connectivity网易轻博客
网易轻博客 时间:2021-01-13 阅读:()
1ElectronicSupplementaryMaterialGeneticAssessmentofEnvironmentalFeaturesthatInfluenceDeerDispersal:ImplicationsforPrion-InfectedPopulationsAmyC.
Kelly,NohraE.
Mateus-Pinilla,WilliamBrown,MarilynO.
Ruiz,MarlisR.
Douglas,MichaelE.
Douglas,PaulShelton,TomBeissel,JanNovakofskiMicrosatelliteMarkersThefollowingmicrosatelliteswereemployedinthisstudy:BM1225,BM4107,CSN3,(Bishopetal.
1994),IGF-1(Kirkpatrick1992),OBCAM(Friesetal.
1993),OarFcb304(Buchananetal.
1993),RT20,RT23,RT27(Wilsonetal.
1997)andSrcrsp-10(Bhebheetal.
1994).
Welabeledforwardprimerswithfluorescentdyes(NED,HEX,FAM)andseparatedmicrosatellitefragmentsonanABI3730XLcapillarysequencer(AppliedBiosystems,FosterCity,CA).
WevisualizedmicrosatellitegenotypeswithGeneMapper(v.
4.
0;AppliedBiosystems,FosterCity,CA).
WeusedMicro-checker(v.
2.
2.
3;VanOosterhoutetal.
2004)toevaluategenotypingerrorsusingexpectedallelefrequenciesderivedunderHardy-Weinbergequilibrium(HWE).
FSTSurfaceProjectionWeusedtheSingleSpeciesGeneticDivergenceoptionwithintheGeneticLandscapesGIS(GeographicInformationSystem)ToolboxtoprojectasurfacefrompairwiseFSTvaluescalculatedbetweenall31studysites.
TheprogramfirstassociatedpairwiseFST2valueswithmidpointsbetweenallstudysitesandanetworkofnearestneighbors.
Spatialinterpolationwasthenperformedusinganinversedistanceweightedinterpolationalgorithmtoestimategeneticdistancesalongagridoverlaidonthestudyarea.
GeneticdistancesforallpointsacrossthegridwereinterpolatedsuchthatmidpointFSTvaluesthatwerespatiallycloserinfluencedtheestimatemoresothanthosethatweredistant.
Moredetailsontheinterpolationprocedurearedescribedinhttp://www.
werc.
usgs.
gov/productdetails.
aspxid=4017.
FRAGSTATSmetricsTheConnectanceIndex(CONNECT)measuresfunctionalconnectivity,meaningthatgridcellsinthedatathatdepictthetargetvariablearenotliterallyadjacent,buttheyareconsideredadjacent(orconnected)withinagiventhresholddistance.
Inthiscase,adjacencywasdefinedascellswithin100mofeachother.
Theuser-defined100mthresholdwasusedtoaccountforpotentialimprecisionofdataclassificationsatfinespatialresolutionsandtoprovideamorerealistic(i.
e.
,functional)depictionofhowdeermightinteractwiththelandscape.
Themetricitselfisapercentage,witharangeof0to100.
Morespecifically,itmeasuresthepercentageoftargetvariableadjacencies(connectionsorjoins)relativetoallpossibleadjacencies.
FormoreinformationontheConnectanceIndexsee:http://www.
umass.
edu/landeco/research/fragstats/documents/Metrics/Connectivity%20Metrics/Metrics/C122%20-%20CONNECT.
htm3ThePatchCohesionIndex(COHESION)isasecondmeasureofconnectivityofalandscapevariable.
Thismetrictakesintoaccountphysicaladjacency(withoutathreshold)incombinationwiththesizeandshapeofthepatches.
Takingforestasanexample,ahigherCOHESIONvaluewouldoccurinalandscapewithlargerandcompactpatchescomparedtoonewithsmallorconvolutedpatches.
FormoreinformationonthePatchCohesionIndexsee:http://www.
umass.
edu/landeco/research/fragstats/documents/Metrics/Connectivity%20Metrics/Metrics/C121%20-%20COHESION.
htmTheClumpinessIndex(CLUMPY)isametricindicatinghowcontiguousordispersedaretheadjacentpatchesofalandscapevariable.
