Remotely Wild Spring 2016 - The Wildlife Societywildlife.org/wp-content/uploads/2016/01/Remotely-Wild... · 2018-09-14 · Remotely Wild - Summer 2016 "6 WORKSHOP PREVIEW Modeling

T H E W I L D L I F E S O C I E T Y

In this issue: Fg 1: From the Working Group Chair.

Pg 2: SETWG annual Treasurer’s report & executive team update

Pg 3: Conference travel awards announcement

Pg 4: 2015 conference workshop recap

Pg 6: mkde R-package conference workshop preview

Pg 7: SETWG awards 2016

Pg 9: Hard travelin’ down the Prairie: within-season breeding dispersal in a declining grassland songbird

Pg 15: An autonomous GPS geofence alert system to curtail avian fatalities at wind farms

Pg 19: Foraging habitat characteristics, prey diversity and detectability of breeding Rusty Blackbirds: implications for land and wildlife management in the Northern Forest

Pg 24: Bobcat habitat selection in a frequently-burned pine savanna

Newsletter of the Spatial Ecology & Telemetry Working GroupIssue 32 Summer 2016

From the Working Group Chair Welcome to the 2016 Summer edition of Remotely Wild, the newsletter of the Spatial Ecology & Telemetry Working Group of the Wildlife Society (SETWG). I hope you are having a productive season of field work and spatial data analyses. We have some great articles in this edition, and this year is proving to be an exciting one for the Working Group as we gear up for the annual Wildlife Society conference in Raleigh, North Carolina this October. We’ll continue to keep you up to date. Last year was an exciting one for SETWG! We sponsored a symposia at the 2015 Wildlife Society conference in Winnipeg as well as the “Rhr: a package for home range estimation with a graphical user interface” workshop led by Rhr R-package authors Johannes Signer and Dr. Niko Balkenhol that was a sold-out success (see page 4). This year we are pleased to again be able to offer travel awards for students to attend the 2016 conference. SETWG is also excited to be sponsoring a half-day workshop ‘Modeling & Visualizing Wildlife Spatial Behaviors in 3D’ led by the mkde R-package authors Dr. Jeff Tracey and myself (see preview on page 6). Given the popularity of previous spatial ecology workshops and the increasing use of R in our field we expect similar high demand for this 3D R tutorial, so get in early!

Thanks to those who submitted articles for this issue of our newsletter - and thanks to the SETWG membership for your continued support and interest in the Working Group. If you would like your research published in Remotely Wild, please feel free to email us your work for consideration.

Best regards, James K. Sheppard

Remotely Wild

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WHERE IN THE WORLD ARE SETWG MEMBERS . . ?

Balanceasof1/1/2015 $5,839.63

2015TotalIncome $788.10

2015TotalExpenses $2,010.10

Balanceasof12/31/2015 $4,617.63

Currentbalance $5,105.63

Membership 176

TWS Spatial Ecology & Telemetry Working GroupTreasurer’s Report: 2016

SETWG Executive team update: SETWG is pleased to announce that Alex Wolf has joined our executive team as Secretary.  Alex graduated with a BS in Evolution, Ecology & Behavior from Beloit College in 2006 and an MS from the Cooperative Wildlife Research Lab at Southern Illinois University in 2012.  His professional experience includes two years working with invasive pythons in the Everglades for the University of Florida and National Park Service before graduate school and three years with the Missouri Dept of Conservation. Alex has recently accepted a position at the Cary Institute of Ecosystem Studies in NY.

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2016 Student & Young Professional Travel Awards

TheSpaHalEcologyandTelemetryWorkingGroupoftheWildlifeSocietyissoliciHngapplicaHonsfor$500travelawardstoaQendtheWildlifeSociety’sAnnual2016ConferenceinRaleigh,NC.Atotaloffourawardswillbeprovidedinthefollowingthreecategories:

2GraduateStudentawards:Mustbeacurrentgraduatestudentorhavegraduatedin2016.

1UndergraduateStudentaward:Mustbeacurrentundergraduatestudentorhavegraduatedin2016.

1YoungProfessionalaward:Musthavegraduatedfromundergraduateorgraduateschoolwithintheprevious2years.

Awardguidelines:

IndividualapplicantsmustbeamemberofTheWildlifeSocietyatthenaHonallevel.MembershipoftheSpaHalEcologyandTelemetryWorkingGroupisnotrequired(althoughitisencouraged!).GraduatestudentandyoungprofessionalapplicantsmustbepresenHngaposterand/ororalpresentaHonattheconference.PreferencewillbegiventoapplicantswhoseresearchemphasizesGIS,remotesensing,ortelemetry.UndergraduateapplicantsarenotrequiredtopresentbutshouldhaveresearchinterestsorexperienceintheareasofGIS,remotesensingortelemetry.TravelawardswillnotbepresentedtoapplicantswhohavealreadyreceivedatravelawardfromtheWildlifeSocietyoranotherworkinggroupin2016.AsacondiHonofthetravelaward,recipientswillbeaskedtowriteashortarHcledescribingtheirresearchforourSETWGnewsleQer.Howtoapply:

ApplicantsmustsendacopyoftheirpresentaHonabstract(graduateandyoungprofessionalapplicants)oradescripHonoftheirresearchinterests(undergraduateapplicants),abrief1-pageCV,anda1-pageleQerstaHngtheirprofessionalinterestsandwhytheyshouldbeconsideredfortheawardtoSETWGChair:JamesSheppard([email protected]).MakesuretomenHonwhichtravelgrantyouareapplyingfor.

TheapplicaHondeadlineisAugust5,2016.AwardrecipientswillbenoHfiedbyAugust19,2016.

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WORKSHOP RECAP:Rhr: a package for home range estimation with

a graphical user interface

Johannes Signer and Dr. Niko Balkenhol

22nd Annual TWS Meeting, Winnipeg (2015)

RapiddevelopmentsinwildlifebiotelemetrytechnologieshaveenabledthecollecHonoflargehigh-resoluHonlocaHondatasets,whichhavebeenmatchedbyadvancesinspaHalanalyHcaltechniquesandcompuHngpower-wildlifeprofessionalsincreasinglyrecognizethevalueofmodelingskillsforopHmizingthemanagementandanalysisofspaHaldata.Recognizingthis,SETWGsponsoredasold-outworkshopatthe2015WildlifeSocietyconferenceonthe“Rhr”packageavailablefortheRsofware,whichoffersfuncHonsandmethodstoenablewildlifeprofessionalstosuccessfullyconductspaHalanalysisandmodelingoftheirtelemetrydata.

TheRhrworkshopwasledbytheR-packageauthors,JohannesSignerandDr.NikoBalkenhol,UniversityofGoeingen

HomerangesareofenusedtoanalyzetrackingdataoriginaHngfromGPStelemetry.Unfortunately,thevarietyofmethodsavailableforhomerangeesHmaHonalsomakesitdifficulttoobjecHvelyevaluatepublishedresultsofmanyhomerangestudies.Thisisbecauseresultsandparametervaluesofhomerangeanalysesareofennotreportedadequately,andimportantanalyHcalstepsareofenmissing(LaverandKelly2008).Consequently,LaverandKelly(2008)urgedresearcherstoconductcertainanalyHcalstepsbeforeactualhomerangeanalyses,andrequestedminimumeditorialstandardsforreporHnghomerangeanalyses.FortheesHmaHonsofhomerangesseveralsofwareproductsandextensionsforGeographicInformaHonSystems(GIS)areavailable.However,currentsofwaresoluHonsareofenclosed-sourceandrequirecommerciallicensesorrequireprogrammingskills,whichnoteverywildlifemanagerorstudenthas.ToimprovethecurrentsituaHonandtoprovideasofwareplaoormimplemenHngtherecommendaHonsofLaverandKelly(2008),wepresentanewRpackage,rhr(reproduciblehomeranges;SignerandBalkenhol2015),thatenablesuserstoperformhomerangeanalysesusingthemostcommonesHmatorsandkeeptrackofallanalyHcalsteps,parametervalues,andresults.

TherhrpackagerunsenHrelywithinprogramRandprovidesagraphicaluserinterfacethatrunswithinthewebbrowser.AtthemomenttherhrpackageprovidesaccesstoassessitefidelityandHmetostaHsHcalindependence,esHmatehomerangeswithminimumconvexpolygons,kerneldensityesHmaHon,localconvexhulls,Jennerich-TurnerEllipses,BrownianBridgeMovementmodelandanareaindependentesHmaHonofcoreareas.InaddiHon,therhrpackagehassomedatamanagementcapabiliHes,e.g.,theusercanselectamongdifferentanimalswhichonestoincludeinananalysis,temporalandspaHalsubsetscanbeperformedandthecoordinatereferencesystemofrelocaHonscanbeadjusted.Finallytherhrpackageproducesareportattheendofeachanalysiswithasummaryofthemainfindingandallparametervaluesusedduringtheanalyses.SpaHalresults(e.g.,Shapefilesofhome-rangeisoplethsorrasterswithuHlizaHondistribuHons)arewriQenautomaHcallytoatemporaryorusespecifieddirectoryandcanbeusedforfurtherprocessinginotherGeographicInformaHonSystems.

