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
atcloud：480G超高防御VPS低至$4/月，美国/新加坡等6机房，512m内存/1核/500g硬盘/不限流量
atcloud主要提供常规cloud（VPS）和storage（大硬盘存储）系列VPS，其数据中心分布在美国(俄勒冈、弗吉尼亚)、加拿大、英国、法国、德国、新加坡，所有VPS默认提供480Gbps的超高DDoS防御+不限流量，杜绝DDoS攻击骚扰，比较适合海外建站等相关业务。ATCLOUD.NET是一家成立于2020年的海外主机商，主要提供KVM架构的VPS产品、LXC容器化产品、权威DNS智能解...
台湾云服务器整理推荐UCloud/易探云！
台湾云服务器去哪里买?国内有没有哪里的台湾云服务器这块做的比较好的?有很多用户想用台湾云服务器，那么判断哪家台湾云服务器好，不是按照最便宜或最贵的选择，而是根据您的实际使用目的选择服务器，只有最适合您的才是最好的。总体而言，台湾云服务器的稳定性确实要好于大陆。今天，云服务器网(yuntue.com)小编来介绍一下台湾云服务器哪里买和一年需要多少钱！一、UCloud台湾云服务器UCloud上市云商,...
Raksmart VPS主机如何设置取消自动续费
今天有看到Raksmart账户中有一台VPS主机即将到期，这台机器之前是用来测试评测使用的。这里有不打算续费，这不面对万一导致被自动续费忘记，所以我还是取消自动续费设置。如果我们也有类似的问题，这里就演示截图设置Raksmart取消自动续费。这里我们可以看到上图，在对应VPS主机的【其余操作】中可以看到默认已经是不自动续费，所以我们也不要担心被自动续费的。当然，如果有被自动续费，我们确实不想续费的...
网易轻博客为你推荐
-
独立ip空间如何设置独立IP加到现有空间com域名注册.com的域名注册需要什么证件和资料吗？免费网站域名申请怎么免费上传我的网站呀和免费申请域名ip代理地址ip代理是什么?美国服务器托管美国服务器租用时要注意什么？免费网站空间申请申请免费空间的网站虚拟主机控制面板如何利用虚拟主机控制面板对网站进行管理1g虚拟主机我要做一个下载资料类网站，刚买了一个虚拟主机1G的，提供商说一次，只能上传一个小于10M的文件虚拟主机系统虚拟主机采用什么操作系统?山东虚拟主机青岛网络公司哪家好