criteria. V alid
elk and g riz z ly bear RSEs w ere used to generate maps o f the relative
p ro b a b ility o f h a bita t selection o f elk and g riz z ly bears across the
stud y area du rin g the sum m er (i.e., the stud y period) using Raster
C alculator InArcG IS 9.2 (ESRI, 2009).
2.3. Human presence data
Legend
■
Town
Road
s tu d y Area
i
i P rovincial Boundary
I
I Protected A rea
0 510
20
30
40
50
] Kilometers
Fig. 1. Location o f th e study area w ith in B anff and Kootenay N ational Parks w ith in
th e Central Rocky M ountains o f Canada.
H um an presence was de term in ed by using tw o datasets. The
firs t one was gathered b y Pacas (1996) w h o categorized roads and
tra ils w ith in the s tud y area in to one o f six hum an-use classes based
on relative use (Table 1). Pacas’ classes 1 and 2 represent the TransCanada H ighw ay 1 and all oth er roads, respectively, w h ile classes
3 -6 represent high-use to low -use trails. Secondly, data on in d i­
viduals, groups o r cars o rig in a lly collected by Parks Canada and
A lb erta Economic D evelopm ent and T ourism A u th o rity using tra ffic
and tra il counters at d iffe re n t e n try points in to Banff and Kootenay
N ational Parks were com piled to assess levels o f and changes in tra f­
fic by hour. By com b in ing b o th datasets, hum an use on all tra ils and
roads in the study area and its h o u rly changes was characterized
(T a b le !).
2.4. Den locations and home ranges o f w o lf packs
pro vid ed by the F oothills Research In stitu te G rizzly Bear Research
Program (M cD erm id et al., 2009).
Tw o resource selection fu n ctio n (RSF; Boyce and McDonald,
1999; M a n ly et al., 2002) m odels fo r elk and g riz z ly bears, respec­
tive ly, were also used in th is project to characterize the probable
locations o f elk and g riz z ly bears on the landscape. RSFs were used
to m odel each species’ preferred habitats (i.e., the relative p ro b­
a b ility o f selecting a h a bita t) in the stud y area in a Geographic
In fo rm a tio n System (GIS). A used-available lo gistic regression RSF
sam pling design was em ployed w here used locations were defined
b y locations provided by Global P ositioning System (GPS) radio col­
lars th a t were placed on in d iv id u a l elk and g riz z ly bears in the study
area. Details on these datasets o f GPS locations can be found in
H e bb lew hite et al. (2008) fo r elk and in Garshelis et al. (2005) for
g riz z ly bears. H abitat characteristics o f w ild life GPS locations w ere
com pared to characteristics at 10,000 ra n d o m ly located points,
w h ic h w ere produced w ith the H a w th ’s Tools Extension (Beyer,
2004) inArcGIS 9.2 (ESRI, 2009) and represented available locations
in the stud y area (Nielson et al., 2004).
H abitat characteristics used in elk and g riz z ly RSE m odels
included canopy closure, the ra tio o f deciduous to coniferous trees,
land-cover type, the annual m a xim u m norm alized difference vege­
ta tio n in de x (NDVI), annual NDVI range, distance to roads, distance
to facilities, distance to cam pgrounds, distance to trails, aspect (five
classes: flat, no rth , south, east, west), elevation, and slope. A Spear­
m an’s rank co rre la tio n (Zar, 1984) was calculated fo r all variables
considered in each o f the m odels (elk and g riz z ly bear). I f the cor­
re la tio n coefficients fo r any tw o variables were greater tha n 0.7,
these variables w ere considered h ig h ly correlated and rem oved
GPS collar data acquired d u rin g the w in te r and sum m er sea­
sons betw een 2002 and 2004 fro m 15 w olves belonging to five
packs were used to id e n tify w o lf den locations and hom e ranges.
The locations were collected at 15-m in, h a lf an hour, 1-h, o r 2-h
intervals, depending upon some k n o w n lim ita tio n s o f the satel­
lite s’ signal, on v a ria tio n in vegetation and topography, and on the
a b ility to com pute and record locations b y the GPS collar un its
(H e bb lew hite et al., 2007). An analysis o f the q u a lity and frequency
o f these GPS locations revealed th a t the signals recorded at 2-h
in te rvals were consistent and unbiased (according to the d e fin itio n
o f H ebblew hite et al., 2007) and w ere therefore retained fo r fu rth e r
analyses.
Home ranges were delineated by calculating a 95% kernel den­
s ity estim a to r w ith a sm oothing factor o f 7000 m (Seaman and
Powell, 1996) o f the w o lf te le m e try data using H a w th ’s Tools
(Beyer, 2004) InArcGIS 9.2. Dens were detected visu a lly by id e n tify ­
in g the m a xim u m con cen tra tion o f observed w o lf locations w ith in
the 95% kernel polygon. The lo catio n o f dens was la te r confirm ed
by field observations. The home ranges o f w o lf packs 1 and 5 are
traversed b y highw ays (Eig. 2). Pack 1 contains a sm all segment
o f H ighw ay 93 and H ighw ay 1 in the southw est pa rt o f the home
range, whereas Pack 5’s hom e range is lite ra lly s p lit b y H ighw ay 1,
lA , and 93.
2.5. Conceptualization and im plem entation o f the ABM model
The ABM m odel developed in th is stud y consists o f fo u r in te r­
related sub-m odels fo r wolves, bears, elk, andhum ans (Eig. 3).
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M. Musiani et al. / Ecological M o de llin g 221 (2010) 2 37 4-2387
Table 1
Hum an-use levels at each h o u r o f th e day on six d iffe re n t classes o f roads and tra ils in B anff and Kootenay N ational Parks, Canada.
H our
Class 1 (H ig h w a y 1)
Class 2 (roads)
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
12
0
0
0
0
24
84
228
527
1054
1462
1581
1438
1390
1414
1402
1282
1066
779
539
383
204
96
36
1
0
0
0
0
2
8
23
53
105
146
158
144
139
141
140
128
107
78
54
38
20
10
4
Class 3 (high-use tra ils )
0
0
0
0
0
0
1
2
5
11
15
16
14
14
14
14
13
11
8
5
4
2
1
0
The p rim a ry com ponent is the w o lf m odel, w h e re in w olves are
sim ulated as cognitive agents th a t have a m en ta l representation
o f th e ir e n viro nm en t. Bears, elk, and humans are represented as
objects w ith o u t cognitive abilities, and c o n trib u te to the dynam ic
com ponents o f the e n viro n m e n t in w h ic h w olves move and pe r­
fo rm activities. Each sub-m odel is described in the fo llo w in g
sections.
Class 4 (m e d iu m use tra ils )
Classes 5 and 6 (low -u se tra ils )
0
0
0
0
0
0
0
0
1
1
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
1
1
1
1
1
1
1
0
0
0
0
2.5.3. W o lf sub-model
The w o lf sub-m odel is com prised o f tw o com ponents: (i) the
landscape over w h ic h w olves move, and (ii) th e ir m ovem ent behav­
ior. The landscape is represented as a fric tio n map, w h ic h is
composed o f several static en viro n m e n ta l layers, w h ile the w o lfs
m ovem ent behavior is sim ulated as a correlated and biased ran­
dom w a lk based on a decision m odel. The w o lf m odel is composed
P8C>( 1
PacSc 5
Roads and Trails
Canada HWY 1
HWY 1A. 93 and Roads j
Trails
: Home ranges
Fig. 2. Estimated home ranges o f five w o lf packs in B anff and Kootenay N ational Parks, Canada. Road and tr a il n etw orks are also illu stra te d . Home ranges w ere delineated
b y calculating a 95% kernel den sity e stim a to r o f w o lf te le m e try locations.
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M. M usiani et al. / Ecological M odelling 221 (2010) 2 37 4-2387
Mode
-space
-agent
-object
A gent
-id
-navigate in
-has
O bject
+addWoifAgent()
^+addE!kObject()
+addBearObject()
+addHumanObject{)
J.
■duration : int
t-appearO
FdlsappearO
-supports agent & objects
•den : Ceil
-energylndex; int
presentAclivity : int
Space
+perceiveElk()
+perceiveHuman()
+perceiveBear()
+goToElkLocationC)
+goToKillSite()
+goToDen()
+patrolTerritory()
+rest()
+killElk()
+eatAndReslAtKillSile()
+feedPup()
+reportBear()
+reportHuman()
+reportDeath()
'^avoids
-part of
Cell
-moves over
-supports
-contains
-preys
-frictionMap
-rsfForElk
-rsfForBear
roadsAndTraiis
-contains
+moveAgentAt()
+getAgentMap()
+getTerrltoryMap()
+getElkMap()
+getHumanMap()
1
Human
-compose Df
-disturbs
Bear
-basemap for wolves
-disturbs
S for num<
-corric ors
umans r
FrictionM ap
; int
-y : int
■frictionValue : int
-rsfForElkValue; int
-rs F c rB e arV alu e: int
-X
*
1
-has
W olf
-avoids
-naglvale in
-has
■Id : int
-X : int
-elevation
-landcover
-aspect
-slope
RoadsAndTraiis
-class : int
Elk
-being hunted
Fig. 3. The conceptual agent-based m odel o f w olve s in B anff and K ootenay N ational Parks, Canada.
o f five in d iv id u a l wolves. Each w o lf represents a pack, based on
the assum ption th a t w olves belonging to the same pack have s im i­
la r behavioral patterns, an established n o tio n in behavioral studies
on w olves (Young and Goldman, 1944; Mech, 1970). W olves are
represented as agents in th a t th e y have cognitive capabilities and
are associated w ith a specific den and a home range. They have a
m en ta l representation o f th e ir e n viro n m e n t in w h ic h th e y move to
pe rfo rm various activities.
Paquet et al. (1996) found th a t w olves in the stud y area selected
fia t valley-b ottom s, south and southw est facing slopes, and decid­
uous vegetation at elevations b e lo w 1850 m. These findings were
used to w e ig h the influence o f these en viro n m e n ta l factors on w o lf
m ovem ents (Table 2) and, w ith th is objective, a fric tio n m ap (i.e.,
the landscape over w h ic h w olves m ove) was produced using spa­
tia l datasets o f land cover, elevation, slope, and aspect. However,
as a firs t step needed to ob tain the map, the o rig in a l iand-cover
layer was re-ciassified in to a reduced nu m be r o f classes (Table 3)
to m atch those used by Paquet et ai. (1996). W eig htin gs were
equivalent to preference assessments th a t to o k in to consideration
the options available to w olves to choose any h a bita t classes; the y
do not correspond to selection coefficients th a t in ecology ty p ic a lly
compare used vs. available habitats. The chosen spatial reso lu tion
o f the fric tio n m ap was lO O m ^, to take in to account the fact
th a t the m in im u m distance at w h ic h w olves perceive bears and
hum ans is 200 m (see below ). Thus at each tim e step, w olves move
100 m, i.e., fro m one 100 m^ ceil to a ne ig hbo uring 100 m^ ceil. The
te m p o ra l reso lu tion (tim e step) o f the m odel is 1 m in, a ve ry fine
scale level o f s tru ctu rin g th a t is appropriate fo r the v a ria b ility in
speed and resting periods exh ib ited by w olves in nature (M usiani
et ai., 1998; Mech, 1970).
Table 2
W e ig h tin g o f hab ita t variables used to b u ild a fric tio n map o f w o lf
m ovem ent in B anff and Kootenay N ational Parks, Canada.
H abitat variables
Elevation
Aspect
Land cover
Slope
Total
E igenvector w eig h ts
53.1
17.9
13.7
15.3
100.0
M. Musiani et al. / Ecological M o de llin g 221 (2010) 2 37 4-2387
Table 3
Reclassification o f th e Iand-cover map to m atch th e classes used by Paquet et al.
(1996) in th e ir fric tio n m odel o f w o lf m ovem ents.
N ew class
1
2
3
4
5
6
No data
Old class
Preference
C oniferous (D ouglas-fir, pine, spruce)
Aspen (m ixed forest and tre e d w e tla n d )
M eadows (shrubs)
Grassland (herbs)
Barren land, snow /ice
W a te r
Cloud, shadow
84.2
2.8
7.2
1.2
0.8
3.8
W h ittin g to n et ai. (2004) com pared three types o f sim uiated
w aiks w ith actuai w o ives’ m ovem ents. The authors conciuded th a t
the best fit was obtained w ith a ‘biased correiated random w a ik ’
in w h ic h the ne xt ceii is seiected w ith m in im a i random tu rn in g
angies fro m the d ire ctio n o f the iast ceii, as w e ii as m in im a i ran­
dom tu rn in g angies tow a rds a preferred destination. The directio nai
vecto r can be chosen ra n d o m iy or p re fe re n tia iiy. in th is study,
we assumed th a t w oives choose w here to go p u rpo sefuiiy (e.g., a
w o if seeks some k n o w n eik de nsity patches) and th a t th e ir a ctiv­
ity was n o t com p ie te iy random (Bartum eus et ai., 2005). However,
to accurateiy sim uiate anim ai m ovem ents on the iandscape, severai o th e r factors m ust be taken in to account at the same tim e,
in ciu d in g preferred directio n, a w e ig h t factor in d ica tin g a ttra ctio n
strength tow a rds the d ire ctio n a i vector, and a h a bita t q u a iity index,
w h ic h represents the cum uiative s u ita b iiity o f aii h a bita t variabies
(Topping et ai., 2003).
W e therefore appiied the the oretica i approach o f Topping et ai.
(2003) and m ethods firs t deveioped fo r w oives by W h ittin g to n et
ai. (2004), w h e re in w oives pick a random ceii w ith in eigh t ne ig h­
bo u rin g ceiis using in fo rm a tio n regarding the directio ns o f th e ir
previous m ovem ent and th a t tow a rds a destination, and the habitat
q u a iity o f these ceiis (often referred to as its inverse: fric tio n vaiue).
Fig. 4 dispiays ho w in th is stud y a w o if agent seiects the next ceii
based on its past iocation and d ire ctio n o f m ovem ent, a directio nai
vecto r in d ica tin g preference tow ards a de stina tion (dashed arrow ),
and a w e ig h t factor in d ica tin g h a bita t s u ita b iiity (grey shade), in the
fig u re ’s sampie case, h a bita t s u ita b iiity is high er in da rke rg re y ceiis.
This m ethod is s im iia r to the ABM o f Fiorida pa nthe r m ovem ent
deveioped b y Cramer and P ortier (2001).
current position
last posilion (one
step back)
Position 1
steps back
two
Position ,,,^ three
steps oack
Fig. 4. Illu s tra tio n o f th e correlated and biased random w a lk m odel to sim ulate how
w olves m ove on th e landscape.
2379
The w o ifs m ovem ents are determ ined by the spatiai d is trib u ­
tio n o f resources (such as prey, fo r exam pie) and o f threats to
safety (such as the presence o f hum ans or bears), and by the in te rnai m o tiv a tio n and a c tiv ity w oives are engaged in. The activities
sim uiated in our m odei (Fig. 5) are hunting, p a tro iiin g te rrito ry ,
feeding pups, and resting. The m odei encompasses the period fro m
A p rii 16 to O ctober 15, w h ic h is hom ogenous fo r key ecoiogicai fac­
tors in v o iv in g w oives and o th er organism s affecting woives. D uring
th is period, in the study area w oives are centrai piace foragers
around a den an d/or rendezvous sites fo r the pack; the ievei o f
hum an use is ord ers-o f-m a gnitu de greater tha n th a t o f the w in ­
te r season; g riz z iy bears are present, whereas in the w in te r the y
are in hib e rn a tio n ; and fin a iiy, m ig ra to ry and n o n -m ig ra to ry eik
are d istrib u te d depending on p ia n t phenoiogy characteristic o f the
sum m er (H e bb iew hite et ai., 2008).
Behaviorai ruies fo r w oives were derived fro m the scie ntific iiterature, fro m experts’ opinions, and fro m ou r fieid observations
(Fig. 5). Each w o if agent (representing a pack) was assigned an
energy in de x vaiue th a t ranged fro m 0 to 100. A t every m inute,
w h ic h corresponds to the te m p o ra i reso iu tion (tim e step) o f the
modei, the energy in de x vaiue decreases based on th is equa­
tio n (energyt+i = e n e rg y t-0 .3 ). The vaiue 0.3 is given by: 100/14
days/24 h to sim uiate the fact th a t the m o rta iity o f a w o if m ig h t
happen after 14 days w ith o u t food, a period consistent w ith the iiterature (Mech, 1970). W hen a w o if agent finishes eating, it reaches
satiation and its energy vaiue is 100; w h en the energy index
reaches 0, the w o if dies. W oives em bark fro m th e ir dens tow ards
an intended de stina tion picked ra n d o m iy w ith in the pack’s home
range, and the n w oives a tte m p t to move tow a rds th a t d ire ctio n
using the path w ith the ieast fric tio n . They are attracted to tra iis for
ease o f navigation, b u t do not use th e m i f the encounter rate w ith
humans increases. W o if preference fo r tra iis at io w hum an-use ieveis is k n o w n fro m the iite ra tu re and docum ented aiso in the study
area (W h ittin g to n et ai.,2005; Paquet etai., 1996; Caiiaghan, 2002).
W hen w oives reach the targeted destination, th e y aim at another
iocation as a new destination.
As w oives p a tro i th e ir te rrito ry , th e y are aiiow ed to deviate
tow ards a food source (exampie, eik). Practicaiiy, w h e n th e ir energy
in de x is b e io w 80% and th e y perceive the presence o f eik w ith in a
3 km range, w oives move tow a rds the eik iocation. The thresh oid o f
80% fo r the energy in de x was seiected based on a se n s itiv ity anaiysis o f the m odei, w h ic h dem onstrated th a t w oives couid n o t start
searching fo r food at io w e r energy ieveis o r th e y w o u id risk starva­
tio n due to a iack o f tim e to h u n t successfuiiy. W hen pursuing eik,
there is a 30% p ro b a b iiity o f a k iii, w h ic h is consistent w ith e m p iricai data on h u n tin g success (Mech, 1970; Peterson and Page, 1988;
Post et ai., 1999).
i f a w o if is successfui at k iiiin g an eik, it w iii start consum ing the
carcass and keep eating fo r 6.5 h. This d u ra tio n is consistent w ith
the spa tio-tem p orai ciusters o f w o if iocations th a t are attrib u ta b ie
to eik kiiis in w estern Canada and in the study area in particuiar,
fo r the sum m er denning period (H e bb iew hite et ai., 2005a; W ebb
et ai., 2008), w h ic h is the focus o f o u r m od eiing effort. Once a w o if
has finished eating, it returns by the shortest ieast fric tio n route
possibie to the den to feed the pups. A fte r a successfui h u n tin g bout,
a w o if w iii make re tu rn trip s to the k iii to coiiect m ore food; such
food is often regurgitated fo r the pups at the den site (H a rrin g to n
and Mech, 1982). in the sum m er denning period, these re tu rn trip s
fo r fe rry in g food back to the pups m ay aiso be separated b y about
6 -7 h o f eating, feeding pups, and resting b o th at dens and k iiiin g
sites (H e bb iew hite et ai., 2005a). i f a w o if does not encounter any
eik w h iie p a tro iiin g its te rrito ry , it returns to its den after 4 days to
attend to pups.
