简介：Background:Conceptualmodelsofforestdynamicsarepowerfulcognitivetools,whichareindispensableforcommunicatingecologicalideasandknowledge,andindevelopingstrategicapproachesandsettingtargetsforforestconservation,restorationandsustainablemanagement.Forestdevelopmentthroughtimeisconventionallydescribedasadirectional,or'linear',andpredictablesequenceofstagesfrom'bareground'tooldforestrepresentingthe'climax-state'.However,thissimpleviewisincompatiblewiththecurrentknowledgeandunderstandingofintrinsicvariabilityofforestdynamics.Hypothesis:Overlysimpleconceptualmodelsofforestdynamicseasilybecometransformedintobiasedmentalmodelsofhowforestsnaturallydevelopandwhatkindofstructurestheydisplay.Tobeabletocommunicatetheessentialfeaturesanddiversityofforestdynamics,comprehensiveconceptualmodelsareneeded.Forthisend,Kuuluvainen(2009)suggestedarelativelysimpleconceptualmodelofforestdynamics,whichseparatesthreemajormodesofforestdynamics,andincorporatesstatechangesandtransitionsbetweentheforestdynamicsmodesdependingonchangesindisturbanceregime.Conclusions:Conceptualmodelsofforestdynamicsshouldbecomprehensiveenoughtoincorporatebothlongtermdirectionalchangeandshort-termcyclicforestdynamics,aswellastransitionsfromonedynamicsmodetoanotherdependingonchangesinthedrivingdisturbanceregimetype.Modelsthatcapturesuchessentialfeaturesofforestdynamicsareindispensableforeducationalpurposes,insettingreferenceconditionsandindevelopingmethodsinforestconservation,restorationandecosystemmanagement.

简介：Mid-subtropicalforestsarethemainvegetationtypeofglobalterrestrialbiomes,andarecriticalformaintainingtheglobalcarbonbalance.However,estimatesofforestbiomassincrementinmid-subtropicalforestsremainhighlyuncertain.Itiscriticallyimportanttodeterminetherelativeimportanceofdifferentbioticandabioticfactorsbetweenplantsandsoil,particularlywithrespecttotheirinfluenceonplantregrowth.Consequently,itisnecessarytoquantitativelycharacterizethedynamicspatiotemporaldistributionofforestcarbonsinksataregionalscale.Thisstudyusedalarge,long-termdatasetinaboostedregressiontree(BRT)modeltodeterminethemajorcomponentsthatquantitativelycontrolforestbiomassincrementsinamid-subtropicalforestedregion(WuyishanNationalNatureReserve,China).Long-term,stand-leveldatawereusedtoderivetheforestbiomassincrement,withtheBRTmodelbeingappliedtoquantifytherelativecontributionsofvariousbioticandabioticvariablestoforestbiomassincrement.Ourdatashowthattotalbiomass(t)increasedfrom4.629106to5.309106tbetween1988and2010,andthatthemeanbiomassincreasedfrom80.19±0.39tha-1(mean±standarderror)to94.33±0.41tha-1inthestudyregion.Themajorfactorsthatcontrolledbiomass(indecreasingorderofimportance)werethestand,topography,andsoil.Standdensitywasinitiallythemostimportantstandfactor,whileelevationwasthemostimportanttopographicfactor.Soilfactorswereimportantforforestbiomassincrementbuthaveamuchweakerinfluencecomparedtotheothertwocontrollingfactors.Theseresultsprovidebaselineinformationaboutthepracticalutilityofspatialinterpolationmethodsformappingforestbiomassincrementsatregionalscales.更多还原

简介：亚洲与任何另外的区域相比正在经历更快速的经济生长。有金属的土壤和地下水的污染能主要被归因于人的活动；因此，描绘风险的性质和大小到人的风险评价和从沾染物的生态的受体是重要的。风险评价经常是反复的进程，它包含鉴定和充满的数据差距。象多媒体一样的统计方法冒险评价建模的试验性的采样，geostatistical和multivariate是在土壤和地下水在对金属污染的评价使用的三主要方法论。这评论为改进风险计算方法加亮很多大小并且详细说明包含污染，暴露小径和金属的bioavailability的主要来源的鉴定的科学途径。一般来说，对在土壤和世界范围的地下水的金属的风险评价是污染，暴露小径的鉴定，和污染的概率的预言的主要集中的onthelevels。迄今为止，很有限的研究在亚洲在土壤和地下水污染的规定报导了相关环境法律和政策的发展。在在发达国家之中的土壤和地下水污染的规定的发展，变化和限制可以在亚洲为发展中的国家提供有用指导。

简介：DifferencesinseedgerminationandseedlinggrowthamongpopulationsofPinusyunnanensisFranch.werequantifiedtoprovideinformationforuseintheregeneration,reforestation,andimprovementofthisspecies.Seedgerminationandseedlinggrowthtraitsatthenurserystagewereinvestigatedatthepopulationlevel.Weevaluatedrelationshipsbetweenpairsofmeasuredtraitsaswellastheassociationbetweenmeasuredtraitsandgeoclimaticvariablesincludinglongitude,latitude,elevation,temperatureandprecipitation.Seedlinggrowthshowedsignificantdifferencesalthoughalowmagnitudeofvariationwasfoundintheseedgerminationtraitsamongthepopulations.Somