optimizing the positioning of wildlife crossing structures ...€¦ · these scenarios represent...

J Appl Ecol 20181ndash9 wileyonlinelibrarycomjournaljpe emsp|emsp1copy 2018 The Authors Journal of Applied Ecology copy 2018 British Ecological Society

Received12November2017emsp |emsp Accepted17January2018DOI 1011111365-266413117

R E S E A R C H A R T I C L E

Optimizing the positioning of wildlife crossing structures using GPS telemetry

Guillaume Bastille-Rousseau12 emsp|emspJake Wall2emsp|emspIain Douglas-Hamilton2emsp|emsp George Wittemyer12

1DepartmentofFishWildlifeandConservationBiologyColoradoStateUniversityFortCollinsCOUSA2SavetheElephantsNairobiKenya

CorrespondenceGuillaumeBastille-RousseauEmailgbrcolostateedu

Funding informationSavetheElephantsTheNatureConservancyNaturalSciencesandEngineeringResearchCouncilofCanada

HandlingEditorManuelaGonzaacutelez-Suaacuterez

Abstract1 Developmentoftransportationcorridorshasacceleratedgloballywithinfrastruc-tureprojects being implemented across remoteecosystems particularly in thetropicsSuchdevelopmentscanhavenegativeimpactsonwildlifeandtheireco-systemsThe importanceofwildlife crossing structures tomitigateadverseef-fectsofsuch features iswidely recognizedbut thesitingofand investment incrossing structures is contentiousDataonanimalmovementprovidevaluablehighly specific information for suchprocesses but canpresent analytical chal-lengesandremainunderutilizedinplanningmitigationefforts

2 Wedevelop two algorithmsbasedon Integer Linear Programming to prioritizecrossingpointsbasedonfrequencyofuseorbreadthofcoverageamongtrackedindividuals These scenarios represent metrics likely to guide the planning ofcrossingstructureswheretheformermayrelatetotheobjectiveofminimizingvehicle-animal collisions and the latter onmaintaining ecosystem connectivityWeexemplifythealgorithmsthroughapplicationonatrackingdatasetfromover150AfricanelephantslivingneartheproposedLamuPort-SouthSudan-Ethiopia-TransportcorridorWeexploretheinfluenceofsamplingbiasonoutcomesanddiscussconsiderationstoguidetheapplicationprocess

3 Given the generally open unfencednature of this ecosystem recordedmove-mentsoccurredthroughoutthesystemandathirdofthecorridorlengthintheecosystemwas intersectedby recordedelephantmovementsThe selectionofcrossingstructure locationsand their impactsonelephantsvariedwhetherweusedasubsampleofelephantrepresentativeoflocalpopulationdensityortotalsampleofmonitored individualsThetwoalgorithmsalsoselectedfordifferentcrossingstructurelocations

4 Synthesis and applicationsOurworkshowssomeofthechallengesofusingGlobalPositioningSystem telemetry indecidingwhere toput crossing structures anddemonstratestheneedtoidentifythetypeofconstraintsinthesystemandde-siredcrossingstructurecharacteristicsaprioriWerecommendmanagerscare-fullyevaluatethepresenceofpotentialbiasesintheirdataHigh-resolutiondatacombinedwithobjectiveprioritizationmethodsallowreasonedplanningactionsbut are often lacking during critical infrastructure planning stages Given the

2emsp |emsp emspenspJournal of Applied Ecology BASTILLE- ROUSSEAU ET AL

1emsp |emspINTRODUC TION

With global development expanding the human footprint land-scape conversion and habitat loss are themost prevalent driversof population decline and key risk factors for species persistence(BrookSodhiampBradshaw2008)Landscapeconversionoccursinmany different shades from themost overt systematic alterationof the topology hydrology andvegetative structureof an area tomorenuancedchanges that leavesmuchof thenativecommunityin placeWhile nuanced developments tend to get less attentiontheycanhaveseriousecologicalramificationsLinearinfrastructuredevelopment including transportation and economic corridors inparticularisrecognizedtohaveoutsizedecologicalimpactsrelativeto their smallphysical footprints flagging these typesofdevelop-mentsasofincreasingconservationrelevanceandconcern(FahrigampRytwinski 2009) Thepotential impactsonwildlife species andecosystemfunctioningcausedbyhuman-made linearfeatures (LF)(roadsrailwayspowerlinesorpipelines)arearesultoffragmenta-tionofhabitatfacilitationofexoticspeciesinvasionandalterationofcommunityinteractions(FahrigampRytwinski2009)

InfrastructuredevelopmentandinparticularassociatedLFcaninterfere with the movement and behaviour of wildlife (EftestoslashlTsegayeHerfindal FlydalampColman 2014 Frair etal 2005) re-sulting inchanges inwildlifedistributionandabundance (FahrigampRytwinski2009)For someherbivores theedgeeffects andcan-opyopeningscausedbyLFcanenhancefoodavailabilityandaccess(GillNorrisGillampSutherland2001SmallampHunter1988)butmayalso enhance predator efficiency and alter critical interspecies in-teractions (JamesampStuart-Smith 2000Whittingtonetal 2011)LFcanincreasehumanaccesstoareaswhichcanhaveadverseef-fectsonwildlifepopulations(Northrupetal2012)andcandirectlyincreasemortalityofwildlifeandincreaseriskstohumansthroughvehiclecollisions(Neumannetal2012)Inadditiontodirectdemo-graphiceffectsLFcanactasimpermeablebarriersreducinggeneflowamongmeta-populationsbylimitingthedispersalof individu-alsorcreatingsmallpopulationdynamicsinisolatedpatches(RicoKindlmannampSedlaacuteček 2009)Mitigating thenegative impacts ofinfrastructure development on wildlife movement and ecosystemconnectivityiscriticaltothepersistenceofanimalpopulationsandthemaintenanceofkeyecosystemprocesses(Beyeretal2016)

Given these identified problems approaches to incorporateecologicaldatasets inplanningforthesitingandstructureofsuch

features are critical Crossing structures (eg overpass bridges orunderpasstunnels)arethefavouredmitigationmeasurestoreducewildlife-vehicle collisions and maintain connectivity (Clevengeramp Huijser 2011) However high-costs limit the number of thesecrossings given the limited budget allocated to these structuresIt is therefore critical that the location of these structures opti-mizestheireffectiveness inmitigatingthenegative impactsofde-velopmentsFine-scaledataonwildlifemovement suchasGlobalPositioningSystem(GPS)telemetrycanprovideaninvaluabletoolinidentifyingareasofimportanceforconnectivityalongaLF(HorneGartonKroneampLewis2007)butsurprisinglysuchdataarerarelyused during planning GPS telemetry can present analytical chal-lengeswhenappliedtoidentifyingwildlifecrossingsoneissuebeingthatthedegreetowhichthesampleofGPSmonitoredindividualsis spatially and behaviourally representative of the populationAdditionallythelimitedapplicationofGPStelemetrymayberelatedtothelackofobjectivetoolstooptimizecrossingstructureposition-ing(seeDownsetal2014LoraammampDowns2016)