W h iie a w o if is p a tro iiin g its te rrito ry , if it perceives the pres­
ence o f any hum an o r bear w ith in a 200 m range, it stops its a c tiv ity
and rem ains at the same iocation fo r the d u ra tio n o f the bear or
2380
M. M usiani et al. / Ecological M odelling 221 (2010) 2 37 4-2387
Report:
Bear o r H um an
-ch a s in g
w it h in 200m
-k illin g
-d is tu rb a n c e
E a tin g and
G o t o Elk
-d e a th
Killer^ Elk
lo c a tio n
r e s tin g 6 -7 h o u rs
Abandon
kill
Yes
Bring rnore
E lk w ith in 3km
No
foods for
Go to den
pups (after 6-
to feed
7 hours
pups
feeding)
F in d ra n d o m
B ear o r H um an
w ith in 200m
lo c a tio n in h o m e
DEN
ra n g e to p a tro l
Go to den
14 days
to take
w ith o u t
care pups
fo o d s
R e stin g
4 days
11-13
w ith o u t
h o u rs
fo o d
Fig. 5. C onceptual decision m odel o f th e w o lf agent w h e n m o vin g th ro u g h th e landscape.
hum an disturbance. The 200 m distance w h e re in w olves are s ig n if­
ic a n tly influenced by humans o r bears were consistent w ith studies
conducted on h u m a n -w ild life interactions. Hikers can e lic it equal
o r m ore intense behavioral and m etabolic responses fro m w ild life
tha n vehicles (M a cA rth u r et al., 1982; Freddy et al., 1986; Holmes
et al., 1993; Andersen et al., 1996; M cLellan and Shackleton, 1988).
Previous research has focused on the distance necessary to provoke
a response fro m approaching pedestrians (i.e., by fleeing) in various
species o f large m am m als (Schultz and Bailey, 1978; Cassirer et al.,
1992; Papouchis et al., 2001; Karlsson et al., 2007). Karlsson et al.
(2007) found th a t the distance required to provoke a response in
w olves ranged betw een 17 and 310 m. However, it is unclear ho w
varying hum an a c tiv ity levels m ay affect flig h t distances. In any
case. W isdo m et al. (2004) found th a t the p ro b a b ility o f elk fleeing
declined ra p id ly w h e n hikers w ere beyond 500 m.
W olve s’ h u n tin g success and con sum p tion o f prey can be
disrup te d due to the presence o f com p etitors such as bears
(H e bb lew hite and Sm ith, 2010). In the m odel, w olves are influenced
b y g riz z ly bears w h ile th e y navigate (as explained above) o r w h ile
th e y consume prey. This is sim ulated using the fo llo w in g rule: i f a
w o lf is consum ing a carcass at a k ill site, it w ill abandon the site i f a
bear appears w ith in a range o f 200 m. However, the w o lf w ill keep
tra ck o f the abandoned k ill location fo r 2 weeks and it w ill re tu rn to
the site (i.e., to eat some leftovers) i f its energy thresh old is be lo w
30%.
2.5.2. Bear and elk sub-models
The elk and bear sub-m odels use the RSFs described pre vio usly
to estim ate the relative p ro b a b ility o f an a nim al selecting a specific
resource u n it (M a n ly et al., 2002; Johnson et al., 2006). Both bear
and elk sub-m odels are o f 100 m^ spatial re so lu tio n to be co m p a ti­
ble w ith the w o lf m odel. Each RSF was re-classified in to three bins
o f selection p ro b a b ility scores: lo w (0.0-0.33), m ed iu m (0.34-0.66)
and high (0.67-1.0). Based on the RSF selection p ro b a b ility bins, 24
elk herds (each herd representing about 25 elk) and 21 g riz z ly bears
were assigned a location in the landscape. The to ta l nu m be r o f elk
and bears was estim ated by c o n sulta tion w ith B anff and Kootenay
N ational Parks personnel and w ith w ild life experts w o rk in g in the
stud y area. Each bear and elk is assigned a location on the landscape
based on the above selection p ro b a b ility b in values (i.e., a bear was 3
tim es m ore lik e ly to occur w ith in a p ixe l w ith a high RSE score than
lo w RSE score), and rem ains there fo r a p p ro xim a te ly 3 0 -4 0 m in.
W hen th a t d u ra tio n is over, the bear o r elk disappears fro m th a t
location, and reappears elsewhere based again on the RSE values,
and the n stays there another 3 0 -4 0 m in (i.e., the tim e step fo r elk
and bear presence).
This m ethodological approach lacks realism ; however, it repre­
sents a reasonable means to m odel anim al d is trib u tio n estimates
available, i.e., RSEs w h e n m od eling anim al m ovem ent is not fea­
sible due to the lack o f data. RSEs have been w id e ly used over
the last decade to m odel the p ro b a b ility o f an anim al selecting a
resource u n it (M a n ly et al., 2002; Johnson et al., 2006). Strickland
and M cDonald (2006) describe a w id e range o f applications o f
resource selection statistics. In th is study, bears and e lk appear and
disappear on the landscape p ro p o rtio n a l to the relative p ro b a b ility
o f selecting a habitat.
2.5.3. Human sub-model
In the hum an sub-m odel, hum an presence, represented as an
in d ivid u a l, a group o f hum ans o r a car carrying hum ans is dis­
trib u te d along roads and tra ils at every h o ur based on th e ir relative
usage classes (Table 1). Once th e y are located on a road o r tra il, the y
rem ain there fo r 1 h. A 200 m b u ffe r zone o f influence is delineated
around each road and tra il fo r th a t hour, a distance th a t is consis­
te n t w ith the detection capacity o f wolves, as it is explained above.
Increases o f hum an presence o f 5 tim es (H5X) and 10 tim es (H I OX)
th a t o f cu rre n t estim ated levels o f presence in the study area (H IX )
were sim ulated.
M. Musiani et al. { Ecological M o de llin g 221 (2 0 1 0 )2 37 4 -2 38 7
2381
Table 4
Percentage o f tim e actual (observed) and sim ula te d w o lf packs spent at th e den and not tra v e lin g at norm a l estim ated levels o f hum an presence (H IX ) on roads and tra ils,
and 5 tim e s (H 5X) and 10 tim e s (H lO X ) norm a l estim ated levels o f hum an presence in B anff and Kootenay N ational Parks, Canada.
Tim e at den
Tim e not tra ve lin g
Observed (%)
H1X(% )
H5X(%)
H10X(%)
Mean(% )
SD(%)
Observed (%)
H1X(%)
H5X(%)
H lO X (%)
Mean (%)
SD (%)
P ackl
Pack 2
Packs
Pack 4
Packs
12.41
0.56
1.99
4.01
4.73
2.62
2.62
3.35
2.20
1.33
2.11
2.57
2.94
1.93
0.78
2.11
2.80
3.54
2.25
0.51
4.81
2.14
2.95
2.60
1.84
5.07
1.06
0.69
0.95
1.96
78.30
91.63
89.72
90.59
88.70
34.53
37.42
35.31
32.28
35.49
33.79
37.24
35.86
34.07
77.87
32.92
38.06
36.96
33.10
94.86
44.88
51.09
49.46
47.51
74.23
22.29
27.03
26.85
28.73
26.76
Mean
SD
4.74
4.59
2.42
0.74
2.07
0.82
2.24
1.12
87.79
5.41
35.00
1.86
43.76
19.12
47.18
26.75
2.6. Sim ulation fram ew ork
Seven sets o f param eters were used in the m odel o f w o lf behav­
io r: nu m be r o f bears (12, 21, and 30), nu m be r o f elk herds (12, 24,
and 36), and hum an presence (H IX , H5X and H I OX). To account fo r
e n viro n m e n ta l stoch astlclty and fo r v a ria b ility In the m odel o u t­
puts, runs w ith each set o f param eters were replicated 15 tim es. The
s im u la tio n results correspond to the average o f the values obtained
In these 15 replicates, w ith e rro r bars representing the standard
error.
