GENERAL PATTERNSAMONG GENERALISTS: WHAT IS REVEALED BY SPATIALMODELS OF COYOTES'!

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GENERAL PATTERNSAMONG GENERALISTS:

WHAT IS REVEALED BY SPATIALMODELS OF COYOTES'!

by

©AnthonyJ. McCue

Athesis submitted tothe Schoolof GraduateStudies

in partial fulfilmentof the requirementsfor the degreeof

Master of Science Department ofBiology MemorialUniversityof ewfoundland

February2012 51.John's. NewfoundlandandLabrador.Canada

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ABSTRACT

Coloniza tio nof insularNewfo und land by coyotes(Callislutransicoinc ided with declin es in woodl and caribo u(Rangifer taranduscaribouspopu lations,genera ting public outcryto red ucecoyote predati on on this iconicspec ies.Myresear chwas focuse don the Maritim eBarr en s Ecorcgio nof ewfo und land,whic h ismore akin to anarctic habi tat thanthedesert.plain s. or for esthabitatstypica lly occup iedby coyotes.I invest igatedboth habit at associationsandspa tialsta b ilityofcoyotes in relati onto sho rt-d ista nce mig rator y caribou.Icompared efficacybetw een stat istica l andalgorithmi cspatia l mo de ls inco rpor - ating relativel y static hab itat andenviro nmenta l data for predi ctin gpattern s of use.The algo rithm ic mod elwas superior forpred ictin g future usewiththelimitedback ground data.How ever.thebestpred ict ivemodel showe dsubsta ntia lind iv idua lvariatio n. pos- sibly re flectin glocal availab ilityoffood resour ces emphas izing theneedto collec t these data.Coyote hom eran ges were relativel y staticacross seasons andyears.Overa llcoyotes appeare d to exhi bitada ptiveandoppo rtunisticbehaviourcommo n throu ghoutthe species range.

Keywords:boostedregression trees:Callis latrans: coyote: geograph ic inform ati on system:Globa l Positi on ing System: MaritimeBarr en s Ecoregion:mixe d-effec ts mod el:

Newlo und land: reso urcese lection mo de l

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ACKNOWLEDGEMENTS

Ibegan myMasterof Scienceprogramin2007 withgrand plansforuncovering truthsregarding coyotes.caribou.and theborealforest ecosystemsofinsular ewfoundland.A few heartbreaks.afunctionofunforeseencircumstances in bothfieldand laborat- ory,a nd nearly asmany subsequent researchproposals culminate in thisthesis.

My sinceregratitudegoes tothosewhoprovidedguidance andsupportfor myre- search. MikeMcGrathwasinstrumental in logisticalplanning,forimpartinghisknow- ledgein all things coyote,andespecially forhis opensharingofdatathat isthefounda- tion of thisthesis. ChristineDoucet andJohnFryxell,rnysupervisory committee members.providedthoughtful advicein researchplanningincludingthoroughreviewsof pro- posalsand thesis.Falk Hueumannenlightened meregardingthe strengthsandapplication ofmachinelearningmethods. YolandaWiersma hasbeen theconsummatesupervisorand provisioner ofsage advice relatingtothesis. graduateschoo l,andbeyond.Herwilling- nesstobe availableandsupportmyeffort swithpositivereinforcementprovidedexcellent guidance throughout.

Ialsoextendmythanksto Nikita l.aite,JohnNeville.John Reynolds.andMarie Winsafor their efforts inconductingvariousiterations of fieldwork.toRickCurranfor hismentoring andassistancewithprocessing scatsamples forthediet analysis.and to members of the LESALabformoralsupport.feedback. and knowledge sharing.Finally.I givespecial thankstomywife .JasmineMcCuc.lorhcrsteadfast belief: encouragement, andemotionalsupportfromthcdaywemet totheculminationof thisthesis.

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Thisresearchwouldnothavebeenpossiblewithout fundingsupportfromCana- dian Foundation forInnovation. NaturalSciencesandEngineering Research Councilof Canada.Leslie Harris Centreof RegionalPolicy and Development. andSafariClub Inter- national Flint Michigan Chapter.ConservationOtlicersfrom the NewfoundlandandLab- rador Department of Natural Resources generously provided exclusive use of a cabinand logistical supportin thefield.Additional resourceswere donatedbythe Newfoundland and LabradorWildlifeDivisionin theformofoff-road vehicles.helicoptertime.andac- cesstolaboratory facilities.

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Abstract.. .

TableofCon ten ts

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1.6.Re ferences. . 13

Co-authorship Stateme nt... . . 24

Chapter2.Creating Habitat ModelsforaGeneralistPredator: Approaches and Issues 25

2.1.lntrod uction... . 25

2.2. Methods .. . 29

2.3.Result s 34

2.3.I.Stoc hast ic Data Mode ls... . 34

2.4. D iscussion . 36

2.5.Re fe rences . .42

Chapter3.NearingtheEcolog icalLimit:CoyoteAdaptabilityProdu cesInd ividualistic

3.2.Methods

3.2.7. ModelEvaluation . 3.3.Results...

3.3.I.I-lomeRange O verlap...

...68

. 75

...76

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3.4.Discuss ion . 3.4. 1.Management lmplications....

3.5.Re ferences .

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. 79

. 89

. 90

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ListofTables

Table 1.1. otable terrestrial animalsofthe island of ewfoundland.L.., .,.,

Table1.1(continued) 23

Table2.1. Datausedto generate models ofcoyotespace usein theMaritimeBarrens Ecoregionoftheisland of ewfoundland.Usewasmodelled astheresponse variable with binaryinputvalues for coyote locations and randompointswithin home range.

Allother variablesarecandidate predictors... . 51

Table2.2.Candidategeneralized linearmixed-effectmodelstodescribecoyotehabitat by seasonin the centralMaritimeBarrens Eco reg ionof theisland ofNe wfo undlandj....52 Table2.3.Parameter coefficientestimates(bold)andnumberofoccurrences (below)from generalizedlinear mixed-effectmodels of coyoteresource selection during summer in

theMaritimeBarrens EcoregionofNewfoundland 53

Table2.4.Relativecontribution(%of constituenttrees) of predictorvariablesfrom boostedregressiontreemodels ofcoyote resource selection in theMaritimeBarrens

EcoregionofNewfoundland 55

Table2.5. Boostedregressiontreemodelperformance assessedvia10-fold cross-

validationofthe trainingdata... . 56

Table3.1. Individual GPScollaredcoyote ageclassrepresentation in the centralMaritime Barren Ecosystemof Newfoundlandduringthestudyperiod,2005-2009 10I Table3.2.Relative contributionof predictor variables fromboostedregressiontreemodel

of coyote resource selection in the central MaritimeBarrens Ecoregionof

ewfo und land 102

TableA1.Summaryof coyotescatscollected duringsummer2009 from GPScollar locationclustersin the MaritimeBarrens Ecoregionof theisland ofNewfoundland. 137 TableA2. Coyotescatcontents fromthe MaritimeBarrens Ecoregionofthe islandof

Newfoundlandcollected July 2009... . 138

TableBI. Candidategeneralized linearmixed-effectmodelstodescribewinter coyote habitatinthecentralMaritimeBarrens Ecoregionof theislandof Newfoundland....140

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TableB2.Candidategeneralized linear mixed-effect modelstodescribesummercoyote habitat in the central MaritimeBarrens Ecoregionoftheisland of Newfoundland....141 TableCI.Bhattacharyya's Affinity valuesfo r overlapofseasonal kerneldensity estimated

utilizationdistributionsforindividual coyotes in the centraI MaritimeBarrens

EcoregionofNewfoundland... ...142

TableC2.SummarystatisticsofBhattacharyya'sAffinityvalues measuring seasonal overlapof coyote kerneldensity estimated utilizationdistributionby individualin the central MaritimeBarrens Ecoregionof Newfoundland... . ...146

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Listof Figures

Figure2.1.Maritime Barrens Ecoregionof Newfoundland(highlighted)with the central portionrepresentingthe studyareafor thisresearch outlinedinred.The island of

ewfoundlandis highlighted in the inset mapofCa nada 57 Figure2.2.Caribou Management Areas(CMA)of the island of Newfoundland designated by the Department of EnvironmentandConservation.WildlifeDivision.Highlighted CMAs(Buchans [Bu], GaffTopsails [GT], Grey River[GR],MiddleRidge[MR].

MountPeyton[MPJand PotHill [PH]) are includedin thisresearch asrepresentative ofcaribou populationsthat overlap withGPSmonitored coyotes... ...58 Figure2.3. Aerialviewof forest and barrenstypicalof the central MaritimeBarrens

Ecoregiono f Newfoundland.Forestedareasaregenerallyrestrictedto steep-sided valleys(a) and protected slopes (d).Barrens(b.c)make up themajority ofthe study area composed ofheathlandsand peatlandsinterspersedwithwaterbodies ofvari ous

sizes... . 59

Figure 2.4.Spatialpredictions ofg eneralized linearmixed-effectmodelsfor coyotes in thecentralMaritime Barrens Ecoregionof Newfoundland.Differencesbetweenwinter (a) andsummer(b) projections areeasily visiblewith the effectofroadsand water incorporatedin the winter model.Blue and red colours representpredictedareas of low

and high coyote use,respectively... . 60

Figure2.5.Spatial predictions of boostedregressiontree (BRT) models forcoyotes inthe central MaritimeBarrens Ecoregionof ewfoundland.Only themodel foradult femalesduring summer isshown(a)among theprojections for the original model containing all 14 predictors. Thesimplified BRTmodelisshown for adultcoyotes(b).

Other age. sexandseason proj ections showonly minordeviationsnot easily differentiated at this scale ingraphical format.Blue and red colours representpredicted

areasoflowand high coyote use.respectively . 61

Figure 2.6.Relative operatingcharacteristic(ROC) plotsfor predictionto an independent evaluation datasetfrom generalized linearmixed-effectmodels of coyote resource selectionwithin thecentralMaritime Barrens Ecoregionof Newfoundland.Area under theROC curve(AUC) valuesprovide of measure of reliabilitylormodelpredictions undervariousc onditions(i.e..allcoyotesp ooled.coyotesfromthelrainingdata ina differenttimeperiod.and newcoyotes inadifferenttime period) foreachseasonal modelandpooledpredictionsfrom both seasonalmodcls.... . 62

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Figure 2.7. Relative operatingcharacteristic(ROC) plotsforpredictionto anindependent evaluation datasetfrom aboostedregressiontreesmodel of coyote resource selection within thecentral MaritimeBarrens EcoregionofNewfoundland.Area undertheROC curve(AUC) values provide of measure of reliabilityfor model predictions under various conditions (i.e.,allcoyotespooled, coyotes from the trainingdatainadifferent timeperiodandnewc oyotesinadifferenttimeper iod) foreachseasonand pooled

predictionsfromboth seasons 63

Figure 3.1. MaritimeBarrens Ecoreg ionofNewfo undland(highlighted) withthecentral portionrepresentingthestudyarea lorthisresearch outlined in red.The island of Newfoundland ishighlightedin the insetmap of Canada 103 Figure 3.2.Caribou ManagementAreas(CMA) ofthe island ofNewfoundland designated bytheDepartment ofEnvironmentandConservation, WildlifeDivision.Highl ighted CMAs (Buchans[Bu], GaffTopsails[GT],GreyRiver [GR],MiddleRidge[MR], MountPeyton[MPj and PotHill [PHI)are included in thisresearchasrepresentative of cariboupopulationsthatoverlap withGPSmonitored coyotes 104 Figure 3.3.Aerial view of fore stand barrenstypical of the central MaritimeBarrens

EcoregionofNewfoundland.Forested areasaregenerally restrictedto steep-sided valleys(a)and protected slopes(d).Barrens(b.c)makeup themajority ofthe study areacomposedof heathlands and peatlandsinterspersedwithwater bodies of various

sizes... . 105

Figure3.4.Part ialdependence plotfor distancetonearest road aspredictor of coyote space usein the central MaritimeBarrens Ecoregionof ewfoundland.Distanceto nearestroadwasincluded by theregression tree algorithm in 19.9%of 6500 trees in themodel. Tick marks at top of plot arearepresentdeciles ofd ata 106 Figure3.5. Part ialdependenceplot forelevationaspredictor of coyotespace usein the

central MaritimeBarrens Ecoregionof Newfoundland.Elevation wasincludedby the regressiontree algorithm in 10.0% of6500treesin themodel. Tick marks at top ofplot area represent deci leso f data... ... ... 107 Figure3.6.Partialdependenceplotlordistancetonearestbody ofwater2:Iha as

predictor ofcoyote space usein thecentralMarit imeBarrens Ecoregionof Newfoundland. Distancetonearestbody ofwa ter2:Ihawasincludedbythe regressiontree algorithm in6.0%of6500 treesin themodel.Tickmarks at top ofplot

arca rcprescntdccilcs of data.... . 108

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Figure 3.7.Partial dependen ceplotfor Earth Obser vati onof theSusta ina ble Devel opm ent ofFores ts(EO SD) landco ver classaspredi ctor of coyot e space use in thecentra l Maritim eBarren s Ecoreg ionofNewfo und land.EOS D land cove r classwasincluded bytheregressiontreealgorithmin6.0%of650 0 trees inthe mo dc l.. 109 Figur e 3.8.Partialdepend enceplot for distancetonear estbod y of water~5 haas

predict or of coyote space usein thecentra l Maritim eBarren s Eco reg ionof ewfoundland.Distanc etonearestbod y of water~5 ha wasinclud edbythe regre ssiontreealgo rithm in 5.7% of6500 treesin themod el. Tick marksat top of plot

area repre sentdeciles of data .... . 110

Figure3.9.Partialdependenceplotfor slope aspredictor of coyotespace usein the ce ntra l Maritime Barren s Eco reg ionof Newfoundland.Slopewasincluded bythe regre ssiontree algorithmin 4.0% of 6500 treesin the model.Tickmarksat top of plot arearepresentdeciles ofdata... ...III Figure3.10.Partial depend enceplotforaspectofslope relativetonorth aspredictorof

coyotespace usein the central MaritimeBarrens Eco reg ionofNew found land. Aspect ofslope wasincludedby theregr essiontreealgo rithm in3.9%of 6500 treesin the model.Tickmarks at top of plotarearepres ent dec ilesof data 112 Figure3.11. Partialdependenceplotfordistanc etonear est surfae e wateraspredi ctor of

coyote space usein thecentralMaritimeBarren s Eco reg ionofNew found land . Distan cetonearest surface waterwas incl uded bythe regressiontree algo rithmin 3.4%of6500 treesin the mod el.Tickmark s at top of plot areareprese nt decil es of

data 113

Figure3.12. Partialdependenceplot for top ograph ic converge nce index aspredict or of coyotespac e use in thecent ralMaritimeBarren s Ecoregio nof ew found land.

Topog ra ph ic conv er gen ce index wasincluded bytheregressiontreealgorithmin3.0%

of6500 treesin the mod el. Tick mark s at top of plot area repr esentdecil es o fd ata...114 Figure 3. 13. Relati ve opera tingcharacteristic (ROC)plotsforpredi cti onto an

independent eva luation datasetfr om aboostedregressiontreesmodel of coyote resource se lec tion within the centra l Maritim eBarrens Ecoreg ionofNew found land.

Areaunder the(ROC) curve (AUC) values provide of measure of reliabilityformod el predictionsundervariou sconditi on s(i.e.. all coy ote spool ed•coyo tes fromthetraining datain a different time period.and new coyote sin a different time period)for each seasona l model and pooledprediction sfrom both seaso na l model s 115

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Figure3.14.CutolT p lotshow ing therangeofmod ell ed respo nsevalues fordiffe renti at ing used and non-u sed geogra phic locat ion s fromaboosted regress ion tree (BRT) mod el of resourceselectio nby coyotes inthecentra l Marit imeBar ren s Ecoreg ionof

ewfound land... . 116

Figure3.15.Spatia l repr esent at ion s of predi cted fema lecoyote usedurin g summe r(a) and winter (b) from aboostedregr ession trees modelforthe centra l Marit imeBarren s Ecoregionof ewfo und land.Blue andredcolours repr esentpredi cted areasof low and

high coyote use.respect ivel y... . 117

Figur e 3.15(co ntinued).Spatial repr esent ati on s ofpredi ctedmale coyote useduring sum mer(c) and winter(d) from aboostedregressiontreesmod elforthe centra l Maritim eBarrens Ecor eg ionofNew found land.Blueand red colours represent predictedareas of lowand high coyote use.resp ecti vel y 118 Figure A I.Location s ofcoyoteGPS locati onclusterswhere scats were collected ("')

duringJuly 2009 in the Maritim eBar ren s Eco reg ionofNe wfound land.The backgroundrepresent s spatial predi ction s of relativ euselor ad u lt fem alecoyot es during sum me r froma boostedregr essiontreemodel(Ch apt er 3). Blue andredcolours representpredi cted areasoflow and high coyote use,resp ecti vel y 139

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List of Abbreviations andSym bols AIC-Akaike's information criterion

AUC-area underthe curve BRT - boostedregression trees CMA-caribou management area

!'!.i-AIC differencesrelativetothe smallestAICvalueamongcandidate models DEM -digital elevation model

DOP-dilution ofprecision

EOSD- Earth Observationfor Sustainable Development of Forests GIS- geographicinformationsystem

GLMM-generalized linearmixed-effectsmodel GPS- Global Positioning System K-number of estimated parameters inthemodel KDE -kernel densityestimation

£.(g,l.\")-discretelikelihood ofmodelg"given the datax MBE-MaritimeBarrens Ecoregion

ROC-relative operatingcharacteristic RSF - resource selectionfunction RSM-resource selectionm odel SDM-s pecies distribution model TCI-topographicco nvergence index

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UD- litilization d istriblition

lI'i-Akaikeweights.ameasure of probabilit ythat modeliisthe bestmodelcon sidered

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Listof Appendices

AppendixA.Summer Dietof Coyotesinthe Barrens of Newfound land 127 A.I.l ntroduction... . 127

A.2.Methods 128

A.3.Resultsand Discussiol1 131

AA.References 133

Appendix B.Genera lized LinearMixed-effectModels... . 140 AppendixC.Bhattacharyya'sAffi nityMeasures ofIndividualSpatialOverlapAcro ss

Seasons... . 142

C.I.References... . 146

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CHAPTERI.INT RO DUCT ION AND OVERVIEW 1.1. Eastern Coyote

Coyotes(Callislatransv arepossiblythe most thoro ugh lystud iedcarn ivo resin NorthAme rica(Voigtand Berg19 87:Bekoff andGese 2003). Whilemuch of th isre- search hasbeen conducte d inweste rn desert .mount ain , and plain shabit at,therehas been substantial resear ch follow ingcoyote rangeexpan sion eastwar dacrossthe contine nL Itis we ll docum ent edthatthe coyote nichediffersbothineastern popul ati on s (Parke r 1995 ; Gom ppe r2002)and in theabsen ce ofwolves (Bekoffand Gese 2003; Ber ger andGese 200 7). Most of theresearch in eastern coyoterange has occ ur red inareaswher ewol ves areinlowden sit y orabse nt.asisthe case for myresearchinins ularNewfo und land.East- erncoyotesarecons ide red distin ct fromwestern popul at ion s inbothgenetic mak e-up , wh ich has ledto increase d body size andpossibl y the abilitytohunt largerprey (Kayset al,20I0), andecolog ica lroleas a preda to r/scavengerof larger mamma ls(Harriso n 1992). Althoug heasterncoyotes typicallyrepresentthelargest caninepreda to ronthe lan dscap e.theyarenot thefunctio na lecolog ica leq uiva lentof wolves(Crete£'1al.2001).

Easterncoyotes do no tshowconsiste nt prefe rencefor partic ular ha bitats . butanthro po - gen ic land scap estendto bemor eprodu cti ve andoccup iedingrea ter den sit ythan for ested areas (Ray 2000:Gomppe r2002) .Commun ity- leve leffec ts foll ow ing coyote colon iza- tion can befar-r eachin g (Gompp cr 20 02).

Thc coyot csinh nbilin gn orth enstcrnN orth Amcri ca nrea gencti call ydi stin clpop- ulati on segm entd cscendcd from d isperse rs lhal im m igraled no rtho f lhcG rea tLa kes

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from western portions ofthe continentoverthe courseofthepast century(Kayset al, 2010). Parker (1995)outlined the colonization historyofthespeciesthroughthe mid- westernstatesandOntario.andinto ew Englandand the Maritime provinces. Thefinal maj orhurdle in the eastwardcolonizationwas clearedin themid-1980s whenthe first coyotesreached theisland ofNewfoundland. purportedly crossingCabotStraitoversea ice (Mooreand Parker1992).Thefirst confirmationof breeding successon theisland was ajuvenilecoyotekilledby avehiclenearDeerLake in 1987. Northwardcolonization of the continentcontinues (Chubbs andPhillips 2002.2005:C lulT2006). thoughata slowerrate. likclydu etothe continued presence of wolvcs and lessint ensive anthropo- genic landscape change north ofallcurrentcoyote range (Mooreand Parker1992).

Inaddition tothis ecological distinction, easterncoyotesseem tobemorerna- Iignedthan theirwesterncounterparts.This publicperception andfear may beacon- sequence havingto dealwithalargelyunknown predatoron thelandscape (e.g., Kellert 1985;Linnell eral,2003; AndersoneandOzolins 2004:Roskaft etal.2007). Researchers throughoutthe easterncoyoterange have commentedonpublicfearsand hatred follow- ings uccessfulcolonization(e.g.,Hilton1992: 1\Iooreand Parker1992: Stevensetal.

1994:Parker1995:Ray 2000:Gompper2 002).Sutherland(2010) focused specificallyon thisissue ininsularNewfoundlandand discoveredpublicperceptions andemotionssim- ilartoth ose experienccdcl scwhere. Specifically.indi vidualsinher survey indicatcdth at ncgativc feclingsm aybe ar csult ofl ack offamiliaritywithcoyotcs comparedt o othcr predatorsthat havebeen on thelandscape since pre-colonialtimes.Attitudes toward

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coyotes in ewfoundland alsofolloweddemograph ic andexperiential parameters in the sameway as elsewhereacross orthAmerica,suchasmorenegative attitudesamong olderand moreruralpeople along withthoseunfamiliarwith the speciesin their area (Kellert 1985).Researchindicatesthat education programsmay improveattitudes for co- existence withcoyotes(Stevensetal.1994; Baker andTimm 1998; Fox 2006), aneces- sity for successful management of a species that isrelativelyimmunetopopulation con- trolmeasures(V oigtandBerg1987;Parker1995).

1.2. NaturalHistoryof Newfound land

Myresearchfocuses onspatialaspectsofcoyoteecology inalandscapethat ap- pearsdramaticallydilferentfrom everywhere else coyotesh avebeen studied.The Mari- time Barrens Ecoregion(MBE)ofNewfoundland(Figure2. 1) ismore akin to anarctic habitatthanthedesert.plains. or forest habitats typicallyoccupied by coyotes(Figure 2.3).In addition tothelandscape, coyotes in theMBE interact withaunique assemblage ofprey.

The terrestrial ecosystemsofthe islandof ewfoundland. historically character- izedbydepauperatenative fauna, have undergone numerous redefi ningevents.This nat- iveassemblagewasheavilyunbalanced with7 carnivores.3 rodents,llagomorph.andI ungulaterepresentingthe entiresuiteofquadrupedalmammals (Bangs 1913).ln the periodfollowing Europeansettlement,numerousmodili cations ofth enatural system have occurred both accidentallyand intentionally (fable 1.1).Fires haveresultedin drastic changes tothe landscape,mostnotablythedevelopment ofthe entire Maritime

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Barrens Ecoregionas aprimarilynon-forestedlandscape (Meades 1983). Faunalchanges include extinctionof the endemicNewfoundlandwolf(Canislupus beothuc us:Allenand Barbour1937) following decades of bounty.and introductions 01'2 galliformbirds(Tuck 1968).7 rodents. I lagomorph. and I ungulate.Allofthese ecosystemandcommunity changes mayhave substantial importance for thenaturally colonized population of coyotes.

Manyfar-reachingdirectand indirect impactsofthese ecosystemchanges have been observedor hypothesized.Introduction of thesnowshoe hare (Lepus ame ricanusi hasbeenimplied asthe indirectcause ofrangerestriction and populationdeclines of the Arctic hare (Lep usarctic us;Bangs1913:Bergerud1967). Similarly it has been hypothesized that red-backedvole('\~I'odesgappe ri)introductionswilleventuallycause rangere- strictionand reducedpopulations ofthe endemic meadow vole (J/icroilispennsylvanicus lerraellome: Hearne lal.2006) .lncreasesi n theacci dentally introd uced mink(M lislela l'i,I'IJII)populationshave beenconsideredthe likely cause of observeddeclinesin muskrat (Ondatra zibethicusobscurus)populations (Soperand Payne1997).Possible extinction of theendemicNewfoundlandcrossbill(Loxiacurvirostrape rc na ]hasbccn attributedto establishmentoftheredsquirrel(Tamiasci nrus hudsoni cus :Benkman el al.2008).The woodland caribou is among thelatest species thatmaybcsuffer ingadverseeffec tsof faunalchanges.

Insular caribou herds of Newfoundlandhavebeenintensivcly studieddurin gth e past 60years due toimportance as a culturalandeconomicresource.Woodland caribou

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arethe only ungulate speciesnativeto ewfoundland(Bangs1913).Unrnonitored hunt- ing of the populationled toprecipitous declines in the early20thcentury limitingtotal numbersto1000-2000 (Bergerud 1971). Thiswas followedby a periodofrestriction on harvestand moredirectedmanagement leadingto steadygrowthofherds (Bergerud 1971) andeventualexponentialgrowth(MahoneyandSchaefer2002a)resuiting ina populationpeak of approximately96000 in 1996 (NLDEC2009a).Since thattime caribou havec ntereda nothcr periodo f prccipitous decline with ac urrcnt population estimate 01'32 000 (NLDEC 2009a). Duringthisdecline,behavioural changes havebeen observed includingmoredispersed calving(NLDEC2009b)andchanges incoreareasof use (StantecConsulting Ltd.20 11).Numerous hypotheseshave beensuggestedastothe causalfactorsin therccen t dcclinc,including density-dependent nutritional limitation (MahoneyandSchaefer2002a).anthropogenicdisturbance leadingtohabitatloss (Chubbset al.1993;MahoneyandSchaefer2002b;McCarthyet al.20II),andpredation by endemic lynx(Lynxcanadensis subsolanus:Bergerud197\).endemicblack bear tUrsusamericanus hamiltoni;Mahoney andVirgl2003), andrecentlycolonizedcoyote (NLDEC2008).ThomasandGray(2002)indicatethat interplayamongthesefactorsmay make itdiffi cult to identifythefactors rcgulating caribou populations.

1.3. Coyotes inNcwfnu ud lnnd

Coyote predationmaybe contributing to caribou populationdeclineswithvarying levels of impactin rime andspace.It hasbeen suggested that. followingcolonization in Quebec,coyotes have contributed toincreasedcaribou calfmo rtalityandconsequent

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populationdeclines (Creteand Desrosiers1995).However.predationmaybe aproximate ratherthan ultimate cause fordeclineincaribou numbers.mediated by habitatc hangea nd alternate prey species(Festa-Blanchetetal.20 II). Sincecoyotes first arrivedinNew- foundland,ca.1985.thepopulationhas rapidly increased andexpanded acrosstheisland (McGrathetal.20 10).Increased coyoteobservations(McGrath2004)and harvest (Mc- Grathet al.20 10)coincided with cariboudeclines,but this correlation isnotsufficient evidence to constitutecausality.Although it islikelythat coyotesare playing a significant role in cariboumortality,the determination of proximateversusultimatefactorsislikely to be lessclearandofgreat importancein the longtermmanagement of the ecosystem.

Determination of which factorsaffect coyote temporaluse ofspace withinthis system should providefurtherinsighttothemechanismsunderlying associated trophic interactions.

The potentialprey componentforcoyotesin theMBE is composedof seasonally migratory caribou. moose (AIces umricanus;as carrion).beaver(Castorcunadensis), muskrat. snowshoe hare. grouse. ptarmigan iLagopusspp.),redsquirrel.andvoles. Inter- specificcompetition in thislandscape islimited.Bears and redfoxesrepresentthe primarymammalian competition. Lynx arealso present.but typically occurat lower density in this open landscapecomparedtoforestedregionstothenorth(M..!.,\k Grath, per s onal conunnnicationv.Additionalecological knowledgein theform ofspatial dynam- ics shouldp rovidevalu ablein sightt othi srelat ively simplifiedpred ator-prey system.

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Inamulti-p rey system,ada ptivepreda tio ncan have dra ma tic populat ionlevel e f- fects(Owe n-S mithand Mills 2008).Th is islargel y a resu ltofprey switc hing due to cha nges in relati ve vulne ra bilitywithcha nges inenv iro nme nta lcondi tionsand prey demogra phics.Ina sim ple wolf- elk-bi son syste m in Yell ow ston e Nationa l Park . prey abundance .size,de fen si vebehav iour , seasona lvulnera b ility,and pred atorprefe ren ce all played roles insw itch ing behaviour ofwolv es (Garrottet al.2007).Owe n-S m ith and Mills (2008 ) concludethatthehigh erthed iversit y ofprey.th ehardertotease apart fact orsaffecting preydemograph icrespon setopredati on. Th issugg es ts that identifyin g fact orsprom otin gprey sw itch ingin MBEcoyote swouldbe extre me lydifficult give n the rangeofprey sizesand theircontrast ingecology.Oneapproac h tobeginthisprocess of investigat ion isthrou ghidentification of spat ial pattern s.Hom eran gehas beenshownto re flec tvariabilityof reso urceswithinanan ima l'sterr itory. butothe rfacto rswork to con- foundthis relat ion sh ip (Borge retal,2006). Add itiona lly.indiv idua lseachselectfroma differe ntsetofopt ionsgivenvariatio nacross the landscape. partic u larly when territo ria l- ity ex ists.

1.4. Modelling SpaceUsc

Inecology.we strive to exp lain processesthrou gh various mean s across a con- tinuu m of com plex ity.Manyof these ana lysesofecolog ica lstudyare conducted within eac h investi gator 'srealm of knowl ed ge and com fort (Elliso nand Denni s 20 10) .Oneof thechallen gestomodern ecolog istsis ada ptingapproac hes tousethebestavailabl e method s allo w ing fo r a greater depth ofscienti tic enq uiry.Thiswilloften requ ire re-

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searchers topush their personallimitation into newrealms of statisticalandtheoretical knowledgeto enhance theunderstanding of systemsandallowecology toprogressbey- ondthe basicquestionsthathavedominated ecologicaljournalsand manuscriptslor the entire history ofthediscipline.

Theconceptof delineating spatial parametersthat correspond towildlife behaviour isbynomeansnew. Forcenturies, naturalhistorians and biologistshave endeav- oured tounderstand space usebyanimals(Burt 1943).Refinements of the conceptsof homerangeand territorytoincludevarious stagesofl ife history and temporal scale have advancedour understanding ofanimal behaviourfromtheindividual and populationper- spectives.ln recentdecades, advances in technology (i.e., radio-andsatellite-telemetry, satellite imagery)havedramaticallyincreasedthetemporal andspatialresolutionandex- tent ofdata available to scientistsand ledto a proliferationof new techniquesformodel- ling animalspace usc.

Technologicaladvancesallowusto applyspatialtheoryto research questionscon- cerning ecology ofwide-ranging carnivores(YoungandShivik2006). Basicuse-availab- ility models were enhancedby considerationofthe effectsof spatialscale(Johnson 1980). Geographicinformationsystems(GIS)coupledwith remotely-sensed datafrom satellite images greatlyexpanded the scopeof backgrounddata availablefor building spatial models.Individual-based spatial models were lurtheradvancedwith theresource selectionfunction(RSF) typicallyimplementedas a generalized linearmodel (Boyceand McDonald1999;Manly etal.2002).BytheturnofthecenturyGlobal l'ositioningSys-

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tern(GPS)collarswere becomingmore prevalentas ameans ofcollecting highfrequency locationdata and bringingtheissueofspatial autocorrelationtotheforefront(Otisand White 1999;Rodgers 200I). Atthis same timenewapproachesbased on machine learn- ingalgorithmswereenteringthefieldofhabitatmodelling (Guisan andZimmermann 2000; SCOIIet01.2002).Despitethis,theRSFapproach tomodellinghaspersistedfor manyyearsastheprimarytoolformodellinghabitat associations.Modificationstothe RSFhave evolved toimprove our knowledgeof systems basedon remotelycollected data.Most of thismodel evolution hasfocused onserialautocorrelation associatedwith high-frequencydata forasmallsampleofindividuals.Generalizedadditivemodels,gen- eralizedestimatinge quations. generalized linear mixed-effect models,andgeneralized additivemixed-effectmodelshaveallbeenappliedandadvocatedfor modellingresource selection inthepast decade(e.g., Gillieset01.2006;Guisanet01.2006;Aartset01.2008;

Koperand Manseau2009).

Alternative methodsto statisticaldatamodels-known as algorithmic modelling.

data mining, or machinelearning-are rapidlyincreasingwithadvancesincomputing technology (seeHastieet01.2009).Thelise of machinelearningtechniquesfor species distributionmodelling continuestobepromoted (e.g.. Elithet01.2006;Hochachkaet01.

2007; Marmionet01.2009; Drewet01.20II).butis yetto enterthe mainstreamof resourceselection modelling.Machinelearning oftenoutperforms traditionalstatisticalap- proachesinidentifyingpatternsin biogeographicalspace (Cushmanctal.2007). Ma- chine learning approaches areespeciallysuited to situationswherethedata donotneces-

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sari ly representmech ani sm s generating theobserved patt ern s.Spec ificstre ngthsof ma - chine learnin gincludenoa priori assumptions regardin grelati on shipbetwe enresp on se and predictorvariable s.vari able se lection isbuilt intothe algo rithms. non-linear and hier- archi calstructureare easil ymodelled. and high- orderinteraction s canbe included(Cra ig and Huettmann 2009).Mod elinterpretati onis gene rallyvery ditlicult with man ymachin e Icarnin gimplcmentation s.butexception sdo ex ist .Forexample .boostedregr essiontrees providemodel outputthati seasytovi sualize,similartotraditionallinear appro ache s (Elithet al.2008).A disadvantage of machinelearningmodel sisthe lackof mechanisti c tiebetweenpredictorand respons evariables (Cu shm anet al.2007). While theserule- basedalgorithm s exce lat findingpatternsin dataand prcdictingthrou ghoutparamct cr space, there isno link to explain ing the underlyin gprocc ss. The re fore thevaluemaybe in identifyin gthreshold s and target sfor add itio na lexploratio n(I-Ioc hac hkaet a l.200 7).

Resour ce se lec tion mod elstyp icall yrequire someassessme ntoftheback g round environmentin whichindividu alsare makingbeha vioural cho icesof select ingamo ng avai lableoptions . Large GIS data setsallow for ease of sam p ling back grounddata and hencethe use of pseud o-ab sen cedata (i.e..arando msam pleof point srepr esentin gthe ava ilab le environmental cond itions ) to inco rpo ratein presen ce-ab sen cemod els. The reis avas t literaturedealin gwith potenti alissue s ofcontam ination in pseud o- ab senc edata as wellasalternativcapproa cheswhen reliableabsencedata arcnot available (c .g.. Keatin g andCherry 2004:Pearceand Boycc 2006 ;Phillips etal. 2009 ). Failureto ade quate lydea l withthe cont aminati onissuecan lead tobiasedparameter estima tesin rcsour cc scl ccti on

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functions.Therefore. evaluatingoutputsisimperativewhenusingthemodelforpredict- ivepurposes (Rykiel 1996: GuisanandZimmermann2000).This procedureprovides some level of credibility regardingmodel accuracyaswell asa measure ofcomparison amongcandidate models.particularlywhen an independent test set isusedfor evaluation (Araujoand Guisan 2006).

1.5. ThcsisOvcrvicw

The MBEisa uniquesystem within the coyote's current range.Coyotes in the MBE exhibit the largesthomeranges amongall populations studied in North America (Blake2006)retlectingthelownet primary productivity of theecoregion (Liuet al, 2002).The MBEhasundergonerapidchange followingthe arrivalof coyotes.mostnot- ably the reductionincaribouabundance.Researchhas shown thatc oyotesare contribut- ingtothe highmortalityrateof caribou calves incentralNewfoundland(Blake2006:

Trindadee /al.2 0 11).Ano ngoing diets llldy hasa lsos hown a high proportionof ungu- latebiomass (i.e..moose andcaribou) is consumed by coyotes duringthewinter (Me- Grathe/al.20 10).T his baseline information supports theideathat coyoteforagingef- fortsmaybe focused onabundant migratorycaribouwhenthey areat highestdensityin theMBEduringthe winter season.The dramatic reductionincaribou populationnumbers andchanges incalving distribution (NLDEC 2009b) may somewhat rellect the establish- rnentofcoyo tes, but continuedpredationpressureisthegreatestconcern formyresearch.

Additional over-winter mortality or reducedfitnessdue toprcdation risk couldsignific- antly impact thc already stressedcaribou population. Clarifyingthc ccologicalniche of

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coyotesintheMBEwill provideinsighttothe dynamicprocesses amongpredatorand preyspec ies.

CoyotesintheMBEwereinstrumented withGPS collarsand trackedfrom 2005- 2009bythe NewfoundlandandLabrador WildlifeDivision.Iusedthese GPS datatode- velop resource selection modelsforcoyotesbasedonavailableGIS data that characterize environmentand habitatacrossthe entireecoregion. I thentested the predictiveaccuracy ofboth a traditionalstatisticalapproachandamachinelearningapproachasameans of determinin g(I)whether one approachis superior withthelimitedavailable background data,(2)the valueofindirectmeasuresin modellingageneralist,and(3)thebestmodel for furtheranalyses(Chapter2).Additionally,Iinvestigatedseasonalandannualshifts in individualhome rangeutilizationinanattempt toidentifyanypatternsinshifting prey focusbased onover-winter presence ofmigratorycaribou(Chapter3).Iinterpretedthe bestpredictivemodel(fromChapter 2) and usedthismodelto generate predictivedistri- butionmapsfor coyotesacrossthecentral portion oftheMBE (Chapter3).Finally,Iat- temptedtofillanotherknowledge gap withapreliminary assessmentof coyotesummer diet (AppendixA) as apotential pathforwardfor coyote researchintomechanisms ofob- servedspatialpatterns.

Thisthesis continuestobuilduponresearchdirectedtowardunderstandingthe ecological nicheofcoyotesin thisnovelinsular landscape. Spccifica lly.myrcscarch adds aspatial component to the understanding ofcoyoteeco logy and how thisfitswith implications thatcoyotes are responsible forthedeclineincaribou populations.Italsoidentifies

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analternate approachtoresource selection modell ingand themerits of datamining given limited ecological knowledge or data, concepts broadly applicablet oecologicalresearch.

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StevensTH. More TA.Glass RJ.1994.Public altitudesaboutcoyotesinNewEngland.

Society & atural Resources 7(1):57-66.

Sutherland M.20 10.1-luman dimensionsofb lack bears.car iboua ndcoyo teso nthe island portion of Newfoundlandand Labrador[Master'sthesis].SI. John's (NL):Memorial UniversityofNewfoundland.

Thomas DC. Gray DR.2002.UpdateCOSEWICstatus report on thewoodland caribou Rang ifertaranduscari bo uinCanada.Ottawa (ON):Committeeon the Statusof Endangered WildlifeinCanada.

Trindade M. Norman F.LewisK.MahoneySP. Weir J.SoulliereC. 20II.Cariboucalf mortality study:asummary andanalysisof thepatterns andcausesofcariboucalf mortalityin Newfoundlandduring aperiod of rapid populationdecline, 2003-2007.

[SI.John's (NL)]:NewfoundlandandLabrador Department ofEnvironment and Conservation,Sustainable Development andStrategicScience Branch.Technical Bulletin No.2.

Tuck LM. 1968.Recent ewfoundlandbirdrecords.TheAuk85(2):304-3 11.

TuckerBJ. BissonelteJA.BrazilJF.1988.Deermouse. Peromyscusmaniculatus.in insular Newfoundland.Canadian Field- aturalist I02(4):722-723.

VoigtDR. Berg WE. 1987.Coyote. In: ovakM,Baker JA. Obbard ME. Malloch B.

editors.Wildfurbearer management and conservationin orthAmerica.Toronto (ON):OntarioMinistryof Natural Resources. p. 344-357.

WarkentinIG. ewto nS.2009.Birds of Newfoundlandfieldguide.Portugal Cove-St.

Phillip's (NL):BoulderPublications.

WhitakerJO. 2004.Sorescinereus.Mammalian Species(743):1-9.

WilsonDE,ReederDM,editors.2005. Mammal speciesof the world:ataxonomic and geographic reference.3rded.Baltimore (MD): JohnsHopkins University Press.

Young.IK. Shivik .IA.2006.What carnivorebiologists can learn from bugs.birds.and beavers: areview ofspatial theories.Canadian Journal ofZoology 84(12):1703-1711.

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Co-a ut ho rs hip Statement

Thefollowing twomanuscript chapters ofthisthesiswereco-authoredbyMichael J. McGrathand Yolanda F.Wiersma.Iwasthe principal contributortoprojectdesign.

proposaLd ata analyses.andm anuscript preparationforallchapters ofthethesis.Mr.Me- Grathcontributed design andimplementationof coyote locationdatacollection.ecolo-

gical kn?wledgeofcoyotesinNewfoundland,logisticalsupport.andconstructive feed- backregardingprojectdesignand manuscriptdrafts.Dr.Wiersmaprovided criticalsup- portin the form ofresearch guidance throughout the processfrominitiald esignthrough manuscriptcompletion.Thisincludesconstructive feedbackatall levels of theworkand manuscriptwritingprocess.

Both Chapters2and3 are writtenfor submission tojournalswithonly minor modification. Thus. itwasnecessary torepeatsomeinformationfromChapter Iaswell asstudyareadescriptionsanddataco llection procedures betweenC hapters2 and3.lalso anticipatesubmitting the materialinAppendixAforpublicationwithco-authorcontribu- tionsfromMichael J.McGrathandRickCurran.

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CHAPTER 2. CREATING HABITAT MODELS FOR A GE ERALIST PREDATOR:APPROACHES AND ISSUES

2.1. Introduction

Species distributionmodels (SDMs)are widely usedinecology,bothfordevelop- ment oftheoretical frameworksandapplication to conservation problems.Despite the plethora ofmodelsthat havebeendeveloped, there remains a relatively limitedsuiteof analytical methodsusedto construct themajority ofthese models.Researchershave notedthat ecologya safield may lagbehind other areas ofscientific endeavourbecause wc failtoappl ym orep owcrfulandmore appropriatet cchniquesthatremain outsideof ourcomfortzone(O'Connor2002; Hochachka et al.2007).Thissituation isnotlimitedto ecologyalone; Breiman (200 I) challenged the statisticalcommunity to expand their knowledge and practicebyincludingthetechniques ofmachinelearningin the suiteof tools for data analysis.citingvariousscenarioswherestochastic datamodelsfailedto meetthe capabilitiesof algorithmic models.