In sub-SaharanAfrica over 53000kmof developments corri-dorshavebeenproposedorarebeingcreatedwiththegoalof in-creasing agricultural production natural resources extraction andoverall economic growth (Laurance SloanWeng amp Sayer 2015Wengetal2013)Theseproposedcorridorswillbisectover2000African protected areas and other areas containing high wildlifeabundances (Laurance etal 2015) making identification of keycrossing points of paramount importance to conservationmitiga-tioneffortsHerewedevelopapproachesusingGPStrackingdatatoquantifytheimportanceandoptimizetheselectionoflocationsalonginfrastructuredevelopmentsfortheestablishmentofwildlifecrossings We exemplify the utility of these approaches throughapplicationonanextensivetrackingdatasetfromover150AfricanelephantslivingneartheplannedLamuPort-SouthSudan-Ethiopia-Transport(LAPSSET)corridoroneoftheproposedsub-Saharande-velopmentcorridorsinnorthernKenyaWedeveloptwoanalyticalapproaches forassisting in localizingpotentialcrossingstructuresthefirstidentifiestheheaviestusedcrossingpointsinagivenarea(ietherelativeuseandspatialdistributionoftheidentifiedcrossinglocations)andthesecondmaximizesaccesstocrossingstructuresbythelargestnumberofindividuals(iecoverage)Wecompareap-plicationoftheseapproachestotwodifferentelephantpopulationsamplingstrategiestoinvestigatehowsamplingbiasmayimpactre-sultsFinallywediscusstheutilityofouralgorithmswithinvarious

limitedbudgetalreadyallocatedtomitigationmeasuresinmostproposeddevel-opments the tools developed and applied here can facilitate effective spatialplanning

K E Y W O R D S

AfricanelephantanimalmovementdevelopmentcorridorsGPStelemetryhabitatconnectivityintegerlinearprogrammingspatialplanningwildlifecrossingstructures

emspensp emsp | emsp3Journal of Applied EcologyBASTILLE- ROUSSEAU ET AL

managementcontextsWe includeall functionsdevelopedfor theanalysesinthefreerpackageldquowildxingrdquotofacilitatetheuseofourapproachbywildlifemanagersanddecisionmakers

2emsp |emspMATERIAL S AND METHODS

21emsp|emspStudy area

TheLaikipiaSamburuecosystem in northernKenya is inhabitedbythecountryrsquossecondlargestelephantpopulationaswellassev-eralotherspeciesofconcern including thecriticallyendangeredGrevyrsquoszebra (Equus Grevyrsquos)Theelephantpopulation isofcon-servationinterestandhasbeendesignatedoneoftheConventiononInternationalTradeinEndangeredSpecies(CITES)MonitoringofIllegalKillingofElephants(MIKE)sites(Wittemyeretal2014)TheMIKEsitedesignedtoenvelopthemajorityoftheelephantpopulations range is located at approximately 04degS to 2degN362degEto383degEcoveringanareaofapproximately34000km2 (Figure1)LanduseintheareavariesfromprivateandcommunalranchestogovernmentforestandnationalwildlifereservesTheareahasavarietyofhabitatsincludingcoolmoisthighlandforestsandsemi-aridsavannaLong-distancemovementsofbothwildlifeand livestock are common in order to access regions of highervegetation productivity driven by spatially stochastic rainfall

(Raizman Rasmussen King Ihwagi amp Douglas-Hamilton 2013WittemyerGetzVollrathampDouglas-Hamilton2007)

Our analysis is focused on a preliminary proposed section ofthe LAPSSET commerce corridor in and around the Samburu andBuffalo Springs National Reserves Complex (lying approximately05degN 375degE Figure1) The development of this transport andinfrastructure project will include the construction of railways tothecapitalsofSouthSudanandEthiopiaaroadnetworkoilpipe-linesairportsportsoilrefineriesandthreeresortcities(Figure1)Projectedrouteswillbisectthemiddleoftheecosystem

22emsp|emspData collection

We analysed GPS data collected since 1998 from 156 elephantsin this area as part of a long-term research project (WittemyerDaballenampDouglas-Hamilton2013)Duetocollarfailurestechni-caladvancesandprojectfundingcollaringeffortshavefluctuatedduringthisperiodElephantshaveacomplexgroupstructureinclud-ingstablefamiliesthatnormallytravelasagroupfromwhichmalesdisperseatmaturity(WittemyerDouglas-HamiltonampGetz2005)As a result GPS data collected from females represent a familyunitofbetween9and15individualsandmalestypicallyrepresenta single individualOnoneoccasionmore thanoneelephantwastrackedsimultaneouslyfromthesamefamilywhichwecontrolled

F IGURE 1emspTheproposedLamuPort-SouthSudan-Ethiopia-Transport(LAPSSET)CorridorprojectwillbisectcriticalwildlifeareasinnorthernKenyaWefocusedonasection(bold)oftherailwaycorridornearaConventiononInternationalTradeinEndangeredSpecies(CITES)MonitoringofIllegalKillingofElephants(MIKE)siteandnearthreenationalreserveswhereover150elephantsweretracked(representedasthinlines)

Kenya

Ethiopia

Tanzania

Somalia

Sudan

Uganda

0 340 680170 km

0 80 160 km40

LegendNational reserves

MIKE site

LAPPSET


forbyexcludingoneofthe individualsforthisanalysisErroneouslocationswerefilteredbyusingaspeed-filterof9kmhr(similartoWallWittemyerKlinkenbergLeMayampDouglas-Hamilton2013)Individual elephant tracking datasets averaged 22879 locations(range 179ndash142654) with a total sample of 3591945 locationsGPSlocationswereconvertedintotrajectorieswhereastepisthestraightlinebetweentwoconsecutivelocationsOnlystepswherethetimeintervalbetweentwolocationswaslessthan24hrwerein-cludedintheanalysissothatveryuncertainmovementstepswerenotincludedintheanalysisThesedatarepresentalargebutpoten-tially spatially biased samplebecausemany collarsweredeployedwithinnationalreserves

Wethereforeconductedaparallelanalysisonasubsetofthetotaldatasetcomprisedof40individualscollaredin2014ndash2015where collars were deployed systematically across the MIKEsiteTheseGPSlocationsarerepresentativeofelephantdensityintheecosystemascharacterizedbyaerialsurveydatacollectedin20022008and2012and locationsofnaturalmortalitycol-lectedaspartoftheMIKEecosystemwidemonitoringprogram(Ihwagi etal 2015) This dataset can be considered spatiallyunbiased

