red squirrels united - evolving ias grey squirrel

www.redsquirrelsunited.org.uk LIFE14 NAT/UK/000467

Red Squirrels United - Evolving IAS grey squirrel management techniques in the UK and Ireland

D3–Ecosystemfunctionrestorationassessment:

Outcomesofmonitoringevaluationsonallyear1conservationactions

October2017

ZeldavanderWaalandAileenMill

NewcastleUniversity


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.


3/19

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


4/19

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.


5/19

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.


6/19

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.


7/19

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


8/19

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.


9/19

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


10/19

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.


11/19

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


12/19

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.


13/19

Figure2:ExampleofpartitioningofprimaryperiodsforNWTpartnersredandgreysquirrelcountdata.


14/19

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.


15/19

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/

red squirrels united - evolving ias grey squirrel

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