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Red Squirrels United - Evolving IAS grey squirrel management techniques in the UK and Ireland
D3–Ecosystemfunctionrestorationassessment:
Outcomesofmonitoringevaluationsonallyear1conservationactions
October2017
ZeldavanderWaalandAileenMill
NewcastleUniversity
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Summary
Thepurposeofthisaction(D3)istoevaluateandinformtheimplementationofgreysquirrelmanagement and to provide an ecosystem restoration assessment for project areas. Thisreportsummarisestheyear1(August2016-August2017)progressofActionD6toassesstheimpact of the conservation actions C1-C4 undertaken during the Red Squirrel United(Sciuriosity)project.
Adelayinprogresshasarisenasthereisnospecificprovisionforstandardisedandintegrateddata collection and collation in the project (e.g. project app or Access database). Specificrequirements for thequality and frequencyof data submissionweredeterminedby NU inPreparatoryActionA3;thesewereactivelycommunicatedtoallpartnersearlyintheproject.Inordertoensurehighdataquality,NUdevelopeddatarecordingtemplateswhichaimtobestandardisedandspecifictotheRSUproject,butalsospecifictothepartners’existingprojectsandcurrentroutines,sotheyremainfamiliartooperators.However,finalisingrecordingsheetshasbeenslowduetothetimetakenforcommunicationandtestingofspreadsheetswithallRSUpartners.Someinitialdatahasbeenprovidedbutrequiressignificantchecking–especiallyofthedocumentationofeffort.
NUhasstarteddevelopingthemodelsrequiredtoevaluatetheconservationactionsbutthishas been limited by data provision. NU have been progressing these models by retrievingassociatedmetadata(e.g.habitat,weatherandenvironmentinformationappropriatetothecorrect timeframe and spatial areas), and developing the code required for the modelstructures.Thesmallsectionsofthedatafromyear1conservationsactionswithsuitableeffortvariables have been used to build model structures. All models will require furtherdevelopmentasmoredatabecomesavailabletodeterminethecorrecttemporalperiodoverwhichtoconstructthemodels.ThemodelframeworksarebeingcodedusingtheRsoftwarepackageandassociatedcodepackageswhichareallfreelyavailableandenhancethesuitabilityofthemodelframeworkforpostprojectuse.
AsthemodelsaredevelopedandtestedNUwillconsultwiththeprojectpartnerstodeterminethemostsuitablemethodsoffeedbackofthemodeloutputstoinformongoingcontrol.
ThereisanurgentneedtofinaliseallrecordingtemplatesastheyareessentialtoproducethedatarequiredtoassessthesuccessoftheRSUconservationactionsandtoallowtheprojecttodevelop.
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SummaryofactionsincludedindeliverableD3.....................................................................................4
Evaluateandinformtheimplementationofgreysquirrelmanagement..........................................4
Provideanecosystemrestorationassessmentforprojectareas......................................................4
ProgressinYear1...............................................................................................................................4
Summaryofpreparatoryactions(A6)....................................................................................................5
Agreementondatareportingprotocols............................................................................................5
MajorchallengesinDataprovision....................................................................................................6
Currentstatuswithinglobalmodellingframework...........................................................................7
Progresstowardsevaluatingtheimplementationofgreysquirrelmanagement.................................8
Roleofecologicalmodels,assumptionsandrequirements...............................................................8
Materials............................................................................................................................................9
DataProvision:FromYear1ConservationActions............................................................................9
Evaluateandinformtheimplementationofgreysquirrelmanagement............................................10
Ratesofgreysquirrelremovalperunitcontroleffortthroughtime...............................................10
Effectofmanagementinterventionsongreysquirrelabundance/range........................................11
Structureandrequirementsofremovalmodels..........................................................................11
Challengesandlimitations...........................................................................................................12
Provideanecosystemrestorationassessmentforprojectareas........................................................14
Changeinabundanceandrangeofnativeredsquirrelsinresponsetogreyculling.......................14
Occupancymodelling...................................................................................................................14
Progress:metadataandscales.....................................................................................................14
Impactofmanagementontheproportionofgreysquirrelscarryinginfections.................................16
Modelaims.......................................................................................................................................16
Progress:recordingtemplatesandtissueanalysis...........................................................................16
NextSteps............................................................................................................................................17
Rpackagesreferences..........................................................................................................................18
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SummaryofactionsincludedindeliverableD3ThetwomainareasofworkbeingcoordinatedandassessedbyNUwithinActionD3involvedataanalysisandmodellingtoquantifyecosystemrestorationassessmentandevaluateandinformtheimplementationofgreysquirrelmanagementacrossactionsC1-C4.Bothareasofworkaredependentontheprovisionofdatafromprojectpartners,andusedataonsightingsofredandgreysquirrels,monitoringdatafromregularcameratrappingandrecordsofgreysquirrelcontrol,includinglivecaptureandshootingeffortdata.
EvaluateandinformtheimplementationofgreysquirrelmanagementTheaimisusedatacollatedongreysquirrelcontrol,includinglivecaptureandshooting,toassesscurrentcontrolpractiseswiththeaimofoptimisingcontrolintimeandspace.Specificallywewillassess:
(1)ratesofgreysquirrelremovalperunitcontroleffortthroughtime(2)effectofmanagementinterventionsongreysquirrelabundance/range
Theaimistousethefindingsfromeachoftheconservationactionstoinformthenextphaseofmanagementineachareawithinthetimeframeoftheproject.