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Tousethepackage,R(Version3.1orhigher)andamoderninterbrowserisrequired.DetailedinstrucHonshowtoinstallandusethepackageareavailableonthepackagewebsite:hQp://rhr.spamwell.net

OncethepackageisinstalledandloadedintoR,thegraphicaluserinterfacecanbestartedwithasinglecommand.DataontherelocaHonofanimalscanthanbeloadedfromdeliminatorseparatedtextfiles(e.g.,csvfiles).

Amailingisavailable(hQps://listserv.gwdg.de/mailman/lisHnfo/rhr-discussion)forfurtherdiscussion,bugreportsandfeaturerequests.

Literaturecited

Laver,P.N.,andM.J.Kelly.2008.AcriHcalreviewofhomerangestudies.JournalofWildlifeManagement72:290–298.

Signer,J.,andN.Balkenhol.2015.Reproduciblehomeranges(rhr):Anew,user-friendlyRpackageforanalysesofwildlifetelemetrydata.WildlifeSocietyBulleHninpress.

Figure 1: The user can upload deliminator separated text files and specify field separators among other things (panel A). Each analytical method has set of options that can be set (kernel density estimation is shown as an example in B). Finally the user can select which analytical steps to include in one run (panel C).

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WORKSHOP PREVIEW

Modeling & Visualizing Wildlife Spatial Behaviors in 3D

Dr. Jeff Tracey1 & Dr. James Sheppard2

23rd Annual TWS Meeting, Raleigh NC (October 15 – 19, 2016)

1. U.S. Geological Survey, San Diego Field Station, Western Ecological Research Center

2. San Diego Zoo Institute for Conservation Research

AdvancesindigitalbiotelemetrytechnologiesareenablingthecollecHonofbiggerandmoreaccuratedataonthemovementsoffree-rangingwildlifeinspaceandHme.ThecoevoluHonofbiologgerswithhomerangeesHmatorsisbringinginferencesonanimalspaceuseclosertobiologicalreality.However,currentesHmatorsfailtocapitalizeonthe3DprofilesofferedbymodernGPSbiotelemetrydatasets.Animalspace-useismulH-dimensionalandcanbecharacterizedwithintwoxandyplanarspaHaldimensions,aswellasaz-dimensionrepresenHngalHtude(forflyingorarborealspecies),elevaHon(forterrestrialspecies),ordepth(for

aquaHcspecies).Althoughmanybiotelemetrydevicesrecord3DlocaHondatawithx,y,andzcoordinatesfromtrackedanimals,thethirdzcoordinateistypicallynotintegratedintostudiesofanimalspaHaluse.DisregardingthezdimensiongreatlylimitsourunderstandingoftheverHcalcomponentofanimalrangingpaQernsandrestrictsourabilitytodefineandpredicthowanimalsmovethroughlandscapesandselectandusehabitats.TradiHonal2DhomerangeesHmatorsmayalsomisrepresentthespaceuseofanimalsthatoccupyhabitatswithastrongverHcalcomponent.

Thisworkshopwillpresentnovel3Dmovement-basedkerneldensityesHmatorsandcomputervisualizaHontoolsforgeneraHngandexploringwildlife3DhomerangesbasedonbiotelemetrylocaHondata.TheapplicaHonandvalueoftheseesHmatorswillbedemonstratedusingbiotelemetrydataacquiredfromendangeredanimalsthatoccupyaerial,terrestrial,andaquaHcspaHaldomains.Theworkshopwillexplainhowthese3Dmethodswork,discusstheirprosandconsrelaHvetoothermethods,andgostep-by-stepthroughthefreelyavailablemkdepackageforR.hQps://cran.r-project.org/web/packages/mkde/index.html

CasestudieswillbeusedtodemonstratetheecologicalinsightsandconservaHonmanagementbenefitsprovidedby3DhomerangeesHmaHonandvisualizaHonforterrestrial,aquaHc,andavianwildliferesearch.ThisisnotastaHsHcsormodelingworkshop,butwillprovidewildlifeprofessionalswithhands-ontoolsandskillstofacilitateenhanced3DvisualizaHonandanalysisofwildlifebiotelemetrydata.

KeepuptodatewithconferenceworkshopsattheWildlifeSociety2016conferencewebsite:

hQp://www.twsconference.org/workshops/

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SETWG Awards 2016

TheSpaHalEcologyandTelemetryWorkingGroupisexcitedtoannouncethe2016recipientsofSETWGAwardsthatrecognizeprofessionalsinthefieldofGISorTelemetrywhohavemadesignificantcontribuHonstothefieldofwildlifebiology.

Awardrecipientsdonotneedtobewildlifebiologistsoreveninvolvedinanyenvironmentalresearchormanagement.TheyonlyneedtohavewriQenorproducedsomething,orprovidedsomeservicethathassubstanHallyimprovedourabilitytodoourjobandenabledustodothingswemaynothavebeenabletodobefore.Althoughourawardsdonotincludeanykindofcashprize,theyareawayforus,asaprofessionalsociety,tosaythankyoutotheseindividualsforthehelptheyhavegivenus.

AllindividualslistedbelowhavebeenawardedCerHficatesofAppreciaHonfromourworkinggroup,andsentleQersthankingthemforthetremendousservicetheyhaveprovidedtoourprofession.Thankyoutothosememberswhonominatedthisyear’swinners-IfyouwouldliketonominateaindividualororganizaHonthatyoufeelshouldbeconsideredforrecogniHonbySETWGwewouldlovetohearfromyou.PleasesendallnominaHonstotheworkinggroupawardscommiQeechairAlexWolf([email protected]).

JoinusincongratulaHngthefollowing2016SETWGawardees!

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HRT: Home Range Tools for ArcGIS®

A.R. Rodgers, J.G. Kie, D. Wright, H.L. Beyer, and A.P. Carr

Centre for Northern Forest Ecosystem Research, Ontario Ministry of Natural Resources

http://flash.lakeheadu.ca/~arodgers/hre/

The HRT is an extension for ArcGIS to analyze animal home ranges developed by Arthur R. Rodgers at the Ontario Ministry of Natural Resources and a group of collaborators. In addition to home range calculations using several methods, the program supports calculation of other movement statistics and has a data animation tool.

The HRT contains software that extends ArcGIS to analyze home ranges of animals. The ability to use large data sets and carry out all required home range analyses within a single software environment were the primary reasons for developing the HRT for ArcGIS. The programs have been written for novice GIS users who already understand basic wildlife telemetry issues and who are familiar with the concept of a "home range".

The HRT include 2 home range analysis models: minimum convex polygons (MCPs) and kernel methods. The HRT for ArcGIS provides raster output and batch processing of kernel analyses for multiple animals.

ArcMET: Movement Ecology Tools for ArcGIS®

Jake Wall

Colorado State University

Department of Fish, Wildlife and Conservation Biology

http://www.movementecology.net

ArcMET (Movement Ecology Tools for ArcGIS) is a package of tools for analyses within the fields of movement ecology and wildlife conservation. At present, these tools comprise 6 major categories:

1. Filter: tools for filtering and temporal segmentation of movement data

2. Trajectory: tools that operate on or calculate aspects of an animal's trajectory

3. Range: tools for calculating the range of an animal based on its sampled trajectory

4. UD: tools for calculating the utilization distribution of an animal based on its sampled trajectory

5. Covariate: tools for linking movement data with covariate information

6. Utilities: various utility tools

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Zippingacrosstheworld,asinglecountry,orevenaspecificregion,thediversityoftopographyandspaHalchangeisoutstanding:we’rewitnesstoshifsfromtheaquaHctotheterrestrial,streamtoriverdelta,mountaintovalley,foresttograssland,urbantorural.Thenzoomintoaveryspecificfragmentofaplace–say,a10-kmsliceofCalifornia–you’llsHllfindtremendousspaHalcomplexity,withpatchesofcropland,emergentmarshwetland,Redwoodforest,andthefoothillsoftheSierras,allintricatelyentwinedwithinanurban-metropolitanmatrix.AcentralthemeinwildlifebiologyishowthespaHalpaQernsofalandscape–atamyriadofscales–canaffectthebehavior,survival,andreproducHonofwildlife.Oneofthemostcommonandperhaps,mostchallengingwaysofansweringspaHalecologyquesHonsistostudyanimalmovementbehavior.Whenfacedwithecotoneshifs,hardedges,andhumandevelopment,howdoanimalscope?Howmightcorridorsfacilitatemovementandaccesstohabitatorfoodresourcesthatwouldotherwisebeunavailable?TheseandcountlessotherquesHonsareattheforefrontoflandscapeecologyandlargelyremainassomeofthemostdifficultquesHonstoanswerinhighlymobilespeciesofwildlife.

Emily J. Williams1,2 & W. Alice Boyle1

1. Division of Biology, Kansas State University 2. Email: [email protected]

Hard travelin’ down the Prairie: within-season breeding dispersal in a declining grassland songbird

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OnerelaHvelywell-studiedanimalmovementbehaviorisbreedingdispersal,orthemovementanadultmakesbetweensuccessivebreedingaQempts.Theabilityofanimalstoreturntoabreedingarea,orsitefaithfulness,isawell-documentedtraitamongmanyspeciesofbirdsandmammals(Greenwood1980).Oneofthemostremarkableexamplesofthisisinmigratorybirds:manyspeciestravelthousandsofkilometersinasingleyear,butthenreturnbacktothesameterritory,thesametree,oreventothesameinfinitesimaldepressionoflichenandbearberryonthetundra.

Thebenefitsofsitefaithfulnessaremanifold:theterritorialsexofencansecureahigherqualityterritory,canmoreeasilyclimbthesocialhierarchy,reapthebenefitsofpastexperienceinknowingthearea,andsaveenergy,asmovingtoanewsiteisenergeHcallycostlyandhasinherentunknownrisk.Theideathatindividualswouldbefaithfultotheirbreedingsiteisnosurprise;thefitnesspayoffsofenoutweightherisksofmovingtoanewterritoryorbreedingarea.However,althoughsitefidelitymaybetheparadigminthebirdworld,therearemanycasesofindividualsdoingtheexactopposite.Somebirdsdispersetoanewbreedingareawithinasinglebreedingseason,ortraveltoanewsitethefollowingyear.Providedthatsitefidelityissocommon,theindividualsnotperformingthis“normal”behaviormakethemtheoddones,andpromptthequesHon,“why?!”

Birdsthatoccupystablehabitatswithin-seasonandacrossyearsarefrequentlyhighlysite-faithful,suchasBlack-throatedBlueWarblersbreedinginthemixeddeciduouswoodlandsofNewHampshire(Clineetal.2013),PipingPloversbreedingalongriversandbarsinsoutheastSouthDakota(Friedrichetal.2015),orPacificGoldenPloversbreedinginthelowlandtundraofnorthernAlaska(Colwell2010).Althoughallofthesehabitatsfaceseasonalchangesintemperature,precipitaHon,andleaf-out,thestructureandcomposiHonoftheplantcommunityandthesubstratesuponwhichbirdsnestchangesliQle.Incontrast,growingevidenceforsiteinfidelityiscomingfrombirdsthatbreedinareascharacterizedbygreatspaHalandtemporalvariability.ThesepaQernssuggestthattheinherentvariabilitythattypifytheselandscapesinfluencesavianinhabitantstocapitalizeonmulHplebreedingareas,asaparHcularlocaHoncanradicallychangeoverspaceandHme.

Fig.1(leE):EmilyWilliams(MastersstudentatKSU)collecHngafeathersamplefromafemaleGrasshopperSparrowatKonzaPrairieBiologicalStaHon,KS.

Fig.2(above):Color-bandedmaleGrasshopperSparrowperchedonafenceline.Thisindividualisknownas“YY-SK.”PhotobyDaveRintoul.

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Variabilityisadefiningfeatureofgrasslands.TheyarelandscapesthatcandramaHcallychangeoverthecourseofthebreedingseason,andfromyeartoyear.Grasslandsarecharacterizedbyincrediblevariabilityinclimate,withhighandstrongseasonalpaQernsofannualrainfall(CV=25%)(Knapp1998).GrasslandsalsoexperienceinterannualvariabilityinprecipitaHon(Nippertetal.2006).Coupledwithinter-andintra-annualchangesclimate,theinteracHveeffectsoffireandgrazingcreateadynamiclandscapethatchangesconsiderablyfromearlytolateseason(Fig.3)(Fuhlendorfetal.2009).

Fig.3:A)ungrazedpastureburnedearlySpring,B)samepastureinlateSummerC)grazedpastureoneyearsinceburninearlySpringD)samepastureinlateSummer.ImagestakenatKonzaPrairieBiologicalStaHon,KS.

Theeffectsofgrazing,burning,andseasonalchangesinclimatecanhaveprofoundeffectsontheseQlementandnesHngdecisionsofbirdsthatrelyuponvariablelandscapes.Anecdotalevidencesuggeststhatgrasslandbirdsadoptamoremobilestrategyinthefaceofconstantflux:theEurasianHoopoe,whichbreedsacrosssavannaandsteppeenvironmentsacrossEurope,Asia,andNorthAfrica(Bötschetal.2012),theRed-billedQuelea,whichbreedswithinthesavannaandsteppeofSub-SaharanAfrica(Jaegeretal.1986),andtheSedgeWren,whichbreedswithinshortgrassmarshandtallgrassprairieacrosstheeasternhalfoftheUnitedStates(Robbins2015).Foreachofthesespecies,publisheddescripHonsoftheirbreedingmovementpaQernsincludepotenHalevidenceforhaving“dual-breedingranges,”asthedistancesbetweensuccessivebreedingaQemptsarevast.OurownstudyofGrasshopperSparrowswithintheFlintHillsregionofeasternKansasnowrevealsthatmanyindividualsshifterritoriesandsomeHmesdisperseoversmallerdistancesofupto9kmbetweenbreedingaQemptswithinthesamebreedingseason(WilliamsandBoyle,inprep).ThisintriguingpaQernofmovementledustoaskthequesHons,“whataffectsthedecisiontodisperse,andthedecisiononwheretoseQlenext?”

ToanswerthesequesHons,wehypothesizedthatdispersalandsubsequentseQlementdecisionsmaybeshapedbyspaHalandtemporalvariaHoninpredaHonornestparasiHsmrisk,foodavailability,andnestmicrohabitatquality.Totestthesehypotheses,westudiedGrasshopperSparrowsbetweenMay–August2013-15attheKansasStateUniversity(KSU)KonzaPrairieBiologicalStaHonandExperimentalRangeUnit(Fig.4),locatedinthenorthernFlintHillsofeasternKansas.KonzaPrairieisa3,487-hatractoftallgrassprairieco-ownedbyKansasStateUniversityandTheNatureConservancy.TheKonzaisexperimentallymanagedwithvaryinggrazingandburningtreatments,wheregrazingvariesbybison,caQle,ornograzingtreatments,andprescribedburnsoccuronanannualtoevery2,3,4,and20-yearbasis.TheKSUExperimentalRangeUnitiscomposedofsix24.3-hapasturesmanagedwith“intensiveearlystocking,”wherepasturesareburnedinlateAprilandheavilystockedwithcaQle(Owensbyetal.1988).

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Wecaptured,color-banded,andmonitoredGrasshopperSparrowsinarandomly-located10-haplotwithin18watershedsinreplicatedcombinaHonsofa)grazingornograzing,b)annualspringburnsortwo-yearburns,c)intensiveearlystockingandd)“patch-burn”plotsmanagedwithathree-yearrotaHonalburnregimeincombinaHonwithwarm-seasoncaQlegrazing(Fig.4).Wesearchedfornests2-4days/weekbyusingbehavioralobservaHonsandropedraggingtoflushfemalesoffofnests,andmonitorednestsevery2daysunHlthenestfailedorfledged.Tomonitordispersaleventsandmeasuredispersaldistances,wemappedterritoriesofallindividualsevery7-14dayswithineachwatershed,andconductedradio-telemetryonmalesatnestsforwhichwehadstrongconfidencewerethenestfathers.Weconsideredsparrowsasdispersediftheya)displayedterritorialbehavior>100mawayfromtheiroriginalterritoryornestlocaHon,orb)werenotresightedattheiriniHalterritory>1-weekafernestcompleHon.GrasshopperSparrowterritoriesrangeinsizefrom0.36ha–0.81haacrosstheirbreedingrange,andare~0.16haintallgrassprairie–soour100mcutoffiswelloutsidetherangeofsparrowterritoriesencounteredattheKonzaPrairie.

Figure4:PanelAshowsthelocaHonofKonzawithintheFlintHills,andpanelBshowsthewatershed-leveltreatmentsontheKonzawithlocaHonsof10-haplotsmarkedbyyellowsquares.ThetwoplotsoffKonzaarelocatedwithintheKSUExperimentalRangeUnit.

Figure5:MaleGrasshopperSparrowfiQedwitharadio-transmiQer.

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FromMay-July2013-2015,wecolor-bandedatotalof779adultGrasshopperSparrows(males=647,females=132)andradio-tagged20individualsbetweenMay-July14-15.Weobserved148dispersals,withmostinstancesofdispersalrecognizedbyresightsurveys(N=139,telemetry=9).Dispersaldistancesrangedfrom0.101–8.94kmfromfirstandsecondterritoriesornests(Fig.6).Datafrom2014indicatethatterritorydensiHesinwatershedsmanageddifferentlyexhibitdifferenttemporaltrajectories(Fig.7).WeobservedconsistentseasonalshifsinhabitatselecHon,withhigherdensiHesofsparrowsoncaQle-grazedplots(e.g.,patch-burnandearly-intensivestocking)thaninungrazedorbison-grazedwatersheds.Ingeneral,caQle-grazedplotshadconsistentlyhigherdensiHesthanungrazedorbison-grazedwatershedsthroughouttheenHreseasonacrossyears.ThesepaQernsofdynamichabitatselecHonagreewithrecentpublishedstudiesindicaHnghigherGrasshopperSparrowabundancesinareasmanagedwithfireandgrazing(Hovicketal.2014).

