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
PacificRack(年付低至19美元),夏季促销PR-M系列和多IP站群VPS主机
这几天有几个网友询问到是否有Windows VPS主机便宜的VPS主机商。原本他们是在Linode、Vultr主机商挂载DD安装Windows系统的，有的商家支持自定义WIN镜像，但是这些操作起来特别效率低下，每次安装一个Windows系统需要一两个小时，所以如果能找到比较合适的自带Windows系统的服务器那最好不过。这不看到PacificRack商家有提供夏季促销活动，其中包括年付便宜套餐的P...
TTcloud：日本独立服务器促销活动,价格$70/月起,季付送10Mbps带宽
ttcloud怎么样?ttcloud是一家海外服务器厂商，运营服务器已经有10年时间，公司注册地址在香港地区，业务范围包括服务器托管，机柜托管，独立服务器等在内的多种服务。我们后台工单支持英文和中文服务。TTcloud最近推出了新上架的日本独立服务器促销活动，价格 $70/月起，季付送10Mbps带宽。也可以跟进客户的需求进行各种DIY定制。点击进入：ttcloud官方网站地址TTcloud拥有自...
Megalayer新加坡服务器国际带宽线路测评
前几天有关注到Megalayer云服务器提供商有打算在月底的时候新增新加坡机房，这个是继美国、中国香港、菲律宾之外的第四个机房。也有工单询问到官方，新加坡机房有包括CN2国内优化线路和国际带宽，CN2优化线路应该是和菲律宾差不多的。如果我们追求速度和稳定性的中文业务，建议还是选择CN2优化带宽的香港服务器。这里有要到Megalayer新加坡服务器国际带宽的测试服务器，E3-1230配置20M国际带...
网易轻博客为你推荐
-
虚拟空间主机虚拟主机和空间有什么关系？linux主机【windows主机换Linux主机该怎么弄啊？需要注意些什么呢？】中文域名注册查询域名还分中文和英文的吗，在哪里可以查到中文域名到期了？com域名空间.com的域名+300M的空间要多少钱?免费虚拟主机申请谁有1年免费的虚拟主机申请地址吖？域名主机IDC（主机域名)是什么意思？美国vps主机我用的美国VPS主机429元/月，感觉好贵，请问有比较便宜点的吗？代理主机主机做成代理服务器，其他局域网内的电脑必须通过我的这个网络出去代理主机如何将我工作的电脑设置为代理主机 让我回家以后可以用家里的电脑连接店里的主机访问网络域名购买域名注册和购买是一个意思吗？