ProvideanecosystemrestorationassessmentforprojectareasToassesstheimpactofthecontrolofgreyssquirrelsontheconservationoftheredsquirrelswewillcombinemonitoring,sightingsandcontroleffortdatainanassessmentof:
(3)changeinabundanceandrangesizeofnativeredsquirrelsinresponsetogreyculling(4)impactofmanagementontheproportionofgreysquirrelscarryinginfections
TodothisNUhavedevelopedamodellingframeworkanddeterminedtheassociateddatarequirements.Themodelswillbeusedtoinformrealtimemanagementactionsandcostedfuturemanagementscenarios.
ProgressinYear1ThisreportcoverstheYear1periodSep2016toSep2017andreportsonactionD3inthistime.TheprepactionA6reportcoveredtheperiodfromNov2015toAug2016.ActionA6highlightedthattheanalysisoffielddatatoevaluateSciuriosityactionsinrealtimeisdependentontheprovisionofsufficient,goodqualitydata.
Thereportreporteddifferencesinpre-existingprotocolsfordatacollection,parametersrecordedanddataquality.Themajorityofyear1hasbeenspentdevelopingrobustdatarecordingmechanismswithallpartnersandworkingtowardsgettingtheseimplementedcorrectly.
Year1progresshaswithmodellinghasbeendelayedbydataprovisionissues.Weprovideanupdateonthefouranalyticalobjectivesthatcontributetotheassessmentofgreysquirrelmanagement.Foreachaspectoftheanalysis,weincludeapresentationofthemodelstructuresaswellastheprogress,limitationsandchallengesassociatedwitheachtypeofmodel.
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Summaryofpreparatoryactions(A6)AgreementondatareportingprotocolsMeetingsandsitevisitstoshareprogressanddisseminateadviceondatarecordingprotocolshavetakenplacewithallpartners.Anessentialpartofefficientandhighqualitydatagatheringisthepreparationofadatarecordingtemplatethatsuitstherequirementsoftheprojectaswellaseachpartner’sneeds,canhandlealargevolumeofdata,andallowsassemblingthedatasets.
Thereisnospecificprovisionforstandardisedorintegrateddatacollectionandcollationintheproject(e.g.projectapporAccessdatabase)andtheprojectpartnersallhavedifferentwaysofworkingandrecordingdata.ToovercomethiswithminimumcostanddisruptiondatarecordingspreadsheettemplatesweredesignedbyNUthatmatchthoseobjectivesandincorporaterequiredchangestoexistingdatarecordingsheets(Table1)andinclude:
- incorporatesolutionstolimitrecurrentissuesidentifiedbyNUinpreviousrecords(e.g.drop-downmenustoeliminatetypingerrors),additionalvariables
- accommodateadditionaldataentrynecessaryfortheanalysis(e.g.effort,numberoftraps,area)- includepartner-specificrequests(e.g.cameralogsinUWT)PartnersundertakingconservationactionsC1-4areresponsibleforcollecting,recordingandsharingdatawithNU.NUisresponsiblefortheprocessing,analysisandinterpretationofdataandsharing/disseminationofthefindings.
Table1:ExamplesofpreviousissuesandsubsequentchangesagreedwithpartnersforRSUdatacollection.
Variable Previousissues Changes
Dailycountwithinsession
Datawererecordedforindividualcapturesandpersession;dailycountofcaptureswasnotrecorded.
Bothtimescales(sessionanddaywithinsession)cannowberecorded,asrequiredbyrobustmodels
Controlsession
Thedefinitionofatrappingsessiondifferedbetweensites.Ideally,asessionisaseriesofconsecutivecontroldays.
Controleffortismisrepresentedifthereporteddurationincludesnon-controldays,leadingtoabiasinthemodeloutputsAsessionisnowdefinedasaseriesofconsecutivecontroldaysforallpartners.
Blanktrapsandbycatchesrecords
Bysummarizingcatchdataoveratrappingsession,blanktrapsandbycatchescannotbeaccountedforonadailybasis.
Trapsfoundblankorwithbycatchmustbeaccountedforastheyleadtoareducedeffort.Partnershaveexpresseddifficultiesinobtainingthislevelofsummarydata.DiscussionsonwaystorecordthisinformationefficientlyarewelcomedforallRSUpartners.
Effortinspace:trappingarea
Traplocationswererecordedonlywhensuccessful,sothatthetrappingareacouldnotbedetermined.
Anestimationoftheareacontrolledduringeachtrappingsessionisessentialtoexpresscapturerateinaconsistentandstandardisedunit.Thelocationofeverytrapisnowrecorded,allowingeffectivetrappingareatobeestimatedastheconvexhullcreatedbythemostouterpointsofthesurfacecoveredduringagiventrappingsession.
Effortinspace:shootingarea
Shootinglocationswereonlyrecordedaslocationofgreysshot,notallowingthecalculationoftheshootingarea.
Discussionwithallpartnerssuggestedthatshootingareacanbeprovidedasanestimationofshootingareabytherangers(alsowalkingdistanceatNWT)foreachshootingsession.
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Solutionsandrecordingrequirementswerediscussedwithallpartnersandintegratedwithinthedatarecordingtemplates.Existingrecordswereusedinordertoretainafamiliarformatfortheoperators.Asaconsequence,allpartnerswererequiredtotesttemplateversionsofthenewelectronicrecordingformproducedandemailedbyNU,andtocommunicatepotentialissuesorrequiredamendmentsorpreferencesbeforeamendmentsweremade,andanupdatedversionwassentagain.Changeswerediscussedanddemonstrated,viaemail,inpersonandon-site.
MajorchallengesinDataprovisionThespeedofdevelopmentoftherecordingtemplateshasbeendictatedbytheinputfromtheconservationactionpartnerstotestandimplementthenewdesignsthathavebeencreatedbyNU.NUhavebeenunabletofinalisethetemplatesandcommencewithdatabasecollationasissues(e.g.errorsinthesheets)havenotbeenhighlightedorcommunicatedimmediately.
OverallthisprocessofdevelopingandtestingthetemplateshasbeenmuchslowerthanagreedandhasbeenhardtoimplementaseachprojectpartnerhasbeenkeentopreserveorintegratetheirexistingmethodsofworkwiththeRSUdataneeds.Ideallyaprojectdatabasewouldhavebeenusedbutdatasharinginthiswaywasimpossibletoimplementinashorttimetoaprojectwithmultipleexistingworkingpractices.
Recordingoperationalefforthasbeenamajorchangeformostpartnersandhasbeendifficulttoobtainconsistentlyintimeandspace.Recordsofeffortintime(i.e.duration)improvedasaresultofpreciselyredefiningasessionasaseriesofconsecutivedaysontherecordingtemplates.Recordsofeffortinspace(i.e.controlarea)isanewparametertorecordforsomepartners;ithasbeenadifficultvariabletoobtainconsistently(especiallyforshooting)(Table1,page5).
Datarecordingqualityremainsaproblemandneedstobeaddressedbypartners,includingpreventableerrorssuchas:
- Typosormisnamingwoodlands e.g.Watersmeet/Watersheet- Woodlandsclassifiedincorrectlyand/orinconsistentlyintobiggerzones e.g.somewoodlandsclassifiedinallfourzones- Datesthatareobviouslywrongmakeotherdatesquestionable e.g.surveysdated2018;isit2017oristhewholedatewrong?
DatacheckingandcleaningistheresponsibilityofthepartnerscollectingthedataandmustbedonebeforesharingdatasetswithNU.Uncheckederrorscanhavelargeconsequencesforanalysisastheyleadto:
- datatobeomittedfromtheanalysise.g.mis-speltlocationnameshindermatchingsessionstowoodlandcharacteristics,landscape,individualdata
- resourcetolocalisethoseerrors(detractingfromdataanalyses)Atthecurrenttestingstage,dataerrorsdelaymodelassessmentasitistediousprocesstodeterminewhetheroutcomesareduetodataerrorsorissueswiththemodelstructureindevelopment.
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CurrentstatuswithinglobalmodellingframeworkTheglobalmodellingframeworkwassetoutinthereportonA6andissummarisedinBox1.Step1wascompletedinthePrepActionA6.Steps2and3areinprogressandsubsequentstepsarecontingentonStep2beingcompleted.Thesupplyofhighqualitydata(Step2)isaprerequisiteforfurtherdevelopmentofthemodellingstructures(Step3),andthefrequentdatareporting(Step2)isrequiredtoassesstheongoingcontrol(Step4).
NUhavebeenprogressingthedevelopmentofprocessesfordataprocessing,includingcleaning,standardisingandassemblingdatasetsandhavestarteddevelopingthemodelstructures.However,theRSUdatabasemustincreaseinthenumberofhighqualitydatasets(bymonthlyinputfromallpartners)sothattheanalyticalapproachcanbefinelytunedtotheRSUdata.RSUpartnersarepresentedwithvariouschallengesassociatedwithdatarequirements(newparameterstorecord,increasedfrequencyofdatadigitisation),howeverdelaysinfinalisingrecordingtemplatesandcompromisesindataqualitywillreducethevolumeofconservationactiondatathatcanbeusedinActionD3.Inturnthiswilllowerthelevelofprecisionandaccuracythatthemodelscanachieve,hencetheirusefulnessinfuturecontrolplanning.ThecompletionoftherecordingtemplateswasraisedattheProjectManagementBoardinSeptemberandtheprovisionofdatahasbeenaddedtotheRSUProjectManagementBoardRiskRegister.
InordertocompleteStep2,datarecordingtemplatesmustbefinalized.Thefirstrecordingtemplatesweresuggestedtoallpartnersearlyintheproject:February2016(RSTW),May2016(NWT),December2016(UWT),andOctober2016(LWT).However,bySeptember2017,templateshaveonlybeenfinalisedbyNUforNWTpartnersandareongoingwithLWTandUWTpartners.RSTWpartnersdidnotadoptthesuggestedformatfortheirdatacollection.
Asaconsequencealargeproportionofyear1hasbeenspentpreparingtemplaterecordingsheetsandthemodeldevelopmenthasbeendelayed.