23emsp|emspQuantifying use of crossing sections of the infrastructure corridor

Toidentifycriticalcrossingpointsalongtheproposedrailwayswedivided the LAPSSET corridor into consecutive 200m long seg-ments(n=3165)evaluatedwhetheranelephantusedthesegmentandtalliedthenumberofstepsforeachelephantthatcrossed(in-tersected)thesegmentWealsocalculatedastandardizedcrossingintensitymetricCforeachsegment(s)following

whereCs isthestandardizedcrossingintensityforsegments xis isthenumberof steps from individual i thatcrossedsegments ti isthe time overwhich the individualwasmonitored in days andns isthenumberofdifferentindividualscrossingsegmentsWetestedif thecalculationof thismetricwassensitive to thenumberof in-dividuals using the segment and found no correlation (R=minus036df=1065 p=239) This standardizedmetric accounted for vari-ation in sampling frequencyand lengthofmonitoringamong indi-vidualsandcouldpotentiallyaccountfordifferentialspatialsamplingalongthecorridor(iemoreindividualscapturedandfollowedinagivenarea)Wetestedthisbylookingathowthecalculationofthecrossingintensitymetricdifferedwhetherusingthefulldatasetorthesystematicallysampledsubset

Finally we evaluated how the standardized crossing intensitymetriccomparedtocrossingpoints identifiedbycommunitygamescouts along a 40km section of the proposed LAPSSET corridorThelattercrossingpointsbasedonlocalknowledgewereassessedbydrivingtheroadwithlocalcommunityscoutsinJune2017Wecompared the segments identified by community scouts as being

usedornottothestandardizedcrossingintensityusingattestwithunequalvariancesTotestiflocalknowledgemorecloselyreflectedrecentcrossingbyelephantsorlongertermpatternsweperformedtheanalysisusingthetotaldatasetincludingonlythelatestyearofdata(May2016ndashJune2017)

24emsp|emspOptimization of crossing structure locations

Wedeveloped and applied two Integer Linear Programming algo-rithmstooptimizethepositioningofwildlifecrossingstructuresThefirstalgorithmisbasedonthestandardizedcrossingintensitymetricandthespatialdistributionofcrossingstructurelocations(hereafterreferredas thecrossing intensity [CI] algorithm)TheCIalgorithmruns an optimization procedure where the importance of a seg-mentrsquos relativeposition (spatial spread fromothercrossingpoints)vstheimportanceofintensityofusecanbespecifiedbytheuserin order to enhance the biological importance of identified cross-inglocationsandinsureselectedsegmentsaresufficientlyfarfromeachother(iewherebothprocessesareweightedequally identi-fiedcrossingswouldbeofhighuseandrelativelydistributedacrossthestudyarea)Thealgorithmwasdevelopedtoselectthemostim-portantcrossingpointsforelephants(iethoselocationsthatweremostheavilytrafficked)

Thesecondalgorithmsolvesatypicalmaximumcoverageloca-tionproblem(referredhereaftertoldquoMLCPalgorithmrdquoDownsetal2014)wheretheselectedsegmentswillldquocoverrdquothegreatestnumberofdifferentindividualsgiveneachsegmentcanserveuptoathresh-olddistanceThisalgorithmdiffersfromtheformerinthatselectedsegmentsdonothavetobeusedheavilybutcanbeincloseproxim-itytomultipleheavilyusedsegments(ieproximitytopathsismoreimportantthantheactualmovementsthathappenedacrossagivensegment) In additionbothalgorithmscanadjust theoptimizationroutinebyincorporatingnaturalcrossings(egabridgeoverariverthat is effectively a wildlife crossing point) or crossings specifieda priori for humaneconomic reasons to adjust the final optimiza-tionsolutionFurtherdetailsabouteachalgorithmareprovided inAppendixS1

Asanillustrationofeachalgorithmandtocomparehowtheycandifferwecreateddifferentscenariosthatcombinedtherep-resentative and global datasetswith each algorithm For all sce-narios we identified five sites (ie development plans budgetedtobuildfivecrossings)FortheCIalgorithmwegaveequalweighttothespatialspreadandintensityofuseFortheMCLPalgorithma thresholddistancefor thecoverageof5kmwasselectedThismeansthatsegmentswithin5km(oneachside)ofastructureareassumedtobecoveredbythisstructure(ieweassumedelephantwouldbewillingtotravelupto5kmtoreachacrossingstructuregiventhisisonthelowerendofaveragedailydisplacementsamongtheindividualstracked)Weevaluateddifferencesamongscenar-ios by calculating the minimum average distance between eachselectedcrossing(ieanalogoustotheEarthmoverrsquosdistancesta-tisticPottsAuger-MeacutetheacuteampLewis2014)WealsoranadditionalscenariosusingeachalgorithmwhicharepresentedinFiguresS1

Cs=

sumns

i=1

xis

ti

ns


andS2 inAppendixS2All functions fromouranalysis (segmen-tationofLFshome-rangeintersectioncrossingintensityandop-timization algorithms) and an explanatory vignette are availableasanrpackage ldquowildxingrdquo (httpsgithubcomBastilleRousseauwildxing)

3emsp |emspRESULTS

Upto33ofthesegmentswerecrossedbyindividualsinthetotalsample containing movements of 156 individual elephants while18 of the 200m segments analysed along the LAPSSET cor-ridor were crossed by the representative subset of 40 elephants(Figure2)As calculated from the total sample each segmentwascrossed by an average of 324 different individuals (range=1ndash22Figure2d)anaverageof637times(range=1ndash88)Usingtherepre-sentativesubsetdataseteachsegmentwascrossedbyanaverageof145different individuals (range=1ndash7 Figure2b) an averageof264times(range=1ndash53)Theaveragestandardizedcrossinginten-sitywas0023(range=0002ndash0632Figure2c)forthetotalsampleand0019(range=0007ndash0186Figure2a)forthesubsetOverallthestandardizedcrossingintensitymetricestimatedfromthesub-sampledatasetshowedmoderatecorrelationwiththemetriccalcu-latedfromthetotalsample(R=37df=2411plt001)butstronger

correlationwith the number of individuals using a given segment(R=57df=2411plt001)

Comparisonbetweenthecrossingsegmentsidentifiedthroughthe optimization routine indicates that strong differentiation incrossingweighting occurredwith the different emphases of thedifferentroutines(Figure3)UsingtheCIalgorithmselectedloca-tionsshowedgreaterdifferencebetweentherepresentativesub-set and the total sample (average distance=36km Figure3ab)thanwhenusing theMCLPalgorithm (averagedistance=16kmFigure3cd) Applying each algorithm to the same dataset alsoprovideddifferentresultswithaveragedistancebetweenselectedcrossingof24and30kmfortherepresentative(Figure3ac)andglobal(Figure3bd)datasetsrespectivelyOveralltheMCLPpro-vided connectivity for a greater number of elephants (Figure3)Modifyingparametersofeachscenarioalsoresultedinadifferentsetofselectedlocations(FiguresS1andS2)indicatingthesensi-tivityofresultstoweighting

Crossing segments identified using local knowledge were notsignificantly aligned with tracking data-based crossing intensitymetrics given that the segments considered as used based onlocalknowledgedidnothaveahigherstandardizedcrossinginten-sity (t=minus074668 p=4602 Figure4a) Alignment was also notinfluenced by the timing of the tracking data (latest year of datat=minus0994p=348Figure4b)

F IGURE 2emspSubsectionoftheproposedLamuPort-SouthSudan-Ethiopia-Transport(LAPSSET)Corridorproject(a)StandardizedcrossingintensityofelephantwiththeLAPSSETcorridorusingarepresentativesampleof40elephants(seeSection2)(b)NumberofdifferentelephantscrossingeachsegmentoftheLAPSSETcorridorusingtherepresentativesampleofelephants(c)StandardizedcrossingintensityofelephantwiththeLAPSSETcorridorusingallmonitoredindividuals(d)NumberofdifferentelephantscrossingeachsegmentoftheLAPSSETcorridorusingallmonitoredindividuals

00

01 ndash 10

11 ndash 20

20 ndash 41

41 ndash 70

0000000

0000001 ndash 0001505

0001506 ndash 0002471

0002472 ndash 0005652

0005653 ndash 0018624

(b)(a) Number of individualsCrossing intensity

(c) (d)Number of individualsCrossing intensity

0000000 ndash 0000899

0000900 ndash 0003076

0003077 ndash 0004052

0004053 ndash 0011295

0011296 ndash 0063250

00 ndash 10

11 ndash 40

41 ndash 70

71 ndash 140

141 ndash 220


4emsp |emspDISCUSSION

Mitigatingtheimpactsofinfrastructurecorridorsonwildlifeiscriti-caltoreduceecologicalimpacts(ClevengerampHuijser2011)toen-hancepublicsafety(OlssonampWiden2008)andalleviatetheneedforcostlyretrofittingprojectsforfuturemitigationneedsTofacili-tateconservationplanninginrelationtothedevelopmentofinfra-structure projectswe developed simple and efficient approachesto analytically identify priority crossing locations fromGPS track-ingdataSuchdataare increasinglycollectedgloballyandprovidethehighestspatio-temporaldataonwildlifeusethatcanbepower-fulforunderstandingandmitigatingimpactsfromhumanland-usechanges

Weappliedthesealgorithmstoanextensiveradiotrackingdata-setcollectedonelephantsinhabitingtheLaikipiaSamburuecosys-temsubjecttoamajorinfrastructuredevelopmentprojectAfricanelephantswereourtargetspeciesgiventhattheyareaflagshipandan umbrella species whose protection can have significant posi-tiveimpactsonotherspecies(EppsMutayobaGwinampBrashares2011)andecosystemfunctioning(Haynes2012)Giventhegener-allyopenunfencednatureofthisecosystemrecordedmovementsoccurred throughout the system and roughly a third of the corri-dorlengthintheecosystemwasintersectedbyrecordedelephantmovements (ie elephants crossed over the proposed route) Theundevelopednatureofthestudyareaaffordsarareopportunityto

identifycriticalareaslikelytobeimpactedbydevelopmentaprioribut also presents a challenge given thatwildlifemovement is notconstrained andmost areas in the region are used bywildlifeAssuchrestrictingfocustoahandfulofcrossinglocationsissensitivetothedefinitionofonersquoscriteria

41emsp|emspApplication of crossing algorithms

WedevelopedtwoalgorithmsthathighlightdifferentfeaturesofthemovementdataandultimatelyaddressdifferentobjectiveswhenanalysingthesedataThefirstapproachtheCIalgorithmissuitedforsituationswherethespecificcrossingintensityisofinterestalonga LF This is applicable to scenarioswhen the immediate crossinglocationistheunitofinterestforplanningsuchaswhenattempt-ingtomitigatevehicle-animalcollisionsorwhencrossingstructureswillhavethebiggestimpactsbybeingplacedincurrentlyusedloca-tionsascanoccurwhenaLFwillremainunfencedandorrepresentasemi-permeablebarrierThesecondapproachusingtheMCLPal-gorithm issuited fora limited-accessscenario (eg fencing)wherefewcrossingstructureswillbelocatedatregulardistancesandcon-nectivityrestrictedinallotherlocationsIntheMCLPapproachtheimportanceoftheselectedsegmentisnotstronglyweightedratherthe focus ison identifyingthesegment thatprovides thegreatestcoverageforindividualsthatgenerallyuseanareaWeassumethisalgorithmwouldbeidealforscenarioswherefencingisusedalong

F IGURE 3emspOptimizationofcrossingstructureslocationalongtheLamuPort-SouthSudan-Ethiopia-Transport(LAPSSET)corridorrailwaysusingthecrossingintensityandmaximumcoveragelocationproblemalgorithms(seeSection2)(a)Thecrossingintensity(CI)algorithmselectedthetopfivecrossingsegmentsbasedonequallyweightingtheinfluenceofspatialspreadandintensityofuseintheoptimizationandusingthesubsampleofdata(b)Thescenarioin(a)appliedtothetotaldataset(c)Themaximumcoveragelocationalgorithm(MCLP)selectedthetopfivecrossingsegmentsbasedonathresholddistanceof5kmandusingtherepresentativesampleofindividuals(d)Thescenarioin(c)appliedtothetotaldataset

G

GGGG

G GG G

G

(b)(a)

elephants = 5 elephants = 43

G

GG

G

G

GG G

G

G

(c) (d)



LFstofunnel individualsintoagivencrossingpointGiventhedif-ferentnatureofthesetwoapproachesandthedivergingresultstheyprovideitiscriticalthattheuseridentifiesthetypeofconstraintsinthesystemanddesiredcrossingstructurecharacteristics

42emsp|emspRepresentativeness of GPS telemetry

Overall theselectionofcrossingstructure locationsandtheir im-pactsonelephantsvariedwhetherweusedthesubsampleortotalsampleofmonitoredindividualsThisisconcerningfortherobust-nessof results reliantonGPSdata tooptimize crossing locationsStrong spatial bias in monitoring has the potential to impact theoveralloptimizationevenifalgorithmresultsarenotclusterednearthecoreof thespatiallybiasedsampleWerecommendmanagerscarefullyevaluatethepresenceofpotentialbiasesintheirdataandsubsampleorweightindividualdatainmannerthatoffersarepre-sentativedatasetTherunningofdifferentscenariosasexemplifiedherecanhelpdecisionmakersunderstandthesensitivityofresultstoimposeassumptionsHoweverthewidevariationinresultsinthestudyareaislikelyrelatedtothegeneralandwidelydisperseduseofthisunfencedecosystemOthersystemswithmoredevelopmentlikelyhavegreaterrestrictionsonpossiblecrossingpointsalongaLFwhichmayrequireexcludingsegmentsfromtheoptimizationalgo-rithm(bothoptimizationfunctionsavailableinthewildxingpackageincludethisoption)