Throughoutthisstudy,ourobjecHvesare:1)toprovidethefirstcomprehensivedescripHonofthespaHalandtemporalpaQernsofwithin-seasonbreedingdispersalinagrasslandbird,andto2)invesHgatetheecologicalcausesandpotenHaladapHveconsequencesofwithin-seasonbreedingdispersal,byexplainingthefactorsthatshapetheiniHaldecisiontodisperseandsubsequentseQlementdecisionsfollowingdispersal.Althoughourstudywillprovidethefirstdetailed,populaHon-leveldescripHonofwithin-seasonbreedingdispersalinagrasslandbird,thiswithin-seasonmovement,despitethedearthofpublishedevidence,maybemorecommonthanwemightexpect.Withinseverallifehistoryaccountsofgrassland-obligatemigratorybirds,includingtheSedgeWren,Cassin’sSparrow,andHenslow’sSparrow,thesespeciesareofencharacterizedashaving“fluid”territoriesandhave“erraHc”habitsthatinevitablyresultinlargegapsintheirannuallifecycle(Dunningetal.1999,Herkertetal.2001,Herkertetal.2002).It’slikelythesespecieshavedevelopedamoremobilestrategytoadapttotheconstantlychangingenvironmentalcondiHons;arequisiteintemporallyandspaHallyvariableenvironmentssuchasgrasslands.

Figure6:RangeofdispersaldistancesofGrasshopperSparrowsatKonzaandtheKSUExperimentalUnit.

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WecurrentlyknowrelaHvelyliQleofwhetherwithin-seasonbreedingdispersaloccurswithinthesespecies,andfurther,lackevenbasicknowledgeofthepaQernsandexplanaHonsforthisbehavior.WithoutthisspaHalinformaHon,wecannotinvesHgatetheecologicalandevoluHonarybasisforwithin-seasonbreedingdispersalbehavior,norcanweconstructcompletedemographicmodelsnecessaryforesHmaHngsurvival,idenHfyinglifestagesresponsiblefordeclines,andprojecHngfuturepopulaHontrajectories.Sincegrasslandbirdshavesufferedthelargestdeclinesoutofanyotheravianguild(Saueretal.2014),idenHfyingtheinstances,paQerns,andcausesofwithin-seasonbreedingdispersaldecisionsofthesespeciesisthuscriHcallyimportanttoeffecHvelymanagethemandprecludefurtherdeclines.

Onthewhole,toanswerthosetoughquesHonsinwildlifebiologycanbeincrediblychallenging,especiallywhenitinvolvesfollowinghighlymobileanimalsthathaveatendencytobesitefaith-lessandunpredictable.Nonetheless,studyingbreedingdispersal–orsomeotherkindofanimalmovement–isonestepclosertogreaterunderstandingofhowwildlifereactandrespondtospaHalpaQernsthatareconstantlychangingoverHme.Althoughcomplexitybringswithitsomeformidablechallenges,variabilityiswhatmakesitinteresHng.Aferall,varia3onisthespiceoflife!(KruglyakandNickerson2001).

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Figure7:ChangesindensityofterritorialGrasshopperSparrowswithinwatershedsfromearly(May)tolateseason(midJune–July).

LiteratureCitedBötsch,Y.,R.ArleQaz,andM.Schaub.2012.BreedingDispersalofEurasianHoopoes(Upupaepops)withinandbetweenYearsinRelaHontoReproducHveSuccess,Sex,andAge.Auk129:283-295.

Cline,M.H.,A.M.Strong,T.S.SilleQ,N.L.Rodenhouse,andR.T.Holmes.2013.Correlatesandconsequencesofbreedingdispersalinmigratorysongbird.Auk130:742-752.

Colwell,M.2010.ShorebirdEcology,ConservaHon,andManagement.UniversityofCaliforniaPress,Berkeley,CA.

Dunning,J.,J.B.,R.K.Bowers,S.J.Suter,andC.E.Bock.1999.Cassin'sSparrow(Peucaeacassinii).inP.Rodewald,editor.BirdsofNorthAmericaOnline.CornellLabofOrnithology,Ithaca,NY.

Friedrich,M.J.,K.L.Hunt,D.H.Catlin,andJ.D.Fraser.2015.Theimportanceofsitetomatechoice:MateandsitefidelityinPipingPlovers.Auk132:265-276.

Fuhlendorf,S.D.,D.M.Engle,J.Kerby,andR.Hamilton.2009.PyricHerbivory:RewildingLandscapesthroughtheRecouplingofFireandGrazing.ConservaHonBiology23:588-598.

Greenwood,P.J.1980.MaHngsystems,philopatryanddispersalinbirdsandmammals.AnimalBehaviour28:1140-1162.

Herkert,J.R.,D.E.Kroodsma,andJ.P.Gibbs.2001.SedgeWren(Cistothorusplatensis).inP.Rodewald,editor.BirdsofNorthAmericaOnline.CornellLabofOrnithology,Ithaca,NY.

Herkert,J.R.,J.A.Vickery,andD.E.Kroodsma.2002.Henslow'sSparrow(Ammodramushenslowii).inP.Rodewald,editor.BirdsofNorthAmericaOnline.CornellLabofOrnithology,Ithaca,NY.

Hovick,T.J.,R.D.Elmore,andS.D.Fuhlendorf.2014.Structuralheterogeneityincreasesdiversityofnon-breedinggrasslandbirds.Ecosphere5:13.

Jaeger,M.M.,R.L.Bruggers,B.E.Johns,andW.A.Erickson.1986.EvidenceofiHnerantbreedingoftheRed-billedQuelea(Queleaquelea)intheEthiopianRif-ValleyIbis128:469-482.

Knapp,A.K.,Briggs,J.M,HartneQ,D.C.,Collins,S.L.1998.GrasslandDynamics:Long-TermEcologicalResearchinTallgrassPrairie.OxfordUniversityPress,NewYork,NY.

Kruglyak,L.,andD.A.Nickerson.2001.VariaHonisthespiceoflife.NatureGeneHcs27:234-236.

Nippert,J.B.,A.K.Knapp,andJ.M.Briggs.2006.Intra-annualrainfallvariabilityandgrasslandproducHvity:canthepastpredictthefuture?PlantEcology184:65-74.

Owensby,C.E.,R.Cochran,andE.F.Smith.1988.StockingRateEffectsonIntensive-EarlyStockedFlintHillsBluestemRange.JournalofRangeManagement41:483-487.

Robbins,M.B.2015.Intra-SummerMovementandProbableDualBreedingoftheEasternMarshWren(Cistothorusp.palustris);aCistothorusAncestralTrait?TheWilsonJournalofOrnithology127:494-498.

Sauer,J.R.,J.E.Hines,J.Fallon,K.L.Pardieck,D.J.ZiolkowskiJr.,andW.A.Link.2014.

TheNorthAmericanBreedingBirdSurvey,resultsandanalysis1966-2013.Version01.30.2015.USGSPatuxentWildlifeResearchCenter,Laurel,MD.

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An autonomous GPS geofence alert system to curtail avian fatalities at wind farms

JamesK.Shepparda,AndrewMcGannb,MichaelLanzoneb,RonaldR.Swaisgooda

a. San Diego Zoo Global, Institute for Conservation Research

b. Cellular Tracking Technologies LLC, Somerset, Pennsylvania, United States of America

Email: [email protected]

Windenergydevelopmentscon3nuetoproliferategloballyasna3onsseekcleanandrenewablealterna3veenergysourcestofossilfuels.However,windfarmsdonotcomewithoutenvironmentalcosts[1].Agrowingliteratureisdocumen3ngtheseriousimpactsthatwindfarmscanhaveonresidentandmigratoryavifaunapopula3onsthroughmortali3esfromdirectcollisionswithturbines[2,3].Recentes3matesindicatethatwindfarmsinNorthAmericaareresponsibleforupto368,000birdfatali3esannually[4].Consequently,thedevelopmentandimplementa3onofeffec3vemeasurestoreducewindenergyimpactsonwildlifeisrecognizedasatopprioritybybiologists,conserva3onorganiza3ons,regulatorsandtheprivatesector(seereviewsby[5-7]).Wedevelopedanewgeofence-basedbiotelemetricsystemtominimizecollisionrisks,par3cularlyforthreatenedandendangeredbirdspecieswhoserangesoverlapwithcurrentandfuturewindfarmsites.