The m odel has a re p o rtin g m echanism describing the Instances
o f various events at each tim e step o f 1 m in. Im p o rta n t outputs o f
the m odel Include the traje cto rie s ofw o lve s, w h ic h are represented
(a )
O b s e rv e d w o lf lo c a tio n s
as a series o f p o in t locations (x, y coordinates and tim e stamp).
This allow ed com parison w ith the observed dataset fo r GPS-collared
w olves, w h ic h Is also com prised o f p o in t locations. For th is p u r­
pose, p o in t locations fo r sim ulated w olves w ere sub-sam pled at
2-h Intervals s im ila r to the tem po ral reso lu tion fo r GPS-collared
wolves. The m odel also reports oth er critica l parameters, such as
w h en a w o lf agent crosses a h igh w ay o r tra il, encounters a hum an
or a bear d u rin g eith e r na vig ation or feeding on prey, chases an
elk or kills It, rests, leaves the den, o r dies. The m odel was Im p le ­
m ented In JAVA pro gra m m ing language using NetBean IDE w ith
RePast 3.1 lib ra ry (N o rth et al., 2006). The RePast lib ra ry was
used p rim a rily to visualize the ou tputs and con tro l the s im u la tio n
engine.
(b)
S im u la te d w o lf lo c a tio n s H IX
'X
-it
f-
/
(c)
I
I Hnnar.ngM
S im u la te d w o lf lo c a tio n s H 5X
S im u la te d w o lf lo c a tio n s H1 OX
(d)
V
1
1hoire r4j»}«s
I
I hftineti
j
Pack
‘
1
2
3
/
/
f
\
_ **
*'
\
*
\
1
r
\
/
5
v
J
Fig. 6. W o lf locations w ith in th e stu d y area obtained fro m : (a) w o lf GPS ra dio co lla r points acquired fro m 2002 to 2004, (b) sim ulated w o lf locations w ith estim ated actual
hum an presence (H IX ), (c) sim ulated w o lf locations w it h hum an presence 5 tim e s estim ated actual hum an presence (H5X), and (d ) sim ulated w o lf locations w it h hum an
presence 10 tim e s estim ated actual hum an presence (H lO X).
2382
M. M usiani et al. / Ecological M odelling 221 (2010) 2 37 4-2387
K5X
H u m an u se level
H u m an u se level
H u m an u se level
H u m an u se level
Fig. 7. Frequency o f w o lf-h u m a n encounters on roads at va ryin g levels o f hum an presence fo r: (a) Pack 1 and (b) Pack 5; sim ulated d u ra tio n o f w o lf-h u m a n in te ra c tio n on
roads at va ryin g levels o f hum an use for: (c) Pack 1 and (d ) Pack 5. The results represent an average o f 15 replicates; e rro r bars represent th e standard deviation.
2.7. Model validation
The va lid a tio n o f the m odel was perform ed by com paring the
sim uiated tra j ectories o f w o lf packs w ith in five k n o w n hom e ranges
w ith the observed traje cto rie s o f real wolves. F ollow ing Tew and
M acdonald (1994), nonp aram e tric u tiliz a tio n d is trib u tio n analyses
w ere appiied to measure the overlap betw een the sim uiated and
observed w o if iocations at 2-h tim e intervals. Using ArcM ap 9.2,
the area encompassing all w o if hom e ranges was d ivide d in to a
g rid o f 426 cells m easuring 4 km x 4 km (Mech, 1970) and covering
five home ranges in such a w a y th a t each hom e range was cov­
ered by at least fifty g rid cells. This ceii size satisfies the conditions
suggested b y Doncaster (1990) th a t the size o f the cells m ust be
large enough th a t some cells con tain several fixes, b u t not so large
as to obscure the overall co n figu ration o f the range. The v is it fre ­
quency o f sim uiated and observed w o if iocations was calculated
fo r each ceii. Spearman corre latio n (tw o -ta ile d ) was used fo r te st­
in g the association betw een these tw o measures over aii grid ceiis,
representing correlations in range use. A high corre latio n im plies
n o t o n ly a high overlap, b u t aiso s im ila r spatial u tiliz a tio n . A pe r­
m u ta tio n test im p lem e nted in SPSS version 10.0 was applied to test
w h e th e r the observed value o f Spearman’s rho was sig n ifica n tiy
d iffe re n t fro m zero.
W e also com pared the percentage o f tim e spent at and around
dens (200 m buffers) fo r observed w oives and fo r w olves sim uiated
at the three levels o f hum an presence (H IX , H5X and H lO X). The
200 m vaiue was consistent w ith w o lves’ perception o f the e n vi­
ronm ent, as explained previously, it is lik e ly th a t w oives consider
themselves in p ro x im ity o f a den at such distance, s im ila r to ho w
th e y perceive p ro x im ity to a disturbance a c tiv ity by a hum an or
a bear. W ith s im ila r approaches, w e com pared the percentages o f
tim e spent n o t tra ve lin g (speed < 1 km /h ) fo r the five packs at the
observed and the three sim ulated levels o f hum an presence. W e
used th is 1 k m /h thresh old as w olves are described in the lite ra ­
tu re as ‘tra v e lin g ’ at speeds o f high er tha n 1.5 km /h (M usiani et al.,
1998; Mech, 1970,1994). Differences w ere evaluated using a Fried­
m an test, w h ic h provides Chi-square and associated P-values. W e
also reported mean and standard de via tion values o f percentages
fo r time a t den and time not traveling.
3.
Results and discussion
3.1. D istribution o f observed w o lf locations and locations
simulated w ith increasing human use
Fig. 6 shows the d is trib u tio n o f observed w o lf locations taken
re g u la rly at 2-h intervals (Panel a) and the d is trib u tio n o f sim u ­
lated w o if locations aiso at 2-h in te rvals w ith estim ated typica l
hum an presence (H IX ), projected 5 tim es hum an presence (H5X),
and projected 10 tim es hum an presence (H lO X ) (Panels b, c,
and d, respectively). A visual com parison o f sim uiated locations
draw s a tte n tio n to s im ila r d is trib u tio n s as those observed w ith
real wolves, w ith some notable exceptions. A closer exam ina­
tio n o f observed and sim ulated w o lf iocations w ith estim ated
hum an presence H IX (Panels a and b, respectively) reveals th a t
the observed iocations fo r Pack 5 were clustered around a valleyb o tto m th a t contains highw ays and tra iis used by humans. The
observed iocations fo r Pack 1 w ere aiso clustered around a valleyb o tto m th a t contains in th is case a n e tw o rk o f tra iis (see Fig. 2).
