简介：Asequentialextractionmethodforthefractionationofphosphorus(P)inlakesedimentswasusedtoanalyzephosphorusfractionsofsedimentstakenfromthreelarge,shallow,eutrophicfreshwaterlakesofChina-TaihuLake,ChaohuLake,andLongganLake.AllthreelakesarelocatedinthelowerreachesoftheChangjiangRiver(YangtzeRiver).InTaihuLakeandChaohuLake,algaebloomsoccurredeveryyear,whileLongganLakewasamacrophyte-dominatedlake.Resultsshowedthatexchangeablephosphorusfractionsweremuchhigherintheeutrophiclakesedimentsthaninthemacrophyte-flourishinglakesediment.Also,theratioofFe:Pinthesedimentsofthealgae-predominantlakeswasgenerallymuchlowerthanthatinthemacrophyte-predominantlakes.Thus,thegeochemicalfractionsofphosphorusinsedimentshadacloserrelationshipwiththetypeofaquaticvegetation.

简介：为旗帜叶角度的基因分离分析用P1，P2，F1，B1，从863B(装饰用的梨树米饭的一根维护者线)和A7444(有大旗帜叶角度的germplasm)的一个十字导出的B2和F2的六代被进行。旗帜叶角度的遗传型和显型在863B(P1)被调查，A7444(P2)和在BC1F1(863B/A7444//863B)的141植物人口。一张SSR基因连接地图被构造，为旗帜叶角度的QTL被检测。包含79信息loci的基因地图被构造，它走完441.6厘米的全部的距离，平均在二附近的loci之间的5.6厘米。结果证明特点被二主要基因加多基因控制，主要基因是更重要的。十五个标记基于单个标记回归分析与旗帜叶角度显示出高度重要的关联。为标志叶角度的二QTL(qFLA2和qFLA8)被两个检测在软件WinQTLCart2.5的合成间隔方法和合成间隔方法基于在QTL的混合线性模型联网2.0。分别地，qFLA2解释了10.50%phenotypic和13.28%变化并且在RM300和RM145的间隔位于染色体2的短手臂。分别地，qFLA8解释了9.59%phenotypic和7.64%变化并且在间隔flankingRM6215和RM8265位于染色体8的长手臂。在二QTL的积极等位基因两个都从A7444被贡献。

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简介：TheexcessiveexploitationandharvestingofforestresourcesduetoChina’seconomicconstructiondemandshavemadeitsforestresourcefallingintostructuralcrisis.Overthelastthreedecades,theareaofChina’snaturalforestsdecreasedby11millionha.Theareaofmaturedforestshasdecreasedfrom12millionhainearly50sto5.6millionhaatp

简介：Background:Withthelossofspeciesworldwideduetoanthropogenicfactors,especiallyinforestedecosystems,ithasbecomemoreurgentthanevertounderstandthebiodiversity-ecosystemfunctioningrelationship(BEFR).BEFRresearchinforestedecosystemsisverylimitedandthusstudiesthatincorporategreatergeographiccoverageandstructuralcomplexityareneeded.Methods:Wecompiledground-measureddatafromapprox.onehalfmilionforestinventorysampleplotsacrossthecontiguousUnitedStates,Alaska,andnortheasternChinatomaptreespeciesrichness,foreststocking,andproductivityatacontinentalscale.Basedonthesedata,weinvestigatedtherelationshipbetweenforestproductivityandtreespeciesdiversity,usingamultipleregressionanalysisandanon-parametricapproachtoaccountforspatialautocorrelation.Results:Ingeneral,forestsintheeasternUnitedStatesconsistedofmoretreespeciesthananyotherregionsinthecountry.ThehighestforeststockingvaluesovertheentirestudyareawereconcentratedinthewesternUnitedStatesandCentralAppalachia.Overall,96.4%ofsampleplots(477,281)showedasignificantpositiveeffectofspeciesrichnessonsiteproductivity,andonly3.6%(17,349)hadaninsignificantornegativeeffect.Conclusions:Thelargenumberofground-measuredplots,aswellasthemagnitudeofgeographicscale,renderedoverwhelmingevidenceinsupportofapositiveBEFR.Thisempiricalevidenceprovidesinsightstoforestmanagementandbiologicalconservationacrossdifferenttypesofforestedecosystems.Foresttimberproductivitymaybeimpairedbythelossofspeciesinforests,andbiologicalconservation,duetoitspotentialbenefitsonmaintainingspeciesrichnessandproductivity,canhaveprofoundimpactsonthefunctioningandservicesofforestedecosystems.