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M. Mirinha/STRIX Nature doi:10.1038/nature.2012.10214

Ageofenceisavirtualboundarydelineatedaroundanareaofinterestthattriggers:1)Acuetothetelemeteredanimal(e.g.electricshock);2)achangeinthelocaHonfixrateaQemptedbytheunit,or;3)analerttomanagerswhenevertheanimalcrossestheboundaryedge.GeofencesareincreasinglyrecognizedasaneffecHveplaoormtoenhancethespaHotemporalflexibilityofwildlifemanagement.Forexample,geofenceshavebeensuccessfullyintegratedintothemanagementofmammalianpopulaHonsthatcomeintoconflictwithoraredisturbedbyhumanacHviHes,suchaselephantsandwolves(seereviewby[8]).However,unHlnowgeofencealerttechnologyhasbeenprohibiHvelytoolargeortoocomplextoincorporateintoavianbiotelemetry.

Wedevelopedanautonomousalertsystemthatsuccessfullyminiaturizesandintegratesvirtualgeofencecapabilityintosolar-poweredbiotelemetrydevicesusedtotrackspeciesoflargebirdscurrentlyimpactedbywindfarms,suchascranesandraptors.TheseunitscombineaGPSreceiverwithaGSMcommunicaHonssystemthattransmitsacquiredhigh-resoluHonlocaHondataviacellularnetworksinnearreal-Hme.Customsizedgeofencescanbeplacedaroundwindfarms.WhenatelemeteredbirdingressesoneofthesevirtualboundariestheGPSlocaHonfixratedecreasesfrom15-minto30-secandanSMSalertisautomaHcallytransmiQedtoausergroupwithin2-min.Whenthebirdegressesthegeofencezone,asecondalertissentandthefixratereturnsto15-mintoconservetransmiQerenergyanddataacquisiHoncosts.

Combining:1)GPSlevelaccuracy;2)highlocaHonfixsamplingrates;3)locaHondatareceivedinnearrealHme,and:4)automatedSMSalertsintoanintegratedandflexiblegeofencebiotelemetrysystemwillprovideconservaHonmanagersandwindfarmoperatorswithsufficientwarningandHmetoimplementappropriatemiHgaHveacHonstopreventaviancollisionmortaliHesassociatedwithwindturbinecollisions.TheflexibilityofthissystemwillenableuserstocustomizethelocaHonsanddimensionsoftheirgeofencesandassociatedalertseingstomeetthemanagementchallengesspecifictoeachwindenergydevelopmentandthemovementbehaviorsofspeciesofconcern.

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Fig.2:DemonstraHonofthegeofencealertsystemplacedaroundahypotheHcalwindfarm.Atelemeteredbirdingressesthegeofenceboundary(toplef),triggeringanSMSalertandincreasingtheGPSfixratefrom15minutes(greendots)to30seconds(reddots).Whenthebirdegressesthegeofencezone(lowerright)asecondSMSalertisbroadcastandthefixratereturnstothestandard15minutes.

Fig.1:SolarpoweredGSM-GPSgeofenceaviantransmiQer.TelemetrydeviceisaQachedtothewingofafree-rangingCaliforniacondorviapatagialmountwithanIDtag.

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UsersmustcarefullyconsiderthelocaHonanddimensionsoftheirgeofencesifthesystemistoprovidereliablealerts.Forexample,geofencesdeployedtoprovidealertsoffastflyingeagleswillhavetobesetapartatgreaterdistancesaroundawindfarmthanthosesettoprovidealertsofslowerflyingcranes.CurrentlimitaHonstothisalertsystemincludeitsweight(whichprecludesitsdeploymentonbatsandsmallbirds),thesolarpowersystem(whichgenerallyrestrictsoperaHontodayHmewithoutlongperiodsofinclementweather)andthenecessityofhavingtocapturebirdstofitthemwiththebiotelemetrysystemandrecapturebirdswhosetelemetryunitsneedreplacing.

TheperformanceofthesystemmayalsobeimpededifGSMnetworkcoverageinremoteregionswheretheunitsaredeployedisverypatchy,althoughaddiHonalcoverageisofeninstalledaroundwindenergysitesduringconstrucHon.Muchofthetechnologythathasbeendevelopedandincorporatedintothisgeofencesystemiscuing-edgeandnovel,soitsperformancewillnotbeabletobetrulygaugedunHlithasbeensuccessfullydeployedacrossmulHplespeciesandfieldseings.DespitetheselimitaHons,wefeelitoffersahighlypromisingcost-effecHvesoluHontomiHgaHngaviancollisionswithwindturbines.

Acopyoftheopenaccessjournalpaperdescribingthegeofencealertsystemcanbedownloadedhere:

hWp://animalbiotelemetry.biomedcentral.com/arYcles/10.1186/s40317-015-0087-y

Ademovideoofthegeofencealertsystemcanbeviewedhere:

hWps://www.youtube.com/watch?v=2oWodZpmbHo

Recentmediacoverageofthegeofencealertsystem:

hWp://wildtech.mongabay.com/2016/02/can-a-virtual-fence-help-protect-birds-from-human-structures/

LiteratureCited:

1.PremalathaM,AbbasiT,AbbasiS:Windenergy:Increasingdeployment,risingenvironmentalconcerns.Renewableand

SustainableEnergyReviews2014,31:270-288.

2.ArneQEB,BrownW,EricksonWP,FiedlerJK,HamiltonBL,HenryTH,JainA,JohnsonGD,KernsJ,KofordRR:PaQernsofbat

fataliHesatwindenergyfaciliHesinNorthAmerica.TheJournalofWildlifeManagement2008,72:61-78.

3.MarquesAT,BatalhaH,RodriguesS,CostaH,PereiraMJR,FonsecaC,MascarenhasM,BernardinoJ:Understandingbird

collisionsatwindfarms:AnupdatedreviewonthecausesandpossiblemiHgaHonstrategies.BiologicalConservaHon

2014,179:40-52.

4.EricksonWP,WolfeMM,BayKJ,JohnsonDH,GehringJL:AComprehensiveAnalysisofSmall-PasserineFataliHesfromCollision

withTurbinesatWindEnergyFaciliHes.PloSone2014,9:e107491.

5.KuvleskyWP,BrennanLA,MorrisonML,BoydstonKK,BallardBM,BryantFC:Windenergydevelopmentandwildlife

conservaHon:challengesandopportuniHes.Thejournalofwildlifemanagement2007,71:2487-2498.

6.PiorkowskiMD,FarnsworthAJ,FryM,RohrbaughRW,FitzpatrickJW,RosenbergKV:ResearchprioriHesforwindenergyand

migratorywildlife.TheJournalofWildlifeManagement2012,76:451-456.

7.WindturbineinteracHonswithwildlifeandtheirhabitats:asummaryofresearchresultsandpriorityquesHons.[hQp://

awwi.org/resources/summary-of-wind-wildlife-interacHons-2/-secHon-summary-of-windwildlife-interacHons]

8.JachowskiD,SlotowR,MillspaughJ:Goodvirtualfencesmakegoodneighbors:opportuniHesforconservaHon.Animal

ConservaHon2014,17:187-196.

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TheRustyBlackbird(Euphaguscarolinus,RUBL)isamigratorysongbirdthatbreedsinandneartheborealwetlandsofnorthernNewEnglandandCanada.ItislistedasVulnerableontheIUCNRedListandtheUSFishandWildlifeServicehaslistedtheRUBLasaFocalSpeciesofBirdsofManagementConcern.Ourobjec3vewastomodelsingle-seasonRUBLoccupancyasafunc3onofsitecovariates,includingaqua3cinvertebratediversityandabundance.WeassessedbreedingRUBLs’useofbothac3veandinac3vebeaver-influencedwetlandsinCoosCounty,NewHampshireandOxfordCounty,Maine.ThisstudyisthefirstresearchtomodelRUBLoccupancyinthisareaandthefirsttoincludepreyavailabilityasacovariate.FromMaytoJuly,2014,wevisited60sitesthree3mesteachtocollectRUBL

detec3on/non-detec3onsitehistoriesandhabitatdata.Followingeach30minuteRUBLsurvey,wesurveyedaqua3cinvertebratesandrecordedpuddlepresence/absence,percentopenwater,anddetec3on/non-detec3onofcurrentbeaverac3vity.Later,weusedaGIStodigi3zeeachwetlandasapolygon,calculatewetlandsize,andmeasurepercentsofwoodcoverwithina500meterbufferofeachsite.Allwetlanddelinea3onswerebasedonthemostrecentorthoimageryavailableandthencheckedbyaregionalexpert.UsingProgramPresence,wecalculatedRUBLdetectabilityanddeterminedwhichsitecovariatesbestpredictRUBLuseofwetlandsinourstudyarea.UmbagogNa3onalWildlifeRefugewilluseourresultstodevelopaRUBLhabitatassessmentandmonitoringplan.