1.AssembledataReviewdata,identifystrengthsandweaknesses,anddevelopmethodsto
addressobjectivesforeachsite.
2.StandardizedatasetsDeterminespecificrequirementsfor
datacollectionwithregardstoenvironmentalfactorsandcontroleffort,producedataaccordingly.
3.DevelopthemodelstructureOccupancymodels,removaldatamodels,cost-benefitmodels.
4.AssessimpactofcontrolModelsprogressivelyimprovedby
adjustingfornaturalandsite-specificvariationswithincreasingamountof
highqualitydata.
5.RerunandupdateUpdatemodelsusinghighqualitydata,
producemorepreciseinferences.
6.TransferandreplicateHighqualitydatarepresentativeofnaturalvariationsandcontroleffortallowtransferringinferencesfromconservationactionstootherareas.
Box1:Globalmodellingframework
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Progresstowardsevaluatingtheimplementationofgreysquirrelmanagement
Roleofecologicalmodels,assumptionsandrequirementsEcologicalmodellingwillbeusedtoevaluateseveralaspectsoftheconservationactions.Ecologicalmodelsallowtheintegrationofmultipletypesandscalesofdatatoinvestigateandunderstandthedynamicsofcomplexecologicalsystems.Inthecaseofthisproject,tounderstandthechangesingreysquirrelspopulationsbroughtaboutbycullingandtheconsequentimpactonredsquirrels.Thestructureofeachmodelisdevelopedbasedonassumptionsthatdescribeunderlyingecologicalprocessesthatareuncertainandoftenunobserved.Highqualitydataallowrobustmodelsthatcanprovideareliablebasisforthedevelopmentofmanagementsupportandpolicies.
Squirrelcountdata(e.g.numbersobservedorcaptured)canbeusedtogatherinformationabouttheiroverallabundance.Howeverinordertoconsidercountdataasanindexofabundanceandpotentiallyinvestigatechangesthroughtime,itisnecessarythat:
(1) CountdataarestandardisedacrossdatasetsAstandardisedmethodologywouldrecordallanimalsobservedduringafixeddurationatdiverselocations,howeveritisnotpracticalorfeasibletostandardisemethodologiesacrossallRSUpartnersaspractices,protocolsandprioritiesvary.
Instead,countdataobtainedbyallpartnersmustbestandardisedaccordingtothemethodologyusedtoobtainthedata.Theeffortinvolvedindatacollectionmustbedescribedpreciselyintimeandspacebyeachpartnerandforeachsession,sothatcountdatacanbeadjustedaccordingly.
(2) WehaveagoodestimateoftheprobabilityofdetectionComparingcountdataasrawnumbersassumesthattheprobabilityofdetectionisthesameacrossalldatasetscompared.Forinstancewhencomparingcapturesorsightingnumbersindifferenthabitats,theratioofthecountsonlyreflectstheratiooftheabundanceifthechanceofseeingorcatchingananimalissimilarinallhabitats.Similarlybysamplingagivenlocationthroughtime,achangeincountdataonlyimpliesachangeinabundanceifthedetectionprobabilityremainsidenticalthroughtime.Itisunlikelythatthedetectionprobabilityiscomparablethroughtimeandspace.Itisthereforeessentialtodeterminethemajorfactorsinfluencingdetectionprobability.Thisrelationshipislikelycomplexandvarieswithhabitatsandthroughtimewithpotentialinteractions,itmayalsodependonthesquirreldemographicsthroughouttheyear,andlikelyvarybetweentheconservationactionsduetodifferencesinlandscapeandclimate.Partnersmustaccuratelyrecordalltheparametersoftheirmethodologythatpotentiallyinfluencedetectionprobability(e.g.samplingorcontroleffortasadurationoftimeandareasurveyedandnumberofdevicesorobservers,timeofyear,presenceofbait).AlongsidethisdatarelevantenvironmentalmetadatamustbeidentifiedandretrievedbyNUforallobservations.Detectionprobabilitywillbeacomponentatthebasisofthestructureofallthemodelsincludedinthisanalysis.
Thesystematicandaccuraterecordingofeffortinspaceandtimeisessentialforthefollowinganalyses.
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MaterialsAllecologicalmodelsarebeingdevelopedusingtheRenvironmentandspecificdetailsaregivenbelow.Tocreatethesemodelstructuresseveralspecialisedpackageshavebeenused:
- toreadshapefilesandprocessspatialdata:geosphere,spdep,raster,rgdal,rgeos,maptools,sp,tmap,dismo- toreadandcompileRSUpartners’records:readxl,reshape,tidyr,rnrfa,plyr,dplyr- fordataillustration:ggplot2,ggmap,lattice- formodelling:unmarked,pscl,lme4- fordataediting:stringr,zoo,lubridate
DataProvision:FromYear1ConservationActionsDatarecordedusingtheprojectprotocolsarereportedinTable2.Themajorityoftheconservationactionhasbeenthroughcontrolsessions(shootingandtrapping).NWThavealsobeenusingcamerasforregularmonitoring.Allconservationactionshaverecordedmoredatabutthisisnotyetinaformatcompatiblewithmodelling.Thelargestissuewiththerecordeddataisthelackofconsistencyinreportingofeffortdata.