AhighervalueofCLUMPYwouldoccurifseveralpatcheswerelocatedclosetogetherratherthanbeingmoreuniformlydistributed.
FormoreinformationontheClumpinessIndex(CLUMPY)seehttp://www.
umass.
edu/landeco/research/fragstats/documents/Metrics/Contagion%20-%20Interspersion%20Metrics/Metrics/C115%20-%20CLUMPY.
htmThePerimeter-AreaFractalDimension(PAFRAC)isashapemetricdeterminedacrossarangeofspatialscales.
PARFRACislowforpatcheswithsimpleperimetersandincreasesforpatchshapeswithhighlyconvolutedperimeters.
FormoreinformationonthePerimeter-AreaFractalDimensionIndex(PAFRAC),seehttp://www.
umass.
edu/landeco/research/fragstats/documents/Metrics/Shape%20Metrics/Metrics/C23%20-%20PAFRAC.
htm.
Multivariatelinearregressionanalysis4DescriptionandsourceoflandscapevariablesincludedinmultivariateregressionanalysisarelistedinTableS1.
Topreventoverlyinfluentialobservationsfrombiasingourmodels,weusedleveragescores,Cook'sDvalues,andstandardizedinfluencevaluestoidentifyoutliers(Kieetal.
2002;ChatterjeeandHadi2009;Anlaufetal.
2011).
Leveragescoresidentifyobservationsthatresultinlargechangesinregressionlinefitupontheirdeletion.
Wecalculatedleverage(pi)accordingtoChatterjeeandHadi(1986)andconsideredobservationsoverlyinfluentialwhenpi>2p/N(p=numberofindependentvariablesinthemodel;N=numberofobservations).
Cook'sDvalueswerecalculatedaccordingtoCook(1977)andcomparedtoanFdistributionwithα=0.
05and(N-p)degreesoffreedom.
AllCook'sDvalues>thecriticalFvaluewereconsideredoverlyinfluentialandremovedfromthemodel(Cook1977).
LeveragescoresandCook'sDallowedustodeterminetheeffectsofoutliersontheoverallmodel,butstandardizedinfluencevalues(DFFITS)allowedustoexaminetheinfluenceofeachobservationonitspredictedvalue.
WecalculatedDFFITSaccordingtoChatterjeeandHadi(1986)andeliminatedobservationsyieldingvalues>2)/(Np(ChatterjeeandHadi1986).
Usingthesethreecriteria,weidentifiedthirteenobservationsoutof465(2.
8%)thatwereoutliersandafterstringentlyevaluatingtheirbasis(Motulsky2010),weomittedthemduringfurtheranalyses.
Themajorityoftheoutliersremoved(7/13)involvedstudysitesthathadrelativelylowsamplesizes.
Threeofthirteenoutliersinvolvedpairwisecomparisonswithstudysite27,thoughtheremainingtenoutliersappearedtoinvolvestudysitesthatwererandomlydistributedgeographically.
AsingleoutlierhadthehighestFSTvalueobserved,thoughtheremainingoutliersdidnotexhibitunusuallyhighorlowFSTvaluesascomparedtotherestofthe5dataset.
WecomparedvaluesofdependentvariablesofoutlierstovaluesfortherestofthedatabyexaminingboxplotsandplottingdependentvariablesagainstFSTvalues(datanotshown).
Trendsinthedistributionofvaluesfordependentvariablewerenotapparentinoutliersascomparedtotherestofthedata.
Whentwoormorelandscapevariableswerehighlycorrelated(Pearson'srP>0.
7),thepredictorwiththelowestpartialcorrelationinthefullmodelwasremoved.
RemovinglandscapevariableswithrP>0.
7(n=7)resultedinagenerallackofcollinearityamongpredictorsasdeterminedbyvarianceinflationfactors.
CorrelatedpredictorsthatwereremovedfromthemodelarelistedinTableS2.
Weusedvarianceinflationfactors(VIF)toevaluatetheincreaseinvarianceforestimatedregressioncoefficientsresultingfromcollinearpredictors,withVIF>10indicativeofhighmulticollinearity(Kutneretal.