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简介：Background:Theimportanceofstructurallydiverseforestsfortheconservationofbiodiversityandprovisionofawiderangeofecosystemserviceshasbeenwidelyrecognised.However,toolstoquantifystructuraldiversityofforestsinanobjectiveandquantitativewayacrossmanyforesttypesandsitesarestillneeded,forexampletosupportbiodiversitymonitoring.Theexistingapproachestoquantifyforeststructuraldiversityarebasedonsmallgeographicalregionsorsingleforesttypes,typicallyusingonlysmalldatasets.Results:HerewedevelopedanindexofstructuraldiversitybasedonNationalForestInventory(NFI)dataofBadenWurttemberg,Germany,astatewith1.3millionhaofdiverseforesttypesindifferentownerships.Basedonaliteraturereview,11aspectsofstructuraldiversitywereidentifiedaprioriascruciallyimportanttodescribestructuraldiversity.Aninitialcomprehensivelistof52variablesderivedfromNationalForestInventory(NFI)datarelatedtostructuraldiversitywasreducedbyapplyingfiveselectioncriteriatoarriveatonevariableforeachaspectofstructuraldiversity.Thesevariablescomprise1)quadraticmeandiameteratbreastheight(DBH),2)standarddeviationofDBH,3)standarddeviationofstandheight,4)numberofdecayclasses,5)bark-diversityindex,6)treeswithDBH>40cm,7)diversityoffloweringandfructification,8)averagemeandiameterofdowneddeadwood,9)meanDBHofstandingdeadwood,10)treespeciesrichnessand11)treespeciesrichnessintheregenerationlayer.Thesevariableswerecombinedintoasimple,additiveindextoquantifythelevelofstructuraldiversity,whichassumesvaluesbetween0and1.Weappliedthisindexinanexemplarywaytobroadforestcategoriesandownershipstoassessitsfeasibilitytoanalysestructuraldiversityinlarge-scaleforestinventories.Conclusions:Theforeststructureindexpresentedherecanbederivedinasimilarwayfromstandardinventoryvariablesformostotherlarge-scaleforestin

简介：Thispaperfocusesontheuseofmodelsforincreasingtheprecisionofestimatorsinlarge-areaforestsurveys.Itismotivatedbytheincreasingavailabilityofremotelysenseddata,whichfacilitatesthedevelopmentofmodelspredictingthevariablesofinterestinforestsurveys.Wepresent,reviewandcomparethreedifferentestimationframeworkswheremodelsplayacorerole:model-assisted,model-based,andhybridestimation.Thefirsttwoarewellknown,whereasthethirdhasonlyrecentlybeenintroducedinforestsurveys.Hybridinferencemixesdesignbasedandmodel-basedinference,sinceitreliesonaprobabilitysampleofauxiliarydataandamodelpredictingthetargetvariablefromtheauxiliarydata.Wereviewstudiesonlarge-areaforestsurveysbasedonmodel-assisted,modelbased,andhybridestimation,anddiscussadvantagesanddisadvantagesoftheapproaches.Weconcludethatnogeneralrecommendationscanbemadeaboutwhethermodel-assisted,model-based,orhybridestimationshouldbepreferred.Thechoicedependsontheobjectiveofthesurveyandthepossibilitiestoacquireappropriatefieldandremotelysenseddata.Wealsoconcludethatmodellingapproachescanonlybesuccessfullyappliedforestimatingtargetvariablessuchasgrowingstockvolumeorbiomass,whichareadequatelyrelatedtocommonlyavailableremotelysenseddata,andthuspurelyfieldbasedsurveysremainimportantforseveralimportantforestparameters.