Table2:SummaryofdatasentintheagreedformattoNUbypartners,bymid-September2017
Partners: NWT LWT UWT
Datacollectionstart 01/12/2016 03/01/2017 26/06/2017Datacollectionend 19/06/2017 31/03/2017 25/08/2017Totalduration ~6months ~3months ~2monthsNgreykilled 170 71 50Ngreyseen 75 94 0Nredrecorded 67 4 0Nsessionstotal 248 48 41Ncontrolsessions 125 48 41Nmonitoringsessions 123 0 0
Nsessionspermethod
Camera 107 0 0Feeder 14 0 0Shoot 89 22 7Sighting 2 0 0Trap 36 26 34
Effortdatasummary 45controlsessionswithouteffortdata Effortdatalacking Effortdatamostly
lacking
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Evaluateandinformtheimplementationofgreysquirrelmanagement
RatesofgreysquirrelremovalperunitcontroleffortthroughtimeTheratesofgreysquirrelremoval,andnumbersofcontrolandmonitoringsessionsareexpectedtovarybetweenpartners,duetothedifferentmanagementapproachesbeingundertaken(e.g.earlywarningversuseradication).Howevertherearealsodifferencesinthevolumeofdataduetoprojectstartingpoints,staffstructuresandalsoduetothefrequencyofdatasharinganddataquality(Table2,page9).
Thelackofeffortdatarecordediscurrentlyamajorconstrainttotheanalysis.MissingrecordsbyNWTpartnersmostlyoccurredduringtheinitialpartofthedatacollection(43/45sessionsDec2016-March2017)andhaveimprovednotablyinrecentmonths.
LWTandUWTpartnershavenotprovidedtherequiredinformation.LWTpartnersfaceddelaysanddifficultiesretrievingdatarelatedtoturn-overofstaff.UWTpartnersprovidedinaccuraterecords(missingtraplocationinformation)thatdonotallowcalculatingcontrolareas.Datasentbybothpartnersarenotsuitedforanalysis.RSTWpartnerdidnotusethesuggestedtemplatefortheirdatacollectionanddidnotsendrecordsinaformatthatallowsefficientprocessingandanalysisofdata.
Discussionison-goingwithLWTandUWTpartnerstoattemptmatchingeffortdatawithpreviousrecordsfromwithintheproject.Mostdatarecordingissueshavebeenresolvedsousingtheagreedrecordingformatwillgeneraterecordsthatmeetthestandardrequiredforanalysis.
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Effectofmanagementinterventionsongreysquirrelabundance/rangeStructureandrequirementsofremovalmodelsRemovalmodelscanbeusedtoestimatepopulationabundancewhileremovingindividualsfromapopulation.Thecountofgreysquirrelscapturedandculledconstituteremovaldata,andwillbeusedinremovalmodelstoinvestigatethepopulationdynamicsofpopulationsundercontrol.Removalmodelsarehierarchicalinthattheycontainseveralstageswhicheachcorrespondtoapreciseassumptionthatisestimatedusingtheremovaldata(Figure1).
AnoverviewofthecomplexstructureoftheremovalmodeldesignedfortheRSUprojectisillustratedinFigure1.Thestructurecontainsseveralassumptions.Througheachofthem,andbyincorporatingrelevantenvironmentalvariablesateachstage,themodelaimstoestimatetheabundanceofgreysquirrelthatisrepresentedbythedailycaptures(y1toy5)andthesumofdailycaptures(Y).
Removaldata(numberofsquirrelcaptures)needtobestandardisedbycharacteristicsofthecontroleffortsuchas:durationofcontrolsession,trappingdensity,frequencyoftrap-setting,numberofshooters,controlarea.
Modelswillinvestigatetherelationshipbetweencapturesandenvironmentalfactors,inordertoquantifytheeffectivenessofthecontroloperations.Inordertoassesschangesinthepopulationsthroughtime,thefactorsdrivinganddescribingabundancewillfirsthavetobeestimatedwithhighprecision.Itisessentialforthesemodelstoberobustandfinelytunedbeforetheeffectivenessofdifferentcontrolregimescanbereliablyassessed.Ithasnotbeenpossibletofurtherdevelopthesemodelsuntilalargequantityofdatacoveringalargertimeperiodareavailable.
Figure1:Overviewofagreysquirrelremovalmodelstructure.
Ycapturesduringsession
Thesumofdailycaptures(capturesduringasession)istheproportionofthepopulationthatisavailabletocapture,thatisindeedcapturedduringthesessionaccordingtoadetectionprobability.D
ATA
Y
y1-5dailycaptures
y1 y2 y3 y4 y5
Dailycaptures(y1toy5)areassumedtodecreaseprogressively,sinceanimalsareremovedeachday.
Navailable
Nsquirrels
availablefordetection
ThepopulationavailableforcaptureisitselfaproportionoftheoverallpopulationatsiteM,accordingtoaprobabilityofbeingavailable.