Resource selection models (RSMs),thesubsetofSDMstypically developed with repeatedobservationsfromalimitednumberof individuals. remainfirmly entrenchedin thelinear stochastic datamodel approach{Hegeletal .2010). Manly el al. (2002)pro- motedlogisticregression as a resourceselectionfunction. whichhasbecomethenormfor identifyingspa tio-tcmporalassoc iationsofanimals in their cnvironmcnt.Modellersla - cing budgetand timc constraintsareoftenchallenged tomeet assumptions of thisap- proach.Th e combination ofin crcasedlocationfrequency available with Global Position-

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ing System(GPS) trackingtechnology, opportunistic fielddata collectionandcommon practiceof usingreadily available datasets(e.g.,satellite image, forestinventory,andto- pographicdata) exacerbate theseissueswhenmodelling speciesresponse to theircnvir-

Advances havebeenmadein themoretraditional approach toresource selection modellingbyincorporatinghierarchical structure toaccountfor randomeffects.These advances in RSM structureh elpt o address somem ajor issues, such as correlation. unbal- ancedsamples,and unaccounted variability(Gillieset al.2006;Cressieetal.2009;

Fiebergetal.20 I0).Additionally, numerous researchers have shown how explicitlyac- countingforrandom effectsinstochasticdata RSMs canenhance the explanatory power ofthesemodels (e.g.,Gillieset al.2006;Hebblewhite and Merrill 2008;Godviketal.

2009).However, the mixed-effectsmodelling approach does come with its ownassump- tions,namelydistribution oftherandom effec ts,that can be botha strength (e.g.,predict- ingt onew situations)and weakness (e.g.,noindividual exhibits themeanresponse;

Ficbergetal,2009;O'Hara2009).

Stochastic datamodelsinecologytypicallyfocusonidentifyingexplainedvari- ance inafunctional form.This procedure requiresclearly definedhypotheses of the rela- tionshipbetween variables.When thegoalofmodellingisfocusedonusingthe best available datato predictscenariosbeyond the original data withoutinferringproccssor functionalassociation.other methods,particularly algorithm-based approaches(a.k.a.

data mining,machinelcarning), may bem ore appropriatc(l-!ochachka el al. 2007). Th e

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design of machinelearningalgorithmsis such thatthegoalof theresulting modelispre- dictionratherthan explanation(De'ath2007:Hochachka etal, 1007:Hastic etal.1009) . This isa fuzzydistinctionfrom amixed-effect stochastic datamodel.which has a similar predictiveattributeinherent in the random-effect structure. butis oneth atm ayh aveprac- tical implications.

The limitations of linearapproachesforconstructing SDMs havebeenhighlighted in recentyears, but thishasprimarilytranspiredin theareaofspeciesoccurrence modelling.Maximum entropy,artificial neural networks, genetic algorithms.decisiontrees,and supportvectormachineshave all beenshown toimprovepredictiveperformancewhen compared tologisticregressionandother forms ofstochastic data modelswhenapplied to occurrence-basedSDMs(Elithetal.1006:Cutleret al, 1007:De'ath 1007).The greatest predictiveperformancehasbeenconsistentlyachieved with ensemblelearning methods (i.e., bagging,boosting,randomforests: CaruanaandNiculescu-Mizil2006;

Oldenet al.2008)thatbuild uponbasicmachinelearning algorithms byincorporating a randomizationcomponent (Hastieet al.2009).The callby O'Connor(2000.2001)toad- vance the fieldofecologywith modelsthat identifyconstraints ratherthan correlates in anattempt tolindcausal relationshipsbyincorporatinganalyticaladvancesadvocatedby Breiman(1001)remainslargelyunfulfilledfully adecadelater(butsee Guilfordetal.

(1009).Monterroso et al,(2009),Oppeletal,(2009),Jiguet et al,(10I0),and Kuern- merleet al.(10I0) forexamples of ensemblelearningmethodsappliedtoRSMs).

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Fina lly.and not ofleast im por tance, isthe under ly ingtheory regard ing best modelling practices.For yearstheo ret icalhabitat ecologistshave advocatedfor the use of in- direc t(enviro nme ntal).direc t(habi tat),andresourcegradient data for constructingbotha priori hypothesesandthe models to test the m(Guisanand Zimmermann 2000; Aust in 2002;Scottet al.2002). In practi ce,RSM s aretypically param et rized withreadily available data. These data are com mo nly de rived fromremo tesens ingapplicatio nsand inter - pretati on oftheresultin gdata withinanecolog ica lcontex t(KerrandOstrovsky2003;

Cohen andGowa rd2004). What is com mo nly miss ingin this approac h isthe exp lic it datarelatin gresour ce grad ients to an ima lspace use (e.g .,spa tiall y andtemp o rall ydy- nami c food resour ces). Froma nind iv idua lani ma lview poi nttheseresourceslargely af- fect thebeh aviour al responsewe are tryingto mode lattheindivi duallevel (e.g .,Creel andChris tia nso n2008:Kan areketal.2008;MoorcroftandBarnett 2008).

In this chapter.Iattemp t to addresssomeofthese issues throughapplica tio nof emerginganalyticalapproachesto model habitat use bya generalistpredator in arelat- ivel y mon oli thic anddepaupera te land scape.SpecificallyIinvestigate whethermodels par am e trized withonlyenviro nme nta land habi tat gradientdatamay be insufficientto ac- cura te ly pred ict ha bitat use forageneralistcarnivore. indicatinga need formore resource data (i.e..prey availabi lity) .Th is isbased on myhyp oth esisthat appro pr iatedrive r data willallowfor effect ive mod ellin gusin g diverse approac hes(l.e..model converge nce).I pred ictthat aresour ce selec tion functi ondesign ed10explainparteI'llSof coyote(Canis

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latransi space use should highly correlate to the prediction of anensemble learning model, givenadequatecorrelative data.

2.2. Methods 2.2.1.StudyArea

Iobtainedcoyote data from the central portion of theMaritimeBarrens Ecoregion (MBE)ofthe Island ofNewfoundland.Canada (Figure 2.1).Theentire MBE encom- passes somegeographically disjunctunits(i.e., Avalon Peninsula,Burin Peninsula, eastern peninsulas, and coastal strip extendingwestwardfrom White BearRiver).Based on spatial connectivityandcaribou migratorypatterns.thesepeninsular areaswill notbe considered hereafter.However.thediscont inuousportions of Centraland Western New- foundlandEcoregions located entirely withintheMBE areincluded in the studyarea.The MBErepresentstheprimaryhistoricalwintering areaforsixoftheprovince's woodland caribou"herds"(Bergerud 1971 ),asdefined by CaribouManagementAreas (i.e., BuchansPlateau, Gaff Topsails.Grey River.MiddleRidge. Mount Peyton. and PotHill;

Figure2.2;NLDEC20lOa, 20lOb. 20IOc.20IOd.20IOe.20lOt). Underthecurrent management regime. the MBE containsroughlyone-thirdof ewfoundland's primary core area forcaribouanda comparable proportion ofsecondarycorearea(StantecConsulting Ltd. 2011).

The MBEischaracterizedbyheathbarrensinterspersed with peatlands and dense patches ofstunted balsamfirandspruce.The climateexhibitsthinwinter snowcover, highwindexposure.andregular. densefog (Damman 1983).Summersarecool and wet

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wintersare mildrelative tosurrounding ecorcgions.Frequentsoil frost and a history of fire preventsubstantialforcstregenerationin thisarea (Meades1983).Existing forested areas are typically restricted tothesteepsided valleysandsome hillslopes(Figure2.3).

?? ? Data Sources

Data lormy researchoriginated fromtwo generalsources.coyotecaptures to de- ployG PStrackingcollarsand publiclyavailableenvironmentaldatascts(Table2.1).