2004).
Afterremovinghighlycorrelatedvariables,wecalculatedvarianceinflationfactorsforindependentvariablesandfoundthatthevarianceofestimatedregressioncoefficientswasnotsubstantiallyincreasedbycollinearpredictorsasVIFvaluesforallpredictorswere0.
7thatweresubsequentlyremovedfromthemodel.
VariableCorrelateDirectionofCorrelationVariableRemoved*%GrasslandSlope+%GrasslandForestCONNECTDevelopedCONNECT+ForestCONNECT%GrasslandGrasslandCONNECT-%GrasslandForestCONNECTGrasslandCONNECT+ForestCONNECTForestCONNECTWaterCONNECT+ForestCONNECTAgricultureCLUMPY%Agriculture-AgricultureCLUMPY%RiparianSlope+Slope%GrasslandForestCLUMPY-%GrasslandForestCONNECTDistance-ForestCONNECTSlopeGrasslandCOHESION+SlopeDevelopedCONNECTGrasslandCONNECT+DevelopedCONNECTGrasslandPAFRACSlope+SlopeDevelopedCONNECTWaterCONNECT+DevelopedCONNECT%GrasslandAgricultureCLUMPY-%Grassland%GrasslandAgriculturePAFRAC+%GrasslandDistanceDevelopedCONNECT-DevelopedCONNECTForestCONNECTWaterCONNECT+ForestCONNECT%AgricultureAgricultureCOHESION+AgricultureCOHESIONWaterCOHESIONWaterCLUMPY+WaterCLUMPY*thepredictorwiththelowestpartialcorrelationinthefullmodelwasremoved.
10TableS3.
Percentsignificant(P<0.
05)localr,rangeoflocalr,andmeanlocalrforfive,15and25nearestneighborsingroupsofwhite-taileddeerinnorthernIllinois(NIL),DuPageCounty(DuP),andWisconsin(WI).
GroupNumberofNearestNeighbors51525%P<0.
051MaxrMeanr%P<0.
051MaxrMeanr%P<0.
051MaxrMeanrAdultMales5.
70.
160.
134.
40.
110.
087.
90.
080.
06MaleYearlings7.
00.
280.
1711.
60.
180.
0914.
10.
120.
06MaleFawns9.
30.
190.
1511.
30.
090.
078.
20.
060.
05AdultMalesandFemaleYearlings6.
40.
270.
147.
60.
120.
088.
10.
090.
06AdultFemales14.
70.
320.
1618.
80.
240.
0920.
50.
150.
07FemaleYearlings5.
70.
160.
124.
80.
110.
074.
80.
070.
05FemaleFawns17.
10.
240.
1415.
20.
130.
0919.
50.
090.
06AdultFemalesandFawns16.
00.
310.
1622.
80.
230.
1024.
50.
190.
081NumberofautocorrelationcoefficientsthatweresignificantatP<0.
05dividedbythetotalnumberautocorrelationcoefficientscalculatedforeachgroup*100.
Includingonlysignificantlocalrvalues.
Kelly,NohraE.
Mateus-Pinilla,WilliamBrown,MarilynO.
Ruiz,MarlisR.
Douglas,MichaelE.
Douglas,PaulShelton,TomBeissel,JanNovakofskiMicrosatelliteMarkersThefollowingmicrosatelliteswereemployedinthisstudy:BM1225,BM4107,CSN3,(Bishopetal.
1994),IGF-1(Kirkpatrick1992),OBCAM(Friesetal.
1993),OarFcb304(Buchananetal.
1993),RT20,RT23,RT27(Wilsonetal.
1997)andSrcrsp-10(Bhebheetal.
1994).
Welabeledforwardprimerswithfluorescentdyes(NED,HEX,FAM)andseparatedmicrosatellitefragmentsonanABI3730XLcapillarysequencer(AppliedBiosystems,FosterCity,CA).
WevisualizedmicrosatellitegenotypeswithGeneMapper(v.
4.
0;AppliedBiosystems,FosterCity,CA).
WeusedMicro-checker(v.
2.
2.
3;VanOosterhoutetal.
2004)toevaluategenotypingerrorsusingexpectedallelefrequenciesderivedunderHardy-Weinbergequilibrium(HWE).