Nsquirrelspresentat
SiteM
Squirrelabundancedependsonasite-specificprobabilityofoccupancy N
SiteM
Whenthepopulationisopen(longterm),abundanceisaffectedbynewanimalsviaprobabilityofcolonisation
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ChallengesandlimitationsTheassumptionsmadeateachstageoftheremovalmodelmustbetestedatrelevantecologicalscalessothattheoverallstructurereflectstheecologicalandmethodologicalprocessesthatleadtonumberofgreysquirrelscaughteachdayduringasession.
Determiningthetemporalandspatialscalesatwhichthoseassumptionsapplyisapartoftheanalysis.Forinstance,oneassumptionisthattheprobabilityofcapturedecreaseseachdayofacontrolsession(i.e.fromy1toy5onFigure1).Thisisbecausetheindividualscapturedareremovedeachday,thereforetheycannotbecapturedagain,noraretheyreplaced(assumptionofclosedpopulation).Asaresult,theprobabilityofcapturingagreysquirrelunderareasonableprobabilityofdetection,mustdecreaseeachdaywithinasession.
Theprobabilityofdetectionduringthewholesessionisafunctionoftheassumptionofdecliningcaptures;thedailyprobabilitiesarelinkedtotheoveralldetectionprobability.Thereforeifadecreaseindailycapturesisnotobservedthroughoutsessions,theprobabilityofdetectionduringacontrolsessionisassumedbythemodeltobeverylow.Thisinturnleadstoanoverestimationofthesquirrelabundance;implyingthatasquirrelcapturedwithinverylowdetectionprobabilitiesmustreflectaveryhighabundance.
Thisdifficultyinestimationimpliesthatthedatacollectedarenotrepresentativeoftheprocessesweareattemptingtoquantify.Wecanimproveourestimationwithmoredatacollectedusingasimilarmethodology.Thisallowsustodeterminewhatothervariablesmayinfluencethetrendobserved,andincorporatethemintothegeneralmodel.
Thetimescalesusedwithinthemodelmustbeecologicallymeaningfultogeneratereliableestimationsoftheprocessesinvolvedhoweverthesecanalsobeverydifferentandareoftendictatedbypartners’practices.Aspartnersapplymultiplemethods(shootingandlivecapturewithorwithoutbait)invaryinglandscapes,comparingandcontrastingtheeffectofexternalparametersischallenging.Whenmanyparametersvarybetweenobservations,thenumberofobservationsthatcanbeconsideredrepeatedandusedforcontrastingisreduced.Forinstance,apotentialfactorcanbetheindexoftheprimaryperiodwhenagivensessiontookplace(Figure2).Eachprimaryperiodisattributedanindexnumberreflectingitsrankingalongachronologicalorderwithineachsite.ThisindexwillhavedifferentmeaningforNWTpartnersthanforotherswhileitreflectsothercriteriadefiningit(e.g.duration,timeofyear).Withmoreprimaryperiodsbeingrecorded,theindexasafactorcanbecontrastedandmodelscanbeusedtoinvestigatearelatedtrend.Thisissuemakesmodellingasmall,temporallyconstraineddatasetdifficultbutwillimprovewithagrowingdataset.
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Figure2:ExampleofpartitioningofprimaryperiodsforNWTpartnersredandgreysquirrelcountdata.
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Provideanecosystemrestorationassessmentforprojectareas
ChangeinabundanceandrangeofnativeredsquirrelsinresponsetogreycullingOccupancymodellingTheeffectofcontroloperationsonredandgreysquirrelpopulationswillbeinvestigatedusingoccupancymodelling.Aparticularfocuswillbeonareasaroundstrongholdboundarieswiththeaimtoassesshowtheseboundariesshiftthroughtimeinrelationtocontroloperations.
Occupancymodelswillestimatetheprobabilityofredsquirrelbeingpresentgivenparticularenvironmentalconditions.Presence/absencedataarecollectedbythepartners,usuallythroughregularmonitoringwithcameratraps;someenvironmentalvariablesassociatedwitheachobservationareretrievedbypartners(e.g.date)andmostlybyNUusingthelocationanddateoftheobservation(e.g.dailyrainfall,percentageofbroadleavedforestwithin1km).
Detectionisimperfect(i.e.individualsmaybepresentyetunseen),togetabetterestimateofdetectionthemodelsrequiredatafrommultiplevisitstosamplesitestoadjustforthedetectionprocess.Afewenvironmentalvariablespotentiallyinfluencingthedetectionprocessarecollectedbypartners(e.g.observer,method)andmostlycomputedbyNU(seemetadata).
Occupancycannotbeassessedbeforetheprobabilityofdetectionisreliablyestimated.Thereiscurrentlyaninsufficientvolumeofdatatoproceedwiththisanalysis.
Progress:metadataandscalesRobustmodelsusinghighqualityfinescaledataarenecessarytoinvestigatetherelationshipbetweencullingpracticesattheselocationsandgreysquirrelabundance.Thosemodelsmaytheninformtherequiredcontrolpracticesassociatedwithlowlevelsofgreysquirrels,andpotentiallyallowredsquirrelstoexpandinstead.
Partnershavebeenrelyingonannualmonitoringtoassesstheresponseofredsquirreloccupancyinrelationtothecontroloperations.Thedatausedinthisnewmodelwillusefinerscaledata,whichwillallowustoinvestigatetheprocessesdrivingredsquirrelabundancemoreaccurately.