We also compared elephant crossing locations obtained fromsurveyswith local communities to those identifiedusingGPS te-lemetrydatatocontrastoutcomesthatresultfromdifferenttypesof data The locations of key crossing locations were not wellmatched across these two sourcesWhere tracking and commu-nity identifiedcrossing locationscoincided thecrossing intensityasmeasuredby the trackingdatawas relatively lowThiswas ir-respectiveofthetimingoftheGPStrackingdatausedtoidentifycrossingsCommunity identified crossingsweremore likely tobefoundnearvillagesorlivestockpathsWhileGPStelemetrysuffersfromitsownissues(discussedinthepreviousparagraph)ouranaly-sissuggeststhatcommunity-baseddataareimportantwhereothersources are not available but subject to biases related to humanactivities and ideally would require more systematic surveyingEmpiricallycollecteddataonanimalactivitiesmaybemorereliablewhenavailable

43emsp|emspAdditional considerations

Our algorithms allowmultiple factors andmotivations to be con-sidered in selecting crossing locations Our algorithms combiningintensityofuseandnumberof individuals representedarean im-provement over existing approaches Previous approaches typi-callysummarized thegeneralcrossingbehaviourhabitatselectionorsimply record intensityofusewithoutsubsequentoptimization(Horne etal 2007 Sawyer Kauffman Nielson amp Horne 2009SchusterRoumlmerampGermain2013)Otherapproachesproposedop-timizationalgorithmsbutwithoutregardtosamplingbiasandspa-tialspread(Downsetal2014LoraammampDowns2016)Intensityof usemay not be the onlymetric of valuewhenquantifying theimportance of a segment forwildlifemovementApproaches thatconsider the importanceofa segment for specific typesofmove-ment(egmovementmodesGurarieetal2016MoralesHaydon

F IGURE 4emspElephantstandardizedcrossingintensityestimatedfromGPStrackingdatawascomparedwithcrossingpointsidentifiedbylocalcommunityscouts(a)Communitiesidentifiedcrossinglocations(dots)andstandardizedcrossingintensityfor200msegmentsoftheroadusingGPStelemetrycollectedbetween2001and2017(b)CommunityscoutidentifiedcrossinglocationsandstandardizedcrossingmetricsdevelopedusingGPStelemetrydatacollectedJune2016ndashJune2017

0000000

0000001 ndash 0003753

0003754 ndash 0006340

0006341 ndash 0010170

0010171 ndash 0019253

(a)

(b)Last year

Full dataset

0000000 ndash 0000691

0000692 ndash 0002250

0002251 ndash 0003951

0003952 ndash 0006352

0006353 ndash 0054055


FrairHolsingerampFryxell2004)orprovideinsighttobroaderscaleconnectivityofalocation(egnetworkmetricsBastille-RousseauDouglas-HamiltonBlakeNorthrupampWittemyer2018WittemyerKeating Vollrath amp Douglas-Hamilton 2017) could providemorerefinedidentificationofcrossinglocationsinprioritizationschemesLikewise approaches assessing connectivity for multiple speciesmaybepreferablewherecommunityormultispeciesconservationgoalsareparamount(MimetClauzelampFoltecircte2016)Wefocusonasinglespeciesinthisstudygiventhelackofcomparabledatafromotherspecies

The increased pace of land-use change and ecosystem deg-radation poses serious threats to wildlife (Cardinale etal 2012)demandingnovelmitigationapproachesSolutionstomitigateland-usechangearechronicallyunderfunded (LindseyBalmeFunstonHenschel ampHunter 2016Miller 2014) requiring smart planningto maximize impact from limited resources (Kiesecker CopelandPocewicz amp McKenney 2010 Mandle etal 2016) Decades oflargemammal research have generated valuable data that can beapplied to such tasks High-resolution data combinedwith objec-tiveprioritizationmethodsallowreasonedplanningactionsbutareoftenlackingduringcriticalinfrastructureplanningstagesInsteadthesedecisionsarefrequentlymadesubjectivelywhichcanresultinsuboptimaluseoffundsdirectedtowardsconservation(KareivaGrovesampMarvier2014)Giventhelimitedbudgetalreadyallocatedtomitigationmeasuresinmostproposeddevelopmentstoolsthatfacilitatespatialplanningareofhighvalue

ACKNOWLEDG EMENTS

ElephantmovementdatacamefromtheSavetheElephantsTrackingAnimals forConservationProgramGBRwassupportedbySavetheElephants TheNatureConservancy and theNatural SciencesandEngineeringResearchCouncilofCanadaAnneMTrainordigi-tizedtheLAPSSETcorridorWethankCharlotteFNarrandAnneMTrainorforearlycommentsonthismanuscript

AUTHORSrsquo CONTRIBUTIONS

GW and JW initially thought of the crossing count approachGBRdevelopedthestandardizationtheoptimizationalgorithmand the analytical framework (wildxing package) GBR per-formedtheanalysesIDHandGWcollectedtheelephantdataGBR ledthewritingofthemanuscriptwithcontributionsfromGWAllauthorshavecommentedandapprovedthefinalversionofthemanuscript

DATA ACCE SSIBILIT Y

Elephanttrackingdatahavenotbeenarchivedgiventheirhighlysen-sitivenatureInterestedreaderscancontactthecorrespondingau-thordirectlyforinquiriesFunctionsandassociateddocumentation(whichincludeexamples)areavailablewithintherpackagewildxinghttpsdoiorg105281zenodo1158705(Bastille-Rousseau(2018)

ORCID

Guillaume Bastille-Rousseau httporcidorg0000-0001-6799-639X

R E FE R E N C E S

Bastille-Rousseau G (2018) First release of wildxing Zenodo Digital Repositoryhttpsdoiorg105281zenodo1158705

Bastille-RousseauGDouglas-HamiltonIBlakeSNorthrupJMampWittemyerG(2018)Applyingnetworktheorytoanimalmovementsto identify properties of landscape space Ecological Applicationshttpsdoiorg101002eap1697

Beyer H L Gurarie E Boumlrger L Panzacchi M Basille MHerfindal I hellip Matthiopoulos J (2016) ldquoYou shall not passrdquoQuantifying barrier permeability and proximity avoidanceby animals Journal of Animal Ecology 85 43ndash53 httpsdoiorg1011111365-265612275

BrookBWSodhiNSampBradshawC J (2008) SynergiesamongextinctiondriversunderglobalchangeTrends in Ecology amp Evolution23453ndash460httpsdoiorg101016jtree200803011