This resu lt indicates th a t o u r m odel underestim ates w o lves’ selec­
tio n fo r va lle y-b o tto m s at m oderate hum an presence ieveis, lik e ly
due to ease o f m ovem ent and to the presence o f im p o rta n t
resources fo r w oives in these iocations, a phenom enon th a t has
been docum ented in the stud y area (Paquet et ai., 1996; Caiiaghan,
M. Musiani et al. / Ecological M o de llin g 221 (2010) 2 37 4-2387
2002). However, w ith sim ulated increased hum an presence, the
sim ila ritie s betw een sim ulated and observed locations increased,
especially fo r Packs 1 and 5, w h ic h have home ranges th a t encom ­
pass areas w ith re la tiv e ly high er hum an use (Panels c and d). Our
m odel therefore indicates th a t w h en hum an use is high o r ve ry high
(H5X and H I OX), w o lf locations m aybe clustered at valley-bottom s,
w here m ost basic resources fo r the species are concentrated. In
fact, va lle y-b o tto m s are characterized b y concentration o f prey,
w a te r and security cover fo r wolves, especially in the stud y area
(Percy, 2003; Shepherd and W h ittin g to n , 2006). U nder these c ir­
cumstances w olves m ig h t tra ve l less to o th e r less critic a l areas
o f th e ir home ranges, lik e ly because m oving m ore increases the
chance o f encountering people (see below ).
In a d d itio n to a visual com parison, an analysis o f spatial u t i­
liza tio n w ith in hom e ranges suggests correspondence betw een
observed and sim ulated w o lf locations over grid cells covering
each o f the five w o lf pack hom e ranges. W ith in cells, locations
fo r real w olves and fo r sim ulated w olves experiencing estim ated
typ ica l hum an presence (H IX ) are h ig h ly correlated (Spearm an’s
rho 0.566, P< 0.001), w h ic h suggests correspondence in range use
and high overlap (i.e., s im ila r u tiliz a tio n ). The corre latio n values
fu rth e r increase betw een observed w olves and sim ulated wolves
experiencing levels o f hum an presence 5 tim es (H5X) and 10 tim es
(H lO X ) high er (Spearman’s rho 0.589, P < 0.001 and Spearman’s
rho 0.612, P< 0.001, respectively). However, analyses conducted
2383
• HIX
• HSX
■ HlOX
Fig. 8. S im ulated frequency o f w o lf-h u m a n encounters on tra ils at va ryin g levels
o f hum an use fo r each w o lf pack (Packs 1, 2, 3, 4 and 5). The results represent an
average o f 15 replicates; e rro r bars represent th e standard deviation.
separately fo r each home range and pack dem onstrated th a t such
increases w ith high er hum an presence are not a p a tte rn com m on
to all packs. C orrelation values increase betw een observed wolves
and sim ulated w olves o f Pack 2 w ith hum an presence increas­
u
Pack 4
\
24
0
32
■Kilometers
H o m e ra n g e s w ith h u m a n 1X
' H o m e ra n g e s w ith h u m a n 5X
*
H o m e ra n g e s w ith h u m an 10X
Fig. 9. Shrinkage o f w o lf hom e ranges associated to d iffe re n t levels o f hum an presence.
2384
M. M usiani et al. / Ecological M odelling 221 (2010) 2 37 4-2387
in g 5 tim es (HSX) and 10 tim es (H lO X ) (w ith Spearman’s rho
peaking at 0.726, P< 0.001). S im ilarly, corre latio n values increase
w ith hum an presence fo r Pack 5, w ith Spearman’s rho peaking at
0.651 (P< 0.001). However, corre latio n values did n o t increase w ith
increasing hum an presence fo r Packs 1, 3 and 4.
Finally, Table 4 shows the va lid a tio n results fo r tim e spent at
dens and fo r tim e spent n o t tra ve lin g by wolves. Percentage o f
tim e spent at and around dens (200 m buffers) fo r observed w olves
and fo r w olves sim ulated at three levels o f hum an use did not
va ry across packs (n = 5, Chi-square = 2.63, P= 0.452), o r across the
observed and the three sim ulated levels o f hum an use (n = 4. Chisquare = 5.72, P= 0.221). Therefore, there is f it betw een o u r m odel
and the observed behaviors. V a lid a tio n results were m ore variable
fo r the analysis o f tim e spent n o t travelin g. The five packs spent
d iffe re n t percentages o f tim e not tra ve lin g (speed < 1 km /h ) at the
observed and the three sim ulated levels hum an use (n = 5. Chisquare =8.28, P= 0.041), largely due to the observed packs resting
m ore tha n the sim ulated packs. Differences were also detectable
w ith regard to tra ve lin g behavior across the observed and the three
sim ulated levels o f hum an use (n = 4, Chi-square = 11.00, P= 0.027),
largely due to Pack 5 spending less tim e tra ve lin g tha n the oth er
packs. These v a lid a tio n results are consistent w ith those explained
above fo r the spatial d is trib u tio n o f locations. There are discrep­
ancies betw een our sim u latio ns and the observed patterns. As a
general pattern, w olves seem to move less in re a lity tha n in our
model, perhaps due to th e ir reaction to high levels o f hum an use.
Consistent w ith th is in te rp re ta tio n , the pack experiencing the h ig h ­
est level o f hum an use (Pack 5) moves less tha n the o th e r packs and,
as explained above, has m ovem ent patterns consistent w ith those
sim ulated w ith the highest hum an-use values.
d
4
3S
3
3
JC
o
£
2
O
IH IX
>
£n01
a
2
■ HIX
■hSX
1
■ HlOK
O’
>
1
o
c41 d>
£
u.
■ H5X
15
■ HlOX
1
05
•1
0
GDing tc Elk l« 4tic n
-2
-0 5
(d)
(c)
■ HIX
■ H5X
■ HlOX
Goineto Elk Locatior
. m
Going to Elk Location
Ealing at Xifijfte
100
BO
■ H lX
60
■H5K
■ HlOX
40
0
Going to Elk Location
Fig. 10. Frequencies o f hum an disturbance o f various w o lf a ctivitie s th a t are im p o rta n t fo r survival, in c lu d in g fin d in g prey (‘going to e lk loca tio n ’), eating at k ill sites, going
to k ill sites, and feeding pups (Panels a, b, c, d and e fo r Packs 1, 2 ,3 , 4 and 5, respectively) and effects on w o lf m o rta lity (Panel f) o f va ryin g hum an-use levels. The results
represent an average o f 15 replicates; e rro r bars represent th e standard deviation.