Metadatawereretrievedforthedataprovidedbypartnersintheagreedformat.Scriptswerewritteninpreparationoffuturedatabeingreceived.Metadatainclude(Table3)site-dependentvariables(suchashabitatsandotherlandscapedescriptors)andtime-dependentvariables(suchasweatheratfineandmediumscales,lifehistoryeventsattimeofyear).
Theeffectofthesevariables,andpotentialinteractions,willbeinvestigatedatseveraltemporalandspatialscalesontheseveralecologicalprocessesincorporatedintothemodels.
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Table3:Metadataretrieved/preparedforretrievingbyNUforthedataprovided/tobeprovidedbypartners.
Typeofvariables
ExampleofvariablesHowdatasetswere
obtainedScalescomputedforanalysis
HabitataccordingtoNationalForestInventoryandEDINA
ConiferBroadleafFelltreesYoungtreesBarelandShrubsAgricultureGrass..
HardcopyoftheNFIdatasetforUWTwasobtainedonCDafterdirectrequest.Datasetsavailableasshapefilesonline:https://www.forestry.gov.uk/fr/beeh-a2uegshttp://digimap.edina.ac.uk/environment
Dataarecomputedspatially,aggregatedatthewoodlandarea,controlarea,andatseveralvaluesofradius(e.g.1km,2km)aroundrelevantlocations(e.g.centralcontrolarealocation).Ateachscale,eachhabitatcategoryisexpressedasapercentagecoverofthewholearea.
Landscapedata
DistancetourbanareasRivercoverRoadtypeandcoverUrbancategories
SpecifictoeachareaAveragedateachspatialscale(woodland,controlarea,pointlocation).
Smallscaleweather
CloudcoverPercentagemoonilluminationTemperatureWindspeedHumidityBarometry
Availableonline,forexample:www.timeanddate.com/weather/@7297704/historic?month=6&year=2016
Providedasadailyvalues.Alsocomputed
- Overthesession- Duringtimesincelast
session
Largerscaleweather
Minimum,mean,andmaximumtemperatureHoursofsunRain(mm)NumberofairfrostdaysNumberofraindays(>1mm)
Availableonline:http://www.metoffice.gov.uk/pub/data/weather/uk/climate/datasets/Tmax/date/England_E_and_NE.txt
Providedasmonthlyandseasonalvalues.Alsocomputedas
- Differencewithpreviousmonth
- Valueduringpreviousmonth/season
Lifehistoryevents
Breeding,litter,gestation
LiteratureThemonthofyearforeachsessionisassociatedwitheachstageoflifehistory
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Impactofmanagementontheproportionofgreysquirrelscarryinginfections
ModelaimsTheoccurrenceofdiseasewillbemonitoredovertime,inordertoassessitsrelationshipwithvariousfactorssuchasthepresenceofredsquirrels,cullingofgreysquirrels,controlpractices,effort,spatialdistribution,anddeterminewhetheranyimpactoftheincidenceisaffectedbyhabitatandlandscapevariables.
Thetissuesamplesrequiredforthisanalysisarenotyetcollectedand/orrecorded.Themetadatainvolvedinthisanalysiswillrequirebeingcomputedatseveralscalesandmatchedtotissuesampledataaswellasthegeneralpopulationatthetimeofsampling.
Progress:recordingtemplatesandtissueanalysisDatarecordingtemplateswereupdatedinordertoaccommodateandautomatetissuesampledatatootherinformationpreviouslycollectedonindividualsquirrels;discussionwithpartnersinordertofinalizethisadaptationisongoing.
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NextSteps
Theplanofworkforthenextyearincludes:1. Finalisingthedatarecordingtemplates.Withallprojectpartners,asquicklyaspossibletoenabletoregularflowofdataforthemodelling.TodothisNUneedto:
-PlanmeetingsandsitevisitstodiscussdatarecordingwithRSUpartners(includingnewrecruits)-Incorporatetissuesamplinginformationtotemplates
ThiswillalsorequirepartnerswithresponsibilityforconservationactionsC1-4to:
-Improvecommunication,designateacontactpersontocommunicatewithNU-Prioritisefinalisingdatacollectiontemplates-ProvideallRSUdataintheagreedformat-PartnerstoprovidepastRSUdatainagreedformat
2. Modeldevelopment.WithmoredataavailableNUwillworkonthemodelstructuresandinvestigatetheappropriatescalesforthemodels.Wewillalsocommunicatewithprojectpartnerstoidentify:
-Bestmethodsforcommunicatingfindingswithpartners(whatisuseful)-Howtoinformmanagementplanning,todothisweneedanunderstandingofthecurrentwaycontrolisplanned,e.g.whichwoodlandsaretargeting(andwhereiseffortlacking)
3. NUwillhostafundedItalianinternwhowillcarryoutleastcostpathwaysmodellinginGIStolinkhabitatsuitabilitymappingtotheearlywarningsystemdevelopment.