Cardinale B JDuffy J EGonzalezAHooperDU Perrings CVenail PhellipNaeem S (2012) Biodiversity loss and its impact onhumanityNature489326httpsdoiorg101038nature11373

ClevengerAPampHuijserMP(2011)Wildlifecrossingstructurehand-bookDesignandevaluationinNorthAmerica

DownsJHornerMLoraammRAndersonJKimHampOnoratoD(2014)Strategically locatingwildlifecrossingstructuresforFloridapanthersusingmaximalcoveringapproachesTransactions in GIS1846ndash65httpsdoiorg101111tgis12005

EftestoslashlSTsegayeDHerfindal IFlydalKampColmanJE(2014)Measuring effects of linear obstacles on wildlife movementsAccounting for the relationship between step length and cross-ing probability European Journal of Wildlife Research 60 271ndash278httpsdoiorg101007s10344-013-0779-7

Epps CWMutayoba BM Gwin L amp Brashares J S (2011) Anempirical evaluation of theAfrican elephant as a focal species forconnectivityplanning inEastAfricaDiversity and Distributions17603ndash612httpsdoiorg101111j1472-4642201100773x

FahrigLampRytwinskiT(2009)EffectsofroadsonanimalabundanceAn empirical review and synthesis Ecology and Society 14 art21httpsdoiorg105751ES-02815-140121

FrairJLMerrillEHVisscherDRFortinDBeyerHLampMoralesJM(2005)Scalesofmovementbyelk(Cervuselaphus)inresponsetoheterogeneity inforageresourcesandpredationriskLandscape Ecology20273ndash287httpsdoiorg101007s10980-005-2075-8

GillJANorrisKGillJampSutherlandWJ(2001)Whybehaviouralresponsesmaynot reflect thepopulation consequencesofhumandisturbance Biological Conservation 97 265ndash268 httpsdoiorg101016S0006-3207(00)00002-1

GurarieEBracisCDelgadoMMeckleyTDKojolaIampWagnerCM(2016)WhatistheanimaldoingToolsforexploringbehavioralstructureinanimalmovementsJournal of Animal Ecology8569ndash84httpsdoiorg1011111365-265612379

HaynesG(2012)Elephants(andextinctrelatives)asearth-moversandecosystemengineersGeomorphology157ndash15899ndash107httpsdoiorg101016jgeomorph201104045

HorneJSGartonEOKroneSMampLewisJS(2007)AnalyzinganimalmovementsusingBrownianbridgesEcology882354ndash2363httpsdoiorg10189006-09571

IhwagiFWWangTWittemyerGSkidmoreAKToxopeusAGNgene S Douglas-Hamilton I (2015)Using poaching levels andelephant distribution to assess the conservation efficacy of privatecommunalandgovernmentlandinnorthernKenyaPLoS ONE101ndash17


JamesARCampStuart-SmithAK(2000)DistributionofcaribouandwolvesinrelationtolinearcorridorsJournal of Wildlife Management64154ndash159httpsdoiorg1023073802985

Kareiva P Groves C amp Marvier M (2014) The evolving linkagebetween conservation science and practice at the nature con-servancy Journal of Applied Ecology 51 1137ndash1147 httpsdoiorg1011111365-266412259

Kiesecker J M Copeland H Pocewicz A ampMcKenney B (2010)DevelopmentbydesignBlendinglandscapelevelplanningwiththemitigationhierarchyFrontiers in Ecology and the Environment8261ndash266httpsdoiorg101890090005

LauranceWFSloanSWengLampSayerJA(2015)Estimatingtheenvironmental costs of Africarsquos massive ldquodevelopment CorridorsrdquoCurrent Biology 25 3202ndash3208 httpsdoiorg101016jcub201510046

LindseyPABalmeGAFunstonPJHenschelPHampHunterLTB(2016)LifeafterCecilChannellingglobaloutrageintofundingforconservationinAfricaConservation Letters9296ndash301httpsdoiorg101111conl12224

LoraammRWampDownsJA(2016)Awildlifemovementapproachtooptimally locatewildlifecrossingstructures International Journal of Geographical Information Science3074ndash88httpsdoiorg1010801365881620151083995

MandleLBryantBPRuckelshausMGenelettiDKieseckerJMampPfaffA (2016)Entrypointsforconsideringecosystemserviceswithin infrastructureplanningHowto integrateconservationwithdevelopmentinordertoaidthembothConservation Letters9221ndash227httpsdoiorg101111conl12201

MillerDC(2014)ExplainingglobalpatternsofinternationalaidforlinkedbiodiversityconservationanddevelopmentWorld Development59341ndash359httpsdoiorg101016jworlddev201401004

Mimet A Clauzel C amp Foltecircte J C (2016) Locatingwildlife cross-ings for multispecies connectivity across linear infrastructuresLandscape Ecology 31 1955ndash1973 httpsdoiorg101007s10980-016-0373-y

MoralesJMHaydonDTFrairJHolsingerKEampFryxell JM(2004) Extracting more out of relocation data Building move-mentmodelsasmixturesofrandomwalksEcology852436ndash2445httpsdoiorg10189003-0269

Neumann W Ericsson G Dettki H Bunnefeld N Keuler N SHelmers D P amp Radeloff V C (2012) Difference in spatiotem-poral patterns of wildlife road-crossings and wildlife-vehicle colli-sionsBiological Conservation14570ndash78httpsdoiorg101016jbiocon201110011

Northrup JMPitt JMuhlyTB StenhouseGBMusianiMampBoyceMS(2012)Vehicletrafficshapesgrizzlybearbehaviouronamultiple-uselandscapeJournal of Applied Ecology491159ndash1167httpsdoiorg101111j1365-2664201202180x

OlssonM PO ampWiden P (2008) Effects of highway fencing andwildlife crossingsonmooseAlcesalcesmovementsand spaceusein southwestern SwedenWildlife Biology14 111ndash117 httpsdoiorg1029810909-6396(2008)14[111EOHFAW]20CO2

Potts J Auger-MeacutetheacuteMamp LewisM (2014)A generalized residualtechnique for analyzing complex movement models using earth moverrsquos distanceMethods in Ecology and Evolution 5 1012ndash1022httpsdoiorg1011112041-210X12253

RaizmanEARasmussenHBKingLEIhwagiFWampDouglas-Hamilton I (2013)Feasibility studyon the spatial and temporalmovementofSamburursquoscattleandwildlifeinKenyausingGPSradio-tracking remote sensing andGISPreventive Veterinary Medicine11176ndash80httpsdoiorg101016jprevetmed201304007

Rico A Kindlmann P amp Sedlaacuteček F (2009) Can the barrier ef-fect of highways cause genetic subdivision in small mammalsActa Theriologica 54 297ndash310 httpsdoiorg104098jat0001-70510682008

Sawyer H Kauffman M J Nielson R M amp Horne J S (2009)Identifyingandprioritizingungulatemigrationroutesforlandscape-level conservation Ecological Applications19 2016ndash2025 httpsdoiorg10189008-20341

SchusterRRoumlmerHampGermainRR(2013)Usingmulti-scaledistri-butionandmovementeffectsalongamontanehighwaytoidentifyoptimal crossing locations for a large-bodiedmammal communityPeerJ1e189httpsdoiorg107717peerj189

SmallMFampHunterML(1988)ForestFragmentationandaviannestpredation in forest landscapes Oecologia 76 62ndash64 httpsdoiorg101007BF00379601

WallJWittemyerGKlinkenbergBLeMayVampDouglas-HamiltonI (2013) Characterizing properties and drivers of long distancemovements by elephants (Loxodonta africana) in the GourmaMali Biological Conservation 157 60ndash68 httpsdoiorg101016 jbiocon201207019

WengLBoedhihartonoAKDirksPHGMDixonJLubisMIampSayerJA(2013)Mineralindustriesgrowthcorridorsandagricul-turaldevelopmentinAfricaGlobal Food Security2195ndash202httpsdoiorg101016jgfs201307003

WhittingtonJHebblewhiteMDeCesareNJNeufeldLBradleyM Wilmshurst J amp Musiani M (2011) Caribou encounterswith wolves increase near roads and trails A time-to-event ap-proach Journal of Applied Ecology 48 1535ndash1542 httpsdoiorg101111j1365-2664201102043x

WittemyerGDaballenDampDouglas-HamiltonI(2013)Comparativedemographyofanat-riskAfricanelephantpopulationPLoS ONE8e53726httpsdoiorg101371journalpone0053726

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WittemyerGGetzWMVollrathFampDouglas-HamiltonI(2007)Social dominance seasonal movements and spatial segregationin African elephants A contribution to conservation behaviorBehavioral Ecology and Sociobiology 61 1919ndash1931 httpsdoiorg101007s00265-007-0432-0

WittemyerGKeatingLMVollrathFampDouglas-HamiltonI(2017)Graphtheoryillustratesspatialandtemporalfeaturesthatstructureelephant rest locations and reflect risk perception Ecography 40598ndash605

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SUPPORTING INFORMATION

Additional Supporting Information may be found online in thesupportinginformationtabforthisarticle

How to cite this articleBastille-RousseauGWallJDouglas-HamiltonIWittemyerGOptimizingthepositioningofwildlifecrossingstructuresusingGPStelemetryJ Appl Ecol 2018001ndash9 httpsdoiorg1011111365-266413117


1emsp |emspINTRODUC TION

With global development expanding the human footprint land-scape conversion and habitat loss are themost prevalent driversof population decline and key risk factors for species persistence(BrookSodhiampBradshaw2008)Landscapeconversionoccursinmany different shades from themost overt systematic alterationof the topology hydrology andvegetative structureof an area tomorenuancedchanges that leavesmuchof thenativecommunityin placeWhile nuanced developments tend to get less attentiontheycanhaveseriousecologicalramificationsLinearinfrastructuredevelopment including transportation and economic corridors inparticularisrecognizedtohaveoutsizedecologicalimpactsrelativeto their smallphysical footprints flagging these typesofdevelop-mentsasofincreasingconservationrelevanceandconcern(FahrigampRytwinski 2009) Thepotential impactsonwildlife species andecosystemfunctioningcausedbyhuman-made linearfeatures (LF)(roadsrailwayspowerlinesorpipelines)arearesultoffragmenta-tionofhabitatfacilitationofexoticspeciesinvasionandalterationofcommunityinteractions(FahrigampRytwinski2009)

InfrastructuredevelopmentandinparticularassociatedLFcaninterfere with the movement and behaviour of wildlife (EftestoslashlTsegayeHerfindal FlydalampColman 2014 Frair etal 2005) re-sulting inchanges inwildlifedistributionandabundance (FahrigampRytwinski2009)For someherbivores theedgeeffects andcan-opyopeningscausedbyLFcanenhancefoodavailabilityandaccess(GillNorrisGillampSutherland2001SmallampHunter1988)butmayalso enhance predator efficiency and alter critical interspecies in-teractions (JamesampStuart-Smith 2000Whittingtonetal 2011)LFcanincreasehumanaccesstoareaswhichcanhaveadverseef-fectsonwildlifepopulations(Northrupetal2012)andcandirectlyincreasemortalityofwildlifeandincreaseriskstohumansthroughvehiclecollisions(Neumannetal2012)Inadditiontodirectdemo-graphiceffectsLFcanactasimpermeablebarriersreducinggeneflowamongmeta-populationsbylimitingthedispersalof individu-alsorcreatingsmallpopulationdynamicsinisolatedpatches(RicoKindlmannampSedlaacuteček 2009)Mitigating thenegative impacts ofinfrastructure development on wildlife movement and ecosystemconnectivityiscriticaltothepersistenceofanimalpopulationsandthemaintenanceofkeyecosystemprocesses(Beyeretal2016)

Given these identified problems approaches to incorporateecologicaldatasets inplanningforthesitingandstructureofsuch

features are critical Crossing structures (eg overpass bridges orunderpasstunnels)arethefavouredmitigationmeasurestoreducewildlife-vehicle collisions and maintain connectivity (Clevengeramp Huijser 2011) However high-costs limit the number of thesecrossings given the limited budget allocated to these structuresIt is therefore critical that the location of these structures opti-mizestheireffectiveness inmitigatingthenegative impactsofde-velopmentsFine-scaledataonwildlifemovement suchasGlobalPositioningSystem(GPS)telemetrycanprovideaninvaluabletoolinidentifyingareasofimportanceforconnectivityalongaLF(HorneGartonKroneampLewis2007)butsurprisinglysuchdataarerarelyused during planning GPS telemetry can present analytical chal-lengeswhenappliedtoidentifyingwildlifecrossingsoneissuebeingthatthedegreetowhichthesampleofGPSmonitoredindividualsis spatially and behaviourally representative of the populationAdditionallythelimitedapplicationofGPStelemetrymayberelatedtothelackofobjectivetoolstooptimizecrossingstructureposition-ing(seeDownsetal2014LoraammampDowns2016)

In sub-SaharanAfrica over 53000kmof developments corri-dorshavebeenproposedorarebeingcreatedwiththegoalof in-creasing agricultural production natural resources extraction andoverall economic growth (Laurance SloanWeng amp Sayer 2015Wengetal2013)Theseproposedcorridorswillbisectover2000African protected areas and other areas containing high wildlifeabundances (Laurance etal 2015) making identification of keycrossing points of paramount importance to conservationmitiga-tioneffortsHerewedevelopapproachesusingGPStrackingdatatoquantifytheimportanceandoptimizetheselectionoflocationsalonginfrastructuredevelopmentsfortheestablishmentofwildlifecrossings We exemplify the utility of these approaches throughapplicationonanextensivetrackingdatasetfromover150AfricanelephantslivingneartheplannedLamuPort-SouthSudan-Ethiopia-Transport(LAPSSET)corridoroneoftheproposedsub-Saharande-velopmentcorridorsinnorthernKenyaWedeveloptwoanalyticalapproaches forassisting in localizingpotentialcrossingstructuresthefirstidentifiestheheaviestusedcrossingpointsinagivenarea(ietherelativeuseandspatialdistributionoftheidentifiedcrossinglocations)andthesecondmaximizesaccesstocrossingstructuresbythelargestnumberofindividuals(iecoverage)Wecompareap-plicationoftheseapproachestotwodifferentelephantpopulationsamplingstrategiestoinvestigatehowsamplingbiasmayimpactre-sultsFinallywediscusstheutilityofouralgorithmswithinvarious

limitedbudgetalreadyallocatedtomitigationmeasuresinmostproposeddevel-opments the tools developed and applied here can facilitate effective spatialplanning

K E Y W O R D S

AfricanelephantanimalmovementdevelopmentcorridorsGPStelemetryhabitatconnectivityintegerlinearprogrammingspatialplanningwildlifecrossingstructures











Kenya

Ethiopia

Tanzania

Somalia

Sudan

Uganda

0 340 680170 km

0 80 160 km40


MIKE site

LAPPSET













Cs=

sumns

i=1

xis

ti

ns



3emsp |emspRESULTS






00

01 ndash 10

11 ndash 20

20 ndash 41

41 ndash 70

0000000

0000001 ndash 0001505

0001506 ndash 0002471

0002472 ndash 0005652

0005653 ndash 0018624



0000000 ndash 0000899

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0011296 ndash 0063250

00 ndash 10

11 ndash 40

41 ndash 70

71 ndash 140

141 ndash 220









G

GGGG

G GG G

G

(b)(a)


G

GG

G

G

GG G

G

G

(c) (d)










0000000

0000001 ndash 0003753

0003754 ndash 0006340

0006341 ndash 0010170

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(a)

(b)Last year

Full dataset

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0002251 ndash 0003951

0003952 ndash 0006352

0006353 ndash 0054055




ACKNOWLEDG EMENTS






ORCID


R E FE R E N C E S


























































Kenya

Ethiopia

Tanzania

Somalia

Sudan

Uganda

0 340 680170 km

0 80 160 km40


MIKE site

LAPPSET













Cs=

sumns

i=1

xis

ti

ns



3emsp |emspRESULTS






00

01 ndash 10

11 ndash 20

20 ndash 41

41 ndash 70

0000000

0000001 ndash 0001505

0001506 ndash 0002471

0002472 ndash 0005652

0005653 ndash 0018624



0000000 ndash 0000899

0000900 ndash 0003076

0003077 ndash 0004052

0004053 ndash 0011295

0011296 ndash 0063250

00 ndash 10

11 ndash 40

41 ndash 70

71 ndash 140

141 ndash 220









G

GGGG

G GG G

G

(b)(a)


G

GG

G

G

GG G

G

G

(c) (d)










0000000

0000001 ndash 0003753

0003754 ndash 0006340

0006341 ndash 0010170

0010171 ndash 0019253

(a)

(b)Last year

Full dataset

0000000 ndash 0000691

0000692 ndash 0002250

0002251 ndash 0003951

0003952 ndash 0006352

0006353 ndash 0054055




ACKNOWLEDG EMENTS






ORCID


R E FE R E N C E S




























































Cs=

sumns

i=1

xis

ti

ns



3emsp |emspRESULTS






00

01 ndash 10

11 ndash 20

20 ndash 41

41 ndash 70

0000000

0000001 ndash 0001505

0001506 ndash 0002471

0002472 ndash 0005652

0005653 ndash 0018624



0000000 ndash 0000899

0000900 ndash 0003076

0003077 ndash 0004052

0004053 ndash 0011295

0011296 ndash 0063250

00 ndash 10

11 ndash 40

41 ndash 70

71 ndash 140

141 ndash 220









G

GGGG

G GG G

G

(b)(a)


G

GG

G

G

GG G

G

G

(c) (d)










0000000

0000001 ndash 0003753

0003754 ndash 0006340

0006341 ndash 0010170

0010171 ndash 0019253

(a)

(b)Last year

Full dataset

0000000 ndash 0000691

0000692 ndash 0002250

0002251 ndash 0003951

0003952 ndash 0006352

0006353 ndash 0054055




ACKNOWLEDG EMENTS






ORCID


R E FE R E N C E S


















































3emsp |emspRESULTS






00

01 ndash 10

11 ndash 20

20 ndash 41

41 ndash 70

0000000

0000001 ndash 0001505

0001506 ndash 0002471

0002472 ndash 0005652

0005653 ndash 0018624



0000000 ndash 0000899

0000900 ndash 0003076

0003077 ndash 0004052

0004053 ndash 0011295

0011296 ndash 0063250

00 ndash 10

11 ndash 40

41 ndash 70

71 ndash 140

141 ndash 220









G

GGGG

G GG G

G

(b)(a)


G

GG

G

G

GG G

G

G

(c) (d)










0000000

0000001 ndash 0003753

0003754 ndash 0006340

0006341 ndash 0010170

0010171 ndash 0019253

(a)

(b)Last year

Full dataset

0000000 ndash 0000691

0000692 ndash 0002250

0002251 ndash 0003951

0003952 ndash 0006352

0006353 ndash 0054055




ACKNOWLEDG EMENTS






ORCID


R E FE R E N C E S
























































G

GGGG

G GG G

G

(b)(a)


G

GG

G

G

GG G

G

G

(c) (d)










0000000

0000001 ndash 0003753

0003754 ndash 0006340

0006341 ndash 0010170

0010171 ndash 0019253

(a)

(b)Last year

Full dataset

0000000 ndash 0000691

0000692 ndash 0002250

0002251 ndash 0003951

0003952 ndash 0006352

0006353 ndash 0054055




ACKNOWLEDG EMENTS






ORCID


R E FE R E N C E S
























































0000000

0000001 ndash 0003753

0003754 ndash 0006340

0006341 ndash 0010170

0010171 ndash 0019253

(a)

(b)Last year

Full dataset

0000000 ndash 0000691

0000692 ndash 0002250

0002251 ndash 0003951

0003952 ndash 0006352

0006353 ndash 0054055




ACKNOWLEDG EMENTS






ORCID


R E FE R E N C E S



















































ACKNOWLEDG EMENTS






ORCID


R E FE R E N C E S
















































optimizing the positioning of wildlife crossing structures ...€¦ · these scenarios represent...

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