Foraging habitat characteristics, prey diversity and detectability of breeding Rusty Blackbirds: implications for land and wildlife management in the Northern Forest

Amanda Pachomski1 & Stacy A. McNulty2

SUNY College of Environmental Science and Forestry Syracuse, NY

1 Email: [email protected] 2 Email: [email protected]

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TheRustyBlackbird(Euphaguscarolinus,RUBL)isconsidereda“posterchild”forborealavianspeciesdecline.AlthoughtheRustyBlackbirdwasoncecommon,thespecieshasdeclinedbyanesHmated90%sincethe1960’s(Greenbergetal.2010).Thecauseofthisdeclineisnotknown;climatechange(McClureetal.2012),mercurycontaminaHon(Edmondsetal.2010),hematozoainfecHons(Barnardetal.2010),andHmberharvest(Powelletal.2010)havebeensuggestedaspossiblefactors.Thesoutheasternlimitsofthebird’sbreedingrangeappeartohaveretreatednorthwardandinlandcoincidentwiththepopulaHondecline(McClureetal.2012).Thus,itisimportanttomonitorRUBLstodetectfurtherpopulaHonchangesorrangeshifs.

RUBLsbreedinandnearborealwetlandsfromnorthernNewEnglandandtheMariHmeProvinceswesttoAlaska.RUBLsselectnestsiteswithminimalcanopycoverandhighbasalareaofyoungconifersinNewEngland(Buckley2015).RUBLsnestinbothwetlandanduplandhabitattypes,typicallyinlivespruceorfirtreesthataresurroundedbyregeneraHngconiferstandsorsomeHmesinalderpatchesatwetlandsites.Occasionally,theynestinsnagsorisolatedconifersinwide-openareas.NesHngtreesaresmall,withanaverageheightof2.47mandanaverageDBHof4.14cminNewHampshire(Buckley2013).

Inrecentyears,importantresearchhasbeenconductedonRUBLproducHvityandnesthabitat.But,fewstudieshavefocusedonthespecies’foragingecology.AsbreedingRUBLs’dietconsistsmostlyofaquaHcmacroinvertebrates(Avery1995),theyareawetlandobligatespecies.InnorthernNewEngland,RUBLsprefersiteswithhighwetlandcoverandhighyoungsofwoodcover(Buckley2015).RecentresearchhasalsoshownthatRUBLspreferwetlandsthatarecurrentlyoccupiedbyAmericanbeavers(Castorcanadensis)(Powelletal.2014).These‘ecosystemengineers’createephemeralimpoundmentsofwaterthatRUBLsuseforforaging.However,thespecificsofRUBLdietandforagingecologyarelargelyunknown.ThisisthefirstefforttostudypreyavailabilityforbreedingRUBLs.

MaleRUBLforaging.PhotobyDevonCote.

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TradiHonalavianpoint-countsarenotsufficientforaccuratelydetecHngRustyBlackbirdswithintheirremoteandinaccessiblebreedinggrounds(Greenbergetal.2010).SincedetectabilityislowintheNorthernForestregion(Glennon2010)andtheRUBLisbothrareandofencrypHc,itisimportantforresearcherstoquanHfyourlimitedabilitytodocumentRUBLpresenceandabsence.OccupancysurveymethodscanaccountformisseddetecHonsofsecreHveandrarespecies(MacKenzieetal.2002).

StudyobjecYves,methods,andresults

Ourgoalwastouseoccupancymodelingtomodelsingle-seasonRUBLuseofwetlandsasafuncHonofsitecovariates,includingaquaHcinvertebratediversityandabundance.Powelletal.(2014)conductedthefirststudytomodelRUBLoccupancyofwetlandsinNewEngland.WeaimedtobuilduponthatstudybyadjusHngsurveymethods,addinginpreyavailabilityandabundance,andconducHngthesurveysinadifferentarea.WeassessedbreedingRUBLs’useofbothacHveandinacHvebeaver-influencedborealwetlandsinCoosCounty,NewHampshireandOxfordCounty,Maine.SiteswereeitheronFederallandownedbyUmbagogNaHonalWildlifeRefugeorwereprivatelyownedandmanagedbyWagnerForestManagement,Ltd.ThisremoteareaofNewEnglandisheavilymanaged,withacHveloggingoperaHonsoccurringnearmostofoursites.Thewetlandswesurveyedwereofensurroundedbyspruceandfirtrees;othersiteswereinspeckledalderswampsorwerewithinamixedforest.

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Originally,wewantedtomodeloccupancyinthreeforaginghabitattypes:beaver-influencedwetlands,acidicswamps,andacidicbasinfens.WehadwantedtousetheTNCNortheastHabitatClassificaHonmapstoselectfromthesethreehabitattypes.However,wefoundthatthemapofourstudyareaonlyidenHfiedafewswampsandfens.Asthiswouldnothavegivenusalargeenoughsamplesize,wereducedoursiteselecHontojustbeaver-influencedwetlands.Also,wefoundthatsomeknownwetlandsweren’tmappedasanykindofwetlandhabitatintheTNCclassificaHon,orweremappedaslargerorsmallerthantheyappearinorthoimageryandareontheground.WealsofoundthatthiswastrueforNaHonalLandcoverDataandNaHonalWetlandsInventorymaps.WhilethesegeospaHaldatabasesofferanimmenseamountofhabitatinformaHonandareincrediblyuseful,weneededmoredetailedandfieldmatcheddataforourstudypurposes.SinceborealwetlandsinourstudyareachangeoverHme,especiallywiththeinfluenceofbeavers,wefoundthatthebestwaytomapoursiteswastodigiHzeourownwetlandpolygons.

Usingourpriorknowledgeofthesurveyareaandorthoimagery,weidenHfiedbeaver-influencedwetlandsofpotenHallysuitablehabitat.WeusedArcMaptoselect60sitesfrom263wetlandswithin500metersofaroadandwithina25kmradiusofthetowncenterofErrol,NH.SomesiteswereknowntobeoccupiedbyRUBLsinpreviousyears.Wewantedtorandomlyselectallsites.But,becauseweonlyhadonefieldvehicle,wehadtosurveynearbysitesinpairs.Ifarandomlysiteselectedsitedidn’thaveanotherselectedsitenearby,weaddedanotherrelaHvelynearbywetlandtomakeitlogisHcallypossibletosurvey60sites.UponarrivaltoourfieldstaHon,wediscoveredthatsomeofthedirtloggingroadsthatweneededtotravelonwerenolongerpassable.Theseaccessissuescausedustodropsomepreviouslyselectedsitesandreplacethemwithnewrandomlyselectedwetlands,andthenmanuallyselectsitestomakepairsifnecessary.Intotal,wenon-randomlyselected21ofthe60sites.Wehadpreviouslysurveyed18ofthe60sitesforourpilotstudyin2013.

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FromMaytoJuly,2014,wevisited60wetlandsthreeHmeseachtocollectRUBLwetlandsiteuseandhabitatdata.Tomeasurewithin-seasonchangesinRUBLprobabilityofdetecHon,weconductedsurveysintwoweekintervalsthatalignwithstagesinthebreedingseason:incubaHon(May14toMay27),nesHng(May28toJune10),andfledging(June11toJune24).Weconductedpassive(withoutplayback)detecHon/non-detecHonsurveysforRUBLs.AlthoughpreviousresearchfoundthattheuseofacousHcplaybackincreasedRUBLdetectability(Powelletal.2014),wechosetousepassivesurveysbecausewewantedtorecordthebehaviorofRUBLsanddidn’twantourpresencetofurtherinfluencetheirbehavior.

Duringeach30minuteRUBLsurvey,werecordedRUBLdetecHonhistory,sexofdetectedRUBLS,andwhetherornotthebirdshadbands.Werecordedsurveyspecificvariables,suchaswindspeedandHmeofday,whichmayhaveimpactedourabilitytodetectRUBLs.FollowingeachRUBLsurvey,wesampledaquaHcinvertebratesandrecordedpuddlepresence/absence,percentopenwater,percentexposedmud,anddetecHon/non-detecHonofbeaveracHvity.Later,weusedaGIStodigiHzeeachwetland,calculatewetlandsize,andmeasurepercentsofwoodcoverwithina500meterbufferofeachsiteusing2011NaHonalLandCover.Weusedthe9/18/2013GoogleEarthorthoimagerytodigiHzeeachwetlandasapolygon,usingvisualvegetaHvechangesandonthegroundsurveyexperienceasaguide.

UsingProgramPresence,wecalculatedRUBLdetectabilityanddeterminedwhichsitecovariatesbestpredictRUBLuseofwetlandsinourstudyarea.WedetectedRUBLsatoverhalfofoursites(NaiveoccupancyesHmate=0.5833).AdjusHngforimperfectdetecHons,wefoundthatRUBLswerepresentin61.55%ofoursites(occupancyesHmate=0.6155±0.0689(95%CI:0.4750,0.7391).PreliminaryresultssuggestthatprobabilityofRUBLoccupancyincreaseswiththepresenceofpuddlesincomparisonwiththebasemodelofnopuddles.Aspercentageofexposedmudincreases,occupancydecreases.Althoughthevariable“percentopenwater”wasnotincludedinourtopmodels,ourresultssuggestapreferenceforwaterovermud.ProbabilityofdetecHonwasbestexplainedbysurveyperiod(topmodel)andJulianday(deltaAIC<2).Weexpectedthisresult,asRUBLstendtobeverycrypHcduringnesHngbutmorevocalanddefensivewhilerearingnestlings.WeareintheprocessofreanalyzingourdatausingUnmarkedinProgramR.Wewillpublishourfinalresultsinthefallof2016.