M. Musiani et al. / Ecological M o de llin g 221 (2010) 2 37 4-2387
3.2. Frequency and duration o f encounters w ith humans on roads
and trails
The home ranges o f w o lf Packs 1 and 5 are traversed by h ig h ­
ways. Pack I ’s home range includes a sm all segment o f H ighw ay
93 and H ighw ay 1, whereas Pack 5’s hom e range Is lite ra lly sp ilt
by H ighw ay 1. P otentially, th is pack m ig h t be severely affected by
humans, as there Is considerable tra ffic on H ighw ay 1 th ro u g h o u t
the year. Fig. 7(a and b) shows the frequency o f encounters w ith
hum ans on roads fo r Pack 1 and Pack 5, respectively. W ith hum an
presence Increasing fro m current, to five and to te n tim es curre nt
levels o f hum an presence (H IX , HSX, HlO X), Pack I ’s encounters
w ith humans on roads Increased; whereas, fo r Pack 5, encoun­
ters decreased at ve ry high levels o f hum an presence (H lO X). The
resu lt fo r Pack 5 seemed co u n te rin tu itive . However, In -d e p th Inves­
tig a tio n revealed th a t In case o f sustained and con tinu ed hum an
presence, the w o lf perceives and registers a single, b u t c o n tin ­
ued encounter/disturbance event. Such an event Is the resu lt o f
the Intera ction betw een one w o lf agent (I.e., a pack) and several
hum an obj ects appearing on consecutive occasions w ith in the same
200 m b u ffe r zone o f the w o lf locations. Fig. 7 (Panels c and d) Illu s ­
trates th is n o tio n by h ig h lig h tin g h o w the d u ra tio n o f w o lf-h u m a n
encounters (and therefore the d is ru p tio n o f w o lf behavior) fo r bo th
Packs 1 and 5 Increased w ith Increasing hum an presence. Therefore,
fo r Pack 5 the frequency o f encounters decreased, b u t Its d u ra­
tio n Increased (Fig. 7b and d, respectively). A n o th e r feature w o rth
h ig h lig h tin g Is th a t the d u ra tio n o f hum an-caused disrup tions o f
Pack 5’s behaviors In itia lly Increased sharply, and the n m oderately,
w h ic h m ig h t relate to an atten uatio n effect consistent w ith the
m o rta lity rate findings th a t are explained below .
Increased hum an presence affected w o lf m ovem ents not o n ly on
roads, b u t also on trails, w ith d is tin c t patterns fo r each w o lf pack
(Fig. 8). Compared to Packs 1 ,3 ,4 and 5, Pack 2 had high er encounter
rate w ith people, lik e ly because o f Its sm all hom e range (1224 k m ^ ),
w h ic h encompasses a concentrated n e tw o rk o f trails. A t the oth er
extrem e. Packs 1 and 4 were the least affected, since th e y have
larger hom e ranges (2085 and 1359 km ^, respectively) and m ore
options available to move to areas w ith lo w e r tra il density.
3.3. Human influence on size o f w o lf home ranges
If there are high levels o f hum an presence on the landscape,
w olves m ig h t be forced Into areas th a t are n o t o p tim a l In term s o f
prey density, and th is m ig h t have a sig nifica nt effect on survival. In
addition, un d e r high hum an-use c on ditions w olves m ay not be able
to h u n t successfully, because hum ans m ig h t Interfere w ith h u n t­
ing w olves o r w ith th e ir con sum p tion o f prey. Fig. 9 Illustrates
these scenarios w h e re in the home range o f w o lf Pack 5 shrank
v is ib ly as hum an presence Increased. Such hum an-caused effects
on home range size m ig h t also result In decreased a b ility to use
habitats frequented by w o lf prey, w ith patterns analyzed In detail
below .
3.4. Disruption o f crucial activities and effects on survival
Fig. 10 Illustrates the frequencies o f hum an disturbance o f v a ri­
ous w o lf a ctivities th a t are Im p o rta n t fo r survival. Inclu ding fin d in g
prey (going to elk location), eating at k ill sites, going to k ill sites, and
feeding pups (Panels a, b, c, d and e fo r Packs 1, 2 ,3 ,4 and 5, respec­
tive ly). W hen the level o f hum an presence Increased to 10 tim es,
the Im pact on all w o lf a ctivities Increased fo r Packs 2 (Fig. 10b)
and 5 (Fig. lOe), w h ile o n ly some a ctivities were disturbed fo r the
oth er packs (Fig. 1 Oa, c, and d). W o lf Pack 4 experienced little dis­
turbance (Panel d), lik e ly because Its hom e range Is In ‘ba ckcou ntry’
areas, less frequented by visitors. W o lf Pack 5 seemed to experience
few e r Im pacts w h e n hum an presence was 10 tim es greater, com ­
2385
pared to 5 tim es (Fig. lOe). A closer analysis Illustrates th a t tra ffic
on tw o Im p o rta n t tra n sp o rta tio n routes present In the hom e range
m ay cause Increased d u ra tio n o f disturbance, w h ic h Is perceived
b y Pack 5 as continuous disturbance, as explained previously. As
a result. Pack 5 has lim ite d tim e fo r h u n tin g and fo r o th e r c r iti­
cal activities; circum stances th a t lead to Increased m o rta lity o f this
pack (Fig. lO f).
4. Conclusions
Recreation and tra n sp o rta tio n m ay have an array o f Im m ediate
and lo n g -te rm Influences on anim al behavior even w ith in national
parks (Boyle and Samson, 1985; Forman and Alexander, 1998;
T rom bu la k and Frlssell, 2000). A ctivitie s such as h ik in g and b ik ­
ing on trails, and vehicle a c tiv ity on roads m ay affect a w id e range
o f species such as fo r exam ple In large m am m als: moose (Yost
and W rig h t, 2001), deer (Freddy et al., 1986), bobcats (Lynx rufus)
and coyotes (Canis latrans) (George and Crooks, 2006), b igh orn
sheep (K eller and Bender, 2007), bison (Bison bison) and pro ng horn
(Antilocapra americana) (Taylor and Knight, 2003), and black bears
(Ursus americanus) (Kasw orm and M anley, 1990). However, the
Im pact o f human-caused behavioral changes In anim als and on
th e ir survival Is often un kno w n.
ABM can assist In discovering know ledge gaps and can provide
the oretica l Insights by fo rm a lizin g the com ponents o f a system
(fo r example, an ecosystem Inclu ding w olves and o th e r species)
and e xp lo rin g th e ir Interactions. This can be p a rtic u la rly useful for
Investigating the Im pact o f behavioral changes by hum ans and the
Id e n tifica tio n o f adequate m anagem ent practices to reduce this
Im pact. Such an approach Is cle arly Illu stra te d by Bennett et al.