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RpackagesreferencesRCoreTeam(2014).R:Alanguageandenvironmentforstatisticalcomputing.RFoundationforStatisticalComputing,Vienna,Austria.http://www.R-project.org/
Readingshapefilesandprocessingspatialdata
RobertJ.Hijmans(2015).geosphere:SphericalTrigonometry.Rpackageversion1.5-1.http://CRAN.R-project.org/package=geosphereRogerBivand,GianfrancoPiras(2015).(spdep)ComparingImplementationsofEstimationMethodsforSpatialEconometrics.JournalofStatisticalSoftware,63(18),1-36.URLhttp://www.jstatsoft.org/v63/i18/Bivand,R.S.,Hauke,J.,andKossowski,T.(2013).(spdep)ComputingtheJacobianinGaussianspatialautoregressivemodels:Anillustratedcomparisonofavailablemethods.GeographicalAnalysis,45(2),150-179.RobertJ.Hijmans(2015).raster:GeographicDataAnalysisandModeling.Rpackageversion2.5-2.http://CRAN.R-project.org/package=rasterRogerBivand,TimKeittandBarryRowlingson(2016).rgdal:BindingsfortheGeospatialDataAbstractionLibrary.Rpackageversion1.1-8.http://CRAN.R-project.org/package=rgdalRogerBivandandColinRundel(2016).rgeos:InterfacetoGeometryEngine-OpenSource(GEOS).Rpackageversion0.3-19.http://CRAN.R-project.org/package=rgeosRogerBivandandNicholasLewin-Koh(2016).maptools:ToolsforReadingandHandlingSpatialObjects.Rpackageversion0.8-39.http://CRAN.R-project.org/package=maptoolsOriginalScodebyRichardA.Becker,AllanR.Wilks.RversionbyRayBrownrigg.EnhancementsbyThomasPMinkaandAlexDeckmyn.(2016).maps:DrawGeographicalMaps.Rpackageversion3.1.0.http://CRAN.R-project.org/package=mapsPebesma,E.J.,R.S.Bivand,2005.ClassesandmethodsforspatialdatainR.RNews5(2),http://cran.r-project.org/doc/Rnews/MartijnTennekes(2016).tmap:ThematicMaps.Rpackageversion1.4.http://CRAN.R-project.org/package=tmapRobertJ.Hijmans,StevenPhillips,JohnLeathwickandJaneElith(2016).dismo:SpeciesDistributionModeling.Rpackageversion1.0-15.http://CRAN.R-project.org/package=dismo
Readingandcompilingrecords
HadleyWickham(2016).readxl:ReadExcelFiles.Rpackageversion0.1.1.http://CRAN.R-project.org/package=readxlHadleyWickham(2016).tidyr:EasilyTidyDatawith`spread()`and`gather()`Functions.Rpackageversion0.4.1.http://CRAN.R-project.org/package=tidyrH.Wickham.Reshapingdatawiththereshapepackage.JournalofStatisticalSoftware,21(12),2007.ClaudiaVitolo().rnrfa:UKNationalRiverFlowArchiveDatafromR.Rpackageversion1.3.0.http://CRAN.R-project.org/package=rnrfaHadleyWickham(2011).TheSplit-Apply-CombineStrategyforDataAnalysis.JournalofStatisticalSoftware,40(1),1-29.URLhttp://www.jstatsoft.org/v40/i01/HadleyWickhamandRomainFrancois(2015).dplyr:AGrammarofDataManipulation.Rpackageversion0.4.3.http://CRAN.R-project.org/package=dplyr
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Illustration
H.Wickham.ggplot2:ElegantGraphicsforDataAnalysis.Springer-VerlagNewYork,2009.D.KahleandH.Wickham.ggmap:SpatialVisualizationwithggplot2.TheRJournal,5(1),144-161.URLhttp://journal.r-project.org/archive/2013-1/kahle-wickham.pdfSarkar,Deepayan(2008)Lattice:MultivariateDataVisualizationwithR.Springer,NewYork.ISBN978-0-387-75968-5ErichNeuwirth(2014).RColorBrewer:ColorBrewerPalettes.Rpackageversion1.1-2.http://CRAN.R-project.org/package=RColorBrewer
Statisticalmodelling
IanFiske,RichardChandler(2011).unmarked:AnRPackageforFittingHierarchicalModelsofWildlifeOccurrenceandAbundance.JournalofStatisticalSoftware,43(10),1-23.URLhttp://www.jstatsoft.org/v43/i10/AchimZeileis,ChristianKleiber,SimonJackman(2008).RegressionModelsforCountDatainR.JournalofStatisticalSoftware27(8).URLhttp://www.jstatsoft.org/v27/i08/DouglasBates,MartinMaechler,BenBolker,SteveWalker(2015).FittingLinearMixed-EffectsModelsUsinglme4.JournalofStatisticalSoftware,67(1),1-48.doi:10.18637/jss.v067.i01.
Dataformattingandediting
HadleyWickham(2015).stringr:Simple,ConsistentWrappersforCommonStringOperations.Rpackageversion1.0.0.http://CRAN.R-project.org/package=stringrAchimZeileisandGaborGrothendieck(2005).zoo:S3InfrastructureforRegularandIrregularTimeSeries.JournalofStatisticalSoftware,14(6),1-27.doi:10.18637/jss.v014.i06GarrettGrolemund,HadleyWickham(2011).DatesandTimesMadeEasywithlubridate.JournalofStatisticalSoftware,40(3),1-25.URLhttp://www.jstatsoft.org/v40/i03/