Iamsogratefultohavereceiveda2015SpaHalEcologyandTelemetryWorkingGroupTravelAward,whichallowedmetogivemyfirstconferencepresentaHonatTWS2015inWinnipeg,Canada.ThankyoutomysteeringcommiQee:CarolFoss,ShannonFarrell,andespeciallyStacyMcNulty,mymajorprofessor,foryouradviceandsupport.ThankyoutoJonathanCohenandBrianUnderwoodforhelpwithmydataanalysis.ThankyoutotheenHreRUBLfieldcrewforyourhelpandadvice.ThankyoutoDevonCote,KelseySchumacher,AmasaFiske-White,ThomasRuland,DonaldArthurandKatelynZonnevilleforhelpingmecollectdatainthefieldandprocessinvertebratesamples.

LiteratureCitedAvery,M.L.1995.RustyBlackbird(Euphaguscarolinus).In:ThebirdsofNorthAmerica(F.B.GillandA.Poole,eds.),no.200.AcademyofNaturalSciences,Philadelphia,PA.

Barnard,W.H.,MeQke-Hofmann,C.,andS.M.Matsuoka.2010.PrevalenceofhematozoainfecHonsamongbreedingandwinteringRustyBlackbirds.Condor,112(4):849–853.

Buckley,S.H.2013.RustyBlackbirdsinnortheasternU.S.industrialforests:amulH-scalestudyofnesthabitatselecHonandnestsurvival.Master’sThesis,StateUniversityofNewYorkCollegeofEnvironmentalScienceandForestry,Syracuse,NY.

Buckley,S.H.,Hodgman,T.P.,McNulty,S.A.,Cohen,J.,andC.R.Foss.HabitatselecHon,nestsurvival,andnestpredatorsofRustyBlackbirdsinnorthernNewEngland,USA.Condor117(4):609-623.

Edmonds,S.T.,D.C.Evers,D.A.Cristol,C.MeQke-Hofmann,L.L.Powell,A.J.McGann,J.W.Armiger,O.P.Lane,D.F.Tessler,P.Newell,K.Heyden,andN.J.O’Driscoll.2010.GeographicandseasonalvariaHoninmercuryexposureofthedecliningRustyBlackbird.Condor112:789-799.

Glennon,M.2010.DistribuHonandabundanceofborealbirdsintheAdirondackPark:finalreporttotheNewYorkStateDepartmentofEnvironmentalConservaHon.WildlifeConservaHonSociety,SaranacLake,NY.

Greenberg,R.andS.M.Matsuoka.2010.RangewideecologyofthedecliningRustyBlackbird:Mysteriesofaspeciesindecline.Condor112:770-777.

MacKenzie,D.I.,J.D.Nichols,G.B.Lachman,S.Droege,J.A.Royle,andC.A.LangHmm.2002.EsHmaHngsiteoccupancywhendetecHonprobabiliHesarelessthanone.Ecology83:2248-2255.

McClure,C.J.W.,B.W.Rolek,K.McDonald,andG.E.Hill.2012.Climatechangeandthedeclineofaoncecommonbird.EcologyandEvoluHon2:370-378.

Powell,L.L.,T.P.Hodgman,Fiske,I.J.,andW.E.Glanz.2014.HabitatoccupancyofRustyBlackbirds(Euphaguscarolinus)breedinginnorthernNewEngland,USA.Condor116(1):122-133.

Powell,L.L.,T.P.Hodgman,W.E.Glanz,J.D.Osenton,andC.M.Fisher.2010.Nest-siteselecHonandnestsurvivaloftheRustyBlackbird:doesHmbermanagementadjacenttowetlandscreateecologicaltraps?Condor112:800-809.

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Bobcat habitat selection in a frequently-burned pine savanna

Longleafpine(Pinuspalustris)savannasareoneofthemostbiologicallydiversesystemsinNorthAmericaandcommonlysupporthundredsofspeciesoffloraandfauna(Alavalapa3etal.2002).Thisecosystemhistoricallyoccupiedover30millionhainthesoutheasternUnitedStates(Brockwayetal.2005,VanLearetal.2005).However,todayapproximately1.2millionhaoflongleafpinesavannasexistinisolatedpatches(VanLearetal.2005),primarilyduetolandusechange(e.g.,conversiontoagricultureandestablishmentofintensively-managedpineplanta3onsweretheprimarygoalis3mberproduc3on).Likewise,theseuniquesystemswerehistoricallymaintainedbyfireignitedbynaturalandanthropogenicsourcesbutgovernmentpoliciesweredevelopedtoencouragelandownerstoexcludefirefromtheirproper3es(Alavalapa3etal.2002).Today,over30plantandanimalspeciesendemictolongleafpinesavannasarenowconsideredtobethreatenedorendangered(Landersetal.1995).Forexample,theendangeredred-cockadedwoodpecker(Picoidesborealis)commonlyfoundinlongleafpinesavannasprefertheopen,park-likecondi3onscreatedbyfrequentprescribedfire(≤3years;Alavalapa3etal.2002).Fortunately,naturalresourceprofessionalsrecognizedthediversityoffloraandfaunainlongleafpinesavannas(Barnel1999,Alavalapa3etal.2002)andsubsequentlyimplementedrestora3oneffortstoconvertalteredlandscapesbacktolongleafpinesavannas(Brockwayetal.2005).Animportantmesopredatorfoundinlongleafpinesavannasisthebobcat(Lynxrufus).Currently,aknowledgegapexistsinourunderstandingofbobcathabitatselec3onintheseecologicallydiversesystemsandresearchiswarrantedtodirectourfuturemanagementdecisions.

Andrew R. Little1, L. Mike Conner2, Michael J. Chamberlain1, Robert J. Warren1

1 Warnell School of Forestry and Natural Resources, University of Georgia, Athens, GA 30602, USA 2 Joseph W. Jones Ecological Research Center at Ichauway, Newton, Georgia, 39870, USA

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Historically,redwolves(Canisrufus)andmountainlions(Pumaconcolor)occupiedmanypartsofthesoutheasternUnitedStates.Today,thesespeciesoccupyasmallpercentageoftheirhistoricalrange(Rippleetal.2014).Thisvoidprovidedmesopredators,suchasbobcats,anopportunitytofillthisopenniche.BobcatsareconsideredthemostwidelydistributedwildcatinNorthAmericaandconHnuetoincreaseinmanyparts(RobertsandCrimmins2010).Bobcatspaceuseisinfluencedbymanydifferentfactorsincludingpreyabundance,season,breedingbehaviors,andintraspecificrelaHonships(Chamberlainetal.2003).Bobcatscommonlyselectmaturepine,youngpine,hardwood,andagriculturehabitattypes(ConnerandLeopold1996,Chamberlainetal.2003,Godboisetal.2003a).PreyabundancehasbeenfoundtobeadriveroftheirhabitatselecHonpaQerns(MillerandSpeake1978,ConnerandLeopold1996,Chamberlainetal.2003,Godboisetal.2003a,Godboisetal.2003b).Hardwoodsarecommonlyusedbybobcatsforrefugia(i.e.,densites,cover,andprotecHonfromsummerheat;(HallandNewsome1976,Godboisetal.2003a)ortravelcorridorsbetweenforagingpatches(Godboisetal.2003a).InaddiHon,roadsarealsoconsideredimportanttravelcorridorsforbobcats(LovalloandAnderson1996).

Figure1:Studyarea,JosephW.JonesEcologicalResearchCenteratIchauwaylocatedinBakerCounty,Georgia,USA.

ToimproveourknowledgeofbobcathabitatselecHoninlongleafpinesavannas,weconductedourstudyinalongleafpine-dominatedlandscapelocatedinBakerCounty,Georgia.Thestudyareawas11,735-hainsizeandwasprivatelyownedbytheJosephW.JonesEcologicalResearchCenteratIchauway(hereafer,JonesCenter;Fig.1).TheJonesCenterwascomprisedofapproximately31.2%mixed-pinehardwood,31.1%maturepine(>20yearsold),11.2%agriculture/foodplot,9.8%youngpine(<20yearsold),9.8%hardwoods,2.6%openwater,1.8%wetlands,1.5%shrub/scrub,and0.9%urban/barren(Fig.2).Wiregrassandold-fieldgrasses(e.g.,Andropogonspp.)werethedominantunderstoryhabitatinthepineandpine/hardwoodstands(Goebeletal.1997).However,>1,000vascularplantspeciesoccuronthesite(Drewetal.1998).Roaddensitywas5.48km/km2(Fig.3).