FSTSurfaceProjectionWeusedtheSingleSpeciesGeneticDivergenceoptionwithintheGeneticLandscapesGIS(GeographicInformationSystem)ToolboxtoprojectasurfacefrompairwiseFSTvaluescalculatedbetweenall31studysites.
TheprogramfirstassociatedpairwiseFST2valueswithmidpointsbetweenallstudysitesandanetworkofnearestneighbors.
Spatialinterpolationwasthenperformedusinganinversedistanceweightedinterpolationalgorithmtoestimategeneticdistancesalongagridoverlaidonthestudyarea.
GeneticdistancesforallpointsacrossthegridwereinterpolatedsuchthatmidpointFSTvaluesthatwerespatiallycloserinfluencedtheestimatemoresothanthosethatweredistant.
Moredetailsontheinterpolationprocedurearedescribedinhttp://www.
werc.
usgs.
gov/productdetails.
aspxid=4017.
FRAGSTATSmetricsTheConnectanceIndex(CONNECT)measuresfunctionalconnectivity,meaningthatgridcellsinthedatathatdepictthetargetvariablearenotliterallyadjacent,buttheyareconsideredadjacent(orconnected)withinagiventhresholddistance.
Inthiscase,adjacencywasdefinedascellswithin100mofeachother.
Theuser-defined100mthresholdwasusedtoaccountforpotentialimprecisionofdataclassificationsatfinespatialresolutionsandtoprovideamorerealistic(i.
e.
,functional)depictionofhowdeermightinteractwiththelandscape.
Themetricitselfisapercentage,witharangeof0to100.
Morespecifically,itmeasuresthepercentageoftargetvariableadjacencies(connectionsorjoins)relativetoallpossibleadjacencies.
FormoreinformationontheConnectanceIndexsee:http://www.
umass.
edu/landeco/research/fragstats/documents/Metrics/Connectivity%20Metrics/Metrics/C122%20-%20CONNECT.
htm3ThePatchCohesionIndex(COHESION)isasecondmeasureofconnectivityofalandscapevariable.
Thismetrictakesintoaccountphysicaladjacency(withoutathreshold)incombinationwiththesizeandshapeofthepatches.
Takingforestasanexample,ahigherCOHESIONvaluewouldoccurinalandscapewithlargerandcompactpatchescomparedtoonewithsmallorconvolutedpatches.
FormoreinformationonthePatchCohesionIndexsee:http://www.
umass.
edu/landeco/research/fragstats/documents/Metrics/Connectivity%20Metrics/Metrics/C121%20-%20COHESION.
htmTheClumpinessIndex(CLUMPY)isametricindicatinghowcontiguousordispersedaretheadjacentpatchesofalandscapevariable.
AhighervalueofCLUMPYwouldoccurifseveralpatcheswerelocatedclosetogetherratherthanbeingmoreuniformlydistributed.
FormoreinformationontheClumpinessIndex(CLUMPY)seehttp://www.
umass.
edu/landeco/research/fragstats/documents/Metrics/Contagion%20-%20Interspersion%20Metrics/Metrics/C115%20-%20CLUMPY.
htmThePerimeter-AreaFractalDimension(PAFRAC)isashapemetricdeterminedacrossarangeofspatialscales.
PARFRACislowforpatcheswithsimpleperimetersandincreasesforpatchshapeswithhighlyconvolutedperimeters.
FormoreinformationonthePerimeter-AreaFractalDimensionIndex(PAFRAC),seehttp://www.
umass.
edu/landeco/research/fragstats/documents/Metrics/Shape%20Metrics/Metrics/C23%20-%20PAFRAC.
htm.
Multivariatelinearregressionanalysis4DescriptionandsourceoflandscapevariablesincludedinmultivariateregressionanalysisarelistedinTableS1.
Topreventoverlyinfluentialobservationsfrombiasingourmodels,weusedleveragescores,Cook'sDvalues,andstandardizedinfluencevaluestoidentifyoutliers(Kieetal.
2002;ChatterjeeandHadi2009;Anlaufetal.
2011).