Pointdatarepresentingtheresponse variable inall models were derivedfromGPScollar locations (n=30788) combined witharandomsampleofpoints(n=61576) represent- ing available hab itatby individual for17 coyotes(8females.9males). Global Position- ing Systemcollarswere deployedby NewfoundlandandLabradorWildlifeDivisionper- sonnelduringmid-winterfrom2005 to 2008.TheseGPScollarswere programmedwith a varietyof locationrecordingschedules, which lfilteredtoa standardized. continuous interval(seeChapter3Ioradditionaldetails).lgeneratedutiiizationdistributions (UDs) via kerneldensityestimationforeachindividual forthe entirestudy periodusingHawth's Tools(Beyer 2007)withinthe ArcGIS(v.9.3: ESRI2008)geographic information system (GIS).Randompointswereselectedwithina buffered 99%volumecontourof each individualUD ata 2:1 ratiowithlocation points(seeChapter3for additionaldetails).

IusedsixteenexplanatoryvariablestoparametrizecoyoteRSMs withthe two methods outlinedbelow.Newfoundlandand Labrador WildlifeDivisionpersonnelas- signcd theindividualidentificr,and determincdagc andscx atcapturc.l delineatedyear andseason(basedoncaribou migration dates:Table2.1)fromdate informationcollected

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by GPS collars.Land coverclassificationfollowed the EarthObservationforSustainable Development of Forestssystem(WulderI!Ial.2004). Igenerated distancerastersfrom waterfeatures (NRC 2007a)androad features (NRC2007b) using theGIS.Adigital elevation model (OEM) based on the Canada3D product (NRC200 1)originally derived from the Canadian Digital Elevation Data (NRC2000)wasusedto sampleelevation.

Slopeandaspect(absolute deviation from north)werederived fromthe OEMwithin the GIS.Bothslopeandaspect weretreatedas continuousvariables.Additionally,atopo- graphicconvergence index (TCI) developedby Skinner(20 1I)replaced elevation,slope, andaspect insomecandidate models.TheTCIisaproxy forsurface moisturebased solelyon OEM componentsslope.aspect,andsteepness. High valuesofTCIrepresent highlydrained areas and lowvalues representareasof moisture collection.Resourcedata were not availableat the scaleof themodels and hencewerenotineluded.

2.2.3.DalaAnalyses

Iemployed two approachesto modellingcoyote responseto environment and habitat variables:a stochastic datamodel andanalgorithmic model.Ge neralized linear mixed-effectmodels(GLMMs)wereusedinthe stochasticdataapproach.With this structural frameworkI was able to accountforautocorrelationwithin individuals and within years by assigning these as randomeffects,

Iconstructed26 candidateGLMMs for eachseason using a varietyofex planatory variablegroupings toassess various hypotheses of coyote ecologyand potcntialinterac- tionwithcaribouandanthropogenicdisturbance(Table2.2,Appendix B). Each modelin-

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eluded parametersto consideryear (slope) dependentuponindividual (intercept)as randomeffects, followingimplementation methods of Bates (20lOa,20lOb,20 IOc).I fit the fixed-effectsportion of each GLMM with abinarylogistic regression functionusingthe Ime4package (Batesand Maechlcr 20 I0) in R(v. 2.11.1; R Development Core Team 2010).Followingstandard practices,allexplanatoryvariables were assessed for collin- earity.Allvariables inany modelhadreasonablylowPearson correlations(:50.4 1). Re- sidual plots were usedto assess assumptionsof linearmodelsincludinghomogeneity,in- dependence.normality and link function (Breslow 1996).Iassessedeachsuiteof candidate modelsusing Akaike's Information Criterion(AIC) to select the"best" summerand wintermodelsbased onthe trainingdata (BurnhamandAnderson2002).Model aver- agingwas notnecessaryduetohigh Akaike weights ofleading candidatem odels for each

As analternative tothemore commonstochastic datam odelling approach,I mod- elled the same datausingboosted regression trees(BRT).Thisalgorithmic modelfrom thefield ofmachinelearning was developed by Friedman(1999a,200 1)and laterrefi ned toincorporaterandomizationleadingto amore robustand less computationally intensive algorithm(Friedman I999b.2002).Thebase algorithmofBRT is adecisiontree. Anen- sembleof trecsisbuilti na forward,s tagewisese riesa ndo ptimized by stochasticgradi- ent descentofthe"pseudo't-rcsiduals(Ridgeway2007;Elithetal.2008).The theory be- hind ensemble methodsisthat a committeeof weak learncrs will be faI'morerobust than nsinglc complcx decisiontrec in predicting outside therange0I'trainingdata(Hastieet

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al.2009). Tree-based algorithmic models areable tohandlemissing values. incorporate interactions among predictorvariables. and identifynaturalbreaksin thedatatomodel non-linearresponse (De'ath and Fabricius2000).

In myimplementation of BRT for a coyote RSM.Ifollowed recommendations of Hastie et al,(2009)for settingalgorithm parameters.The sizeofconstituent trees(nodes;

J)was set to 6 (Hastie et al.2009:363): learningrate (shrinkage:v)was setat 0.1(Hastie et al.2 009:620 );subsarnpleof trainingdata observations ineach iteration(bagfraction;

11)was0.5(Ridgeway 2007:Hastie et al.2009:620);an additional regularizationpara- meter.numberof treesin linal model(A)wasdetermined by minimizingthecross-validation deviancefollowingthecodeofElithet al,(2008:supplementar ymaterial).Boosted regressiontree implementati onwasconductedusingthe gbm package (Ridgeway20 I0) in R.FollowinginitialBRT modeldevelopment.I rana simplification procedure(Elithet al.2008) toreducemodelcomplexityby sequentially droppingtheleastimportant variable whilemaintainin gpredictivedeviancebased on 10-fold cross-validation.This sim- plified BRTmodelwasassessed foroverall performance compared withother models.

2.2.-1.ModelEvaluation

Theobjectiveof thisresearchwastodevelop anoperationallyvalid predictive modelofcoyotespace useacross seasons andyears within the MBE.Ievaluated the

"best'Imodelsfromeachapproach using atemporally-ind ependentdatasetofG PS locations(n=II1(5) andrandompoints(n=22390) obtainedtrom7individual s( 3female.

4male).Three of the individuals(Itemale.2male)in the evaluationdatasetwere also

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monitoredwithin thetrainingdataset. Sensitivity (proportion0I'observed positive cases correctlyc lassified) ands pecificity( proportionofobserved negativecases correctlyclas- sified) of model outputare commonly usedmetricsfor assessmentofprediction tonew data(Fielding and Bell1997).Specifically,relativeoperatingcharacteristic(ROC)curves areaderivedgraphical representation ofmodeldiscrimination acrossthe rangeof thresholdvalues(Swets 1988;Pearce andFerrier2000).Iconsidered predictivecapa bil- ityfor both GLMMand BRT modelling approaches using areaunder theROC curve (AUC) with theROCRpackage (Singet al.2009) in R.AssessmentwithAUC comes withsome inherentpitfalls concerning model accuracy,especiallyformodels ofgeneral- ists andmodels built frompseudo-absences (Loboet al.2008;Hand 2009).Despite this, Loboetal.(2008) notethat AUC scorescomplemented with sensitivityandspecificity valuesare usefulfordiscriminating among models forasinglespecieswithinthesame

2.3. Results 2.3.1.StochasticData Models

Withineachseasona single"best"model emergedfromamong the 26 candidate GLMMs based onAkaike weights (Table2.2.Appendix B).Thesummerandwinter modelsdivergedsubstantially.Thesummer GLMMwas a simplifiedversionofthe winterGLMMwith2 fewerexplanatoryvariables.Alsoof noteis thedrastic differe nce invariabilityamong the randomeffects,Variance ofthe randomeffectsof individualand year was1.78x10⁷and 4.38,respectively. in thesummerGLMM:whereas.thesame ran-

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