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WeexaminedhabitatselecHonbybobcatsatthestudyareaandhomerangescale.Bobcatsconsistentlymovedoffthestudyareaduringthestudyperiod.Therefore,toassessstudyareahabitatselecHonwecalculatedthemedianlineardistanceofallbobcatlocaHonsoccurringoutsideoftheJonesCenterboundarytotheboundaryline,whichresultedinamediandistancefromthestudyareaboundaryof237-m.WethenbufferedtheJonesCenterboundaryby237-m(i.e.,availablehabitat;seeFigure1solidline)andremovedalllocaHonsoccurringoutsideofthisboundary.ToassesshomerangehabitatselecHon,wecalculated95%fixedkerneluHlizaHondistribuHonsintheAdehabitatPackage(Calenge2006)forprogramR(RCoreTeam2013).ToinvesHgatetheinfluenceofhabitattypeandroadsonhabitatselecHonofbobcats,weusedageographicinformaHonsystem(ArcGIS®10.2,EnvironmentalSystemsResearchInsHtuteInc.,Redlands,CA,USA)tomap6habitattypesavailableonthestudyarea:maturepine(>20yearsold),youngpine(<20yearsold),mixedpine/hardwood,hardwood,shrub/scrub,andagriculture/foodplot.Toevaluatetheinfluenceofroadsastravelcorridors,weclassifiedroadsonthestudyareainto2categoriesbasedontraffic-levels:1)primaryroads(countyandprimary);and2)secondaryroads(secondaryandterHary).

Figure2:HabitatcomposiHonduringourstudy(2001-2007)at

theJosephW.JonesEcologicalResearchCenteratIchauwaylocatedinBakerCounty,Georgia,USA.

Figure3:Primary(paved,graded,anddirt)andsecondary

(harrowed,mowed,andfirebreaks)roadsattheJosephW.JonesEcologicalResearchCenteratIchauwaylocatedinBakerCounty,Georgia,USA.

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WedevelopedresourceselecHonfuncHons(RSFs)toexaminerelaHonshipsbetweenlandscapefeaturesandbobcatestablishmentofhomerangesonthelandscape(studyareaselecHon)andexaminedrelaHonshipsbetweenlandscapefeaturesandbobcatusewithintheirhomeranges(homerangeselecHon).WeevaluatedbobcathabitatselecHonusingaEuclideandistance-basedapproach.WeperformedalogisHcregressionanalysisusingageneralizedlinearmixedeffectsmodelinthelme4package(Batesetal.2007)inprogramRtoquanHfylandscapefeaturesthatinfluencebobcathabitatselecHon.WeusedabinomialapproachtoesHmatehabitatselecHonbycomparingcharacterisHcsofused(bobcat)locaHonstoanequalnumberofrandomlocaHonswithinthestudyareaboundaryandwithinbobcathomeranges(Manlyetal.2002).WeincludedrandominterceptsforindividualbobcatstoaccountforcorrelaHonofhabitatusewithinindividuals,accountforunequalsamplesizesamongindividuals(minimum:42locaHonsandmaximum:309locaHons),andaidinimprovedmodelfit(Gilliesetal.2006).

Results

Wecapturedandmonitored63bobcats(27malesand36females)during2001-2007.Aferremovalofbobcatswith<40locaHonsduringagivenyearand<6monthsoftelemetrylocaHons,ourfinaldatasetcontained45bobcats(16malesand29females).Fromthisdataset,weconstructed144–95%homeranges.Atthestudyareascale,bobcatswereclosertomaturepine,mixedpine/hardwoods,hardwoods,agriculture/foodplots,shrub/scrub,andprimaryroadsbutfartherfromyoungpines.DistancetosecondaryroadswasnotstaHsHcallysignificant.Primaryroadswere13.3Hmesmoreimportantthansecondaryroadsandmixedpine/hardwoodswere1.8Hmesmoreimportantthanhardwoodsforbobcathomerangeestablishment.UsingthecoefficientesHmates,wedevelopedastudyarearesourceselecHonmaptodepictareasoflowesttohighestrelaHveprobabilityofresourceselecHon(Fig.4).

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Atthehomerangescale,bobcatswereclosertomaturepine,mixedpine/hardwoods,hardwoods,agriculture/foodplots,shrub/scrub,primaryroads,andyoungpines.DistancetosecondaryroadswasnotstaHsHcallysignificant.Agriculture/foodplotswere6.4Hmesmoreimportantthanyoungpineandhardwoodswere2.4Hmesmoreimportantthanmixedpine/hardwoodsforbobcatusewithintheirhomerange.UsingthecoefficientesHmates,wedevelopedahomerangeresourceselecHonmaptodepictareasoflowesttohighestrelaHveprobabilityofresourceselecHon(Fig.5).

Figure4:RelaHveprobabilityofstudyarearesourceselecHonforbobcats(Lynxrufus)during2001-2007attheJosephW.JonesEcologicalResearchCenteratIchauwaylocatedinBakerCounty,Georgia,USA.

Figure5:RelaHveprobabilityofhomerangeresourceselecHonforbobcats(Lynxrufus)during2001-2007attheJosephW.JonesEcologicalResearchCenteratIchauwaylocatedinBakerCounty,Georgia,USA.

Discussion

Ourfindingsdemonstratethegeneralistnatureofbobcats.WeobservedbobcatsselecHngmaturepineandmaturepine-hardwoodstandsmanagedbyfrequentfire,butalsoselecHngforotherimportanthabitattypessuchashardwoods,agriculture/foodplots,andshrub/scrub.WealsoobserveddifferenHalhabitatselecHonacrossspaHalscales.Forexample,hardwoodswereofgreaterimportanceatthehomerangescalethanthestudyareascale,suggesHngthatlandmanagersneedtorecognizeandpotenHallyincorporatetheeffectsofspaHalscaleintotheirmanagementprogramsdependingontheirgoalsandobjecHves.

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Ourstudysiteconsistedof72%fire-maintained,pine-dominatedforestwithfrequentfirereturnintervals(≤2years).Ourfindingswereconsistentwithpreviousworkthatfoundmaturepineandmixedpine/hardwoodstobeimportanttobobcatsinafire-maintainedforest(Godboisetal.2003a).Bobcatswerealsogenerallyclosertoagricultural/foodplots,shrub/scrub,andhardwoodshabitats.SmallmammalpopulaHonshavebeenfoundtobemostabundantinareaswithdenseherbaceousgroundcoverinterspersedwithshrubs(Golleyetal.1965,Schnell1968),whicharecharacterisHcofshrub/scrubhabitattypesjuxtaposedtoagriculture/foodplotsonourstudyarea.Hardwoodswerealsofoundtobeimportanttobobcats.Hardwoodstandsmayserveastravelcorridorsbetweenforagepatches(Godboisetal.2003a).Likewise,selecHonofhardwoodstandsmayalsoserveasimportantlocaHonsforrefugia(i.e.,densites,cover,protecHonfromsummerheat;HallandNewsome1976,Godboisetal.2003a)inpine-dominatedsystems.AddiHonalresearchisneededtoevaluatepotenHalseasonaldifferencesinhabitatselecHonandevaluatetheinfluenceofHme-since-fireonbobcatselecHonpaQerns.Forexample,bobcatsmayquicklyoccupyarecentlyburnedpatchtoexploitpotenHalresources,thusaffecHngthepredator-preydynamicsinafrequently-burnedlandscape.

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Spatial Ecology & Telemetry Working GroupOn the Web at: http://wildlife.org/setwg/

2016 Working Group ExecutiveOfficers Chair — James K. Sheppard, San Diego Zoo Institute for Conservation Research, Escondido, CA Past Chair — Jeff Jenness, Jenness Enterprises, Flagstaff, AZ Treasurer — Marci Johnson, National Park Service, Kotzebue, AK

Upcoming Events 2016 Wildlife Society Annual Conference, Raleigh, NC: October 15-19, 2016. http://www.twsconference.org

SCGIS Conference, Jun 22 to Jun 25, 2016, Asilomar Conference Grounds, Pacific Grove, CA. http://www.scgis.org/conference

ESA 101th Annual Meeting, August 7-12, 2016, in Fort Lauderdale, FL. http://www.esa.org/esa/meetings/annual-meeting/

Spatial Ecology & Conservation 4, University of Birmingham (UK), 12 to 14 July 2016 .http://www.ert-conservation.co.uk/sec4-introduction.php

Interaction workshop @ UT-Austin Nov 10-11, 2016: https://sites.utexas.edu/interaction/

REMOTELY WILD Summer 2016 – Volume 32

Remotely Wild is a virtual publication issued by the Spatial Ecology and Telemetry Working Group of The Wildlife Society. The newsletter provides information about the working group and its activities, columns and features, information about new technologies, publications and resources of interest to spatially enabled wildlife professionals.

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The Spatial Ecology and Telemetry Working Group provides an opportunity for TWS members to address issues of concern to the GIS community and to advance their own skills and understanding of GIS, remote sensing, and telemetry technologies. The Working Group functions as a clearinghouse

of information and expertise in the area of GIS, remote sensing, and telemetry for The Wildlife Society Council, TWS sections and chapters, and individual TWS members.