(2009) w h o Im ple m en te d a b e h a vio ra l-e xp licit ABM to explore
w ild life species responses to patterns o f anthropogenic d is tu r­
bances to be used as a m anagem ent to o l In order to m in im iz e the
d e trim e n ta l Im pact on w ild life populations.
In attem pts to fu lfill a s im ila r goal, the strength o f o u r m odel
lies In Its a b ility to capture the outcom e o f Interactions betw een
w olves and humans at a fine tem po ral scale (w ith hum an-use data
gathered at every ho ur) and spatial reso lu tion (In 100 m units), and
at the In d ivid u a l level. The pe rfo rm ed sim u latio ns reveal th a t w o lf
m ovem ents and behaviors are considerably affected by Increasing
In te n sity o f hum an presence. W olves have less tim e to search for
prey, to hunt, to access and consume a prey Item , and to attend
to th e ir pups, w h ic h m ig h t resu lt In m o rta lity . The m ost affected
w o lf pack (Pack 5) Is the one whose den Is closest to an Im p o r­
ta n t tra n sp o rta tio n route (the Trans-Canada H ighw ay 1) and to
another highw ay. Overall, the m odel probably under-represents
hum an Im pact because w o lf a ctivities are resum ed once encoun­
ters w ith hum ans are over, w h ic h m ig h t not be the case In reality.
In fact, a nu m be r o f studies have show n th a t hum an-caused d isru p ­
tio n o f anim al behaviors m ig h t not be reversible, and forces anim als
to p e rm a n e n tly s h ift to o th e r a ctivities (Boyle and Samson, 1985;
Forman and Alexander, 1998; T rom bu la k and Frlssell, 2000). Even
w ith th is u n d e r-repre sen ta tlon o f hum an Im pact, severe Impacts
on w o lf behavior and also on survival were observed.
Despite the focus given to predators and w olves specifically
(e.g., Soule et al., 2003), In m ost ecosystems humans arguably con­
stitu te the m ost Im p o rta n t keystone species. I.e., a species w ith
effects on the ecosystem are greater than Its num erica l represen­
ta tio n o r biom ass (Paine, 1995). The n o tio n th a t hum ans are a
keystone species Is n o t new (M cD onnell et al., 1993) and the effects
o f hum ans on ecosystems at various tro p h ic levels are w e ll docu­
m ented (V ltousek and Mooney, 1997). However, as Indicated by
Castilla (1999), there Is an u rg en t need to Incorporate hum ans Into
ecological studies and to understand th e ir fu n ctio n a l relationships
w ith oth er organisms. O ur s tud y Indicates th a t In a d d itio n to play­
ing a keystone role, hum ans m ay o u tn u m b e r w ild predators In m ost
2386
M. M usiani et al. / Ecological M odelling 221 (2010) 2 37 4-2387
ecosystems, in clu d in g protected areas, therefore p o te n tia lly p ro ­
ducing even greater effects. Our results on the p o te n tia l effects o f
hum an presence on w o lf survival, th ro u g h Intera ction w ith other
b io tic and physical variables, co n firm e m p irica l findings obtained
In the stud y area by H e bb lew hite et al. (2005b), Percy (2003) and
W h ittin g to n et al. (2005).
Despite some lim ita tio n s, the m od eling p ro to type developed In
th is stud y fo r w olves m ay serve as a useful to o l to test hypotheses
about h u m a n -w o if Interactions, such as fo r d e te rm in in g the level o f
te rrito ry fra g m e n ta tio n o r the thresh old o f hum an use In the study
area beyond w h ic h w o lf survival could be sig n ifica n tly affected.
It m ay guide decision-m akers In designing adequate m anagem ent
strategies th a t are com patible w ith conservation o f m am m als In
N a tion al Parks and o th e r areas w ith sig nifica nt hum an presence.
Such m anagem ent strategies th a t could be Investigated Include
lim itin g the access to certain tra ils d u rin g periods p a rtic u la rly c r it­
ical to w ild life populations. Im p ro vin g the nu m be r and location o f
h ig h w a y crossing corridors to fa cilita te the m ovem ent and activ­
ities o f animals, and reducing the level o f fra g m e n ta tio n o f areas
p a rtic u la rly suitable to w ild life species.
Tw o areas o f fu rth e r developm ent are considered to refine the
actual m od eling p ro to type. Firstly, a set o f m ore sophisticated rules
could be Incorporated to b e tte r m im ic prey behavior based on
know ledge gathered fro m behavioral ecology studies. Prey behav­
iors could also be dependent upon pre da to r presence and activity,
fo llo w in g princip le s o f p re d a to r-p re y theory. Secondly, the hum an
m odule could be enriched by a representation o f m ovem ents and
activities along the roads and tra ils based on decision rules o f cog­
n itiv e hum an agents rathe r tha n a static representation o f th e ir
presence/absence.
Acknowledgements
This pro je ct w o u ld n o t have been possible w ith o u t data o r advice
received fro m M ike Glbeau, K athy Rettle, G ordon Stenhouse and
C liff W h ite , as w e ll as fro m Parks Canada and A lb erta Sustainable
Resource D evelopm ent. Special thanks also go to all personnel,
technicians and w ild life biologists w h o w o rke d In the fie ld to m o n ­
ito r w ild life and hum ans In the stud y area, and to the fie ld staff
and technicians w h o produced the geospatlal data used to charac­
terize w o lf habitat. T yler M u h ly helped In developing the resource
selection fu n ctio n m odels fo r bears and elk, and Paul Paquet In p ro ­
v id in g In fo rm a tio n fo r the developm ent o f the w o lf com ponent o f
the m odel. This research has been funded b y ACA Challenge Grants
In B iodiversity, F oothills Research In stltu te -C h lsh o lm -D o g rlb Fire
In itia tiv e grants, Sundre Forest Products LTD., Canon N ational Parks
Science Scholarship fo r the Americas, U n ive rsity o f A lb erta and
Parks Canada th ro u g h research funds allocated to Dr. M usiani, by a
NSERC D iscovery grant aw arded to Dr. Marceau, a NSERC Collabora­
tive Research and D evelopm ent gra nt co-held by D r M cD erm id, and
b y scholarships offered to Sk. M orshed A n w a r by the D epartm ent
o f Geomatics Engineering, U n ive rsity o f Calgary. W e th a n k Rick
Smee, Darren Labonte, and James R. Allen, fro m A lb erta Sustainable
Resource D evelopm ent (AB-SRD), Tom Daniels and Barry M cE lhlnney fro m Sundre Eorest Products Ltd., and Ian Pengelly and Darrel
Zell fro m Parks Canada fo r assistance. W e th a n k the Ya Ha Tlnda
ranch staff, p a rtic u la rly ranch managers Johnny and M arie N ylund
and Rick and Jean S m ith fo r logistical support. W e are also grate­
fu l to the E dito r and an anonym ous review e r fo r th e ir constructive
com m ents on the m anuscript.
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