Leveragescoresidentifyobservationsthatresultinlargechangesinregressionlinefitupontheirdeletion.
Wecalculatedleverage(pi)accordingtoChatterjeeandHadi(1986)andconsideredobservationsoverlyinfluentialwhenpi>2p/N(p=numberofindependentvariablesinthemodel;N=numberofobservations).
Cook'sDvalueswerecalculatedaccordingtoCook(1977)andcomparedtoanFdistributionwithα=0.
05and(N-p)degreesoffreedom.
AllCook'sDvalues>thecriticalFvaluewereconsideredoverlyinfluentialandremovedfromthemodel(Cook1977).
LeveragescoresandCook'sDallowedustodeterminetheeffectsofoutliersontheoverallmodel,butstandardizedinfluencevalues(DFFITS)allowedustoexaminetheinfluenceofeachobservationonitspredictedvalue.
WecalculatedDFFITSaccordingtoChatterjeeandHadi(1986)andeliminatedobservationsyieldingvalues>2)/(Np(ChatterjeeandHadi1986).
Usingthesethreecriteria,weidentifiedthirteenobservationsoutof465(2.
8%)thatwereoutliersandafterstringentlyevaluatingtheirbasis(Motulsky2010),weomittedthemduringfurtheranalyses.
Themajorityoftheoutliersremoved(7/13)involvedstudysitesthathadrelativelylowsamplesizes.
Threeofthirteenoutliersinvolvedpairwisecomparisonswithstudysite27,thoughtheremainingtenoutliersappearedtoinvolvestudysitesthatwererandomlydistributedgeographically.
AsingleoutlierhadthehighestFSTvalueobserved,thoughtheremainingoutliersdidnotexhibitunusuallyhighorlowFSTvaluesascomparedtotherestofthe5dataset.
WecomparedvaluesofdependentvariablesofoutlierstovaluesfortherestofthedatabyexaminingboxplotsandplottingdependentvariablesagainstFSTvalues(datanotshown).
Trendsinthedistributionofvaluesfordependentvariablewerenotapparentinoutliersascomparedtotherestofthedata.
Whentwoormorelandscapevariableswerehighlycorrelated(Pearson'srP>0.
7),thepredictorwiththelowestpartialcorrelationinthefullmodelwasremoved.
RemovinglandscapevariableswithrP>0.
7(n=7)resultedinagenerallackofcollinearityamongpredictorsasdeterminedbyvarianceinflationfactors.
CorrelatedpredictorsthatwereremovedfromthemodelarelistedinTableS2.
Weusedvarianceinflationfactors(VIF)toevaluatetheincreaseinvarianceforestimatedregressioncoefficientsresultingfromcollinearpredictors,withVIF>10indicativeofhighmulticollinearity(Kutneretal.
2004).
Afterremovinghighlycorrelatedvariables,wecalculatedvarianceinflationfactorsforindependentvariablesandfoundthatthevarianceofestimatedregressioncoefficientswasnotsubstantiallyincreasedbycollinearpredictorsasVIFvaluesforallpredictorswere0.
7thatweresubsequentlyremovedfromthemodel.
VariableCorrelateDirectionofCorrelationVariableRemoved*%GrasslandSlope+%GrasslandForestCONNECTDevelopedCONNECT+ForestCONNECT%GrasslandGrasslandCONNECT-%GrasslandForestCONNECTGrasslandCONNECT+ForestCONNECTForestCONNECTWaterCONNECT+ForestCONNECTAgricultureCLUMPY%Agriculture-AgricultureCLUMPY%RiparianSlope+Slope%GrasslandForestCLUMPY-%GrasslandForestCONNECTDistance-ForestCONNECTSlopeGrasslandCOHESION+SlopeDevelopedCONNECTGrasslandCONNECT+DevelopedCONNECTGrasslandPAFRACSlope+SlopeDevelopedCONNECTWaterCONNECT+DevelopedCONNECT%GrasslandAgricultureCLUMPY-%Grassland%GrasslandAgriculturePAFRAC+%GrasslandDistanceDevelopedCONNECT-DevelopedCONNECTForestCONNECTWaterCONNECT+ForestCONNECT%AgricultureAgricultureCOHESION+AgricultureCOHESIONWaterCOHESIONWaterCLUMPY+WaterCLUMPY*thepredictorwiththelowestpartialcorrelationinthefullmodelwasremoved.
10TableS3.
Percentsignificant(P<0.
05)localr,rangeoflocalr,andmeanlocalrforfive,15and25nearestneighborsingroupsofwhite-taileddeerinnorthernIllinois(NIL),DuPageCounty(DuP),andWisconsin(WI).
GroupNumberofNearestNeighbors51525%P<0.
051MaxrMeanr%P<0.
051MaxrMeanr%P<0.
051MaxrMeanrAdultMales5.
70.
160.
134.
40.
110.
087.
90.
080.
06MaleYearlings7.
00.
280.
1711.
60.
180.
0914.
10.
120.
06MaleFawns9.
30.
190.
1511.
30.
090.
078.
20.
060.
05AdultMalesandFemaleYearlings6.
40.
270.
147.
60.
120.
088.
10.
090.
06AdultFemales14.
70.
320.
1618.
80.
240.
0920.
50.
150.
07FemaleYearlings5.
70.
160.
124.
80.
110.
074.
80.
070.
05FemaleFawns17.
10.
240.
1415.
20.
130.
0919.
50.
090.
06AdultFemalesandFawns16.
00.
310.
1622.
80.
230.
1024.
50.
190.
081NumberofautocorrelationcoefficientsthatweresignificantatP<0.
05dividedbythetotalnumberautocorrelationcoefficientscalculatedforeachgroup*100.
Includingonlysignificantlocalrvalues.
- connectivity网易轻博客相关文档
- 香港网易轻博客
- multiple网易轻博客
- 金融服务外包动态
- x2100网易轻博客
- 意象网易轻博客
- descriptive网易轻博客
CloudCone中国新年特别套餐,洛杉矶1G内存VPS年付13.5美元起
CloudCone针对中国农历新年推出了几款特别套餐， 其中2019年前注册的用户可以以13.5美元/年的价格购买一款1G内存特价套餐，以及另外提供了两款不限制注册时间的用户可购买年付套餐。CloudCone是Quadcone旗下成立于2017年的子品牌，提供VPS及独立服务器租用，也是较早提供按小时计费VPS的商家之一，支持使用PayPal或者支付宝等付款方式。下面列出几款特别套餐配置信息。CP...
易探云美国云服务器评测，主机低至33元/月,336元/年
美国服务器哪家平台好?美国服务器无需备案，即开即用，上线快。美国服务器多数带防御，且有时候项目运营的时候，防御能力是用户考虑的重点，特别是网站容易受到攻击的行业。现在有那么多美国一年服务器，哪家的美国云服务器好呢？美国服务器用哪家好?这里推荐易探云，有美国BGP、美国CN2、美国高防、美国GIA等云服务器，线路优化的不错。易探云刚好就是做香港及美国云服务器的主要商家之一，我们来看一下易探云美国云服...
MineServer：洛杉矶CN2 GIA VPS/512MB内存/20GB NVME/800GB流量/200Mbps/KVM,58元/季
mineserver怎么样?mineserver是一家国人商家，主要提供香港CN2 KVM VPS、香港CMI KVM VPS、日本CN2 KVM VPS、洛杉矶cn2 gia端口转发等服务，之前介绍过几次，最近比较活跃。这家新推出了洛杉矶CN2 GIA VPS，512MB内存/20GB NVME/800GB流量/200Mbps/KVM,58元/季，并且进行了带宽升级，同时IP更改为美国IP。点击...
网易轻博客为你推荐
-
美国vps服务器美国VPS和美国服务器速度快吗美国vps服务器打听下,国外V P S服务器哪个好?企业虚拟主机一个企业网站是什么比较划算，买服务器还是买虚拟主机呢注册国际域名哪里的国际域名注册便宜？海外服务器租用国外服务器租用与国内服务器租用有哪些区别me域名.me域名和com的价值对比，懂的告诉我呀虚拟主机申请个人虚拟主机怎么申请？网站空间域名什么是网站域名和网站空间海外域名怎样注册国外域名？网站空间购买哪里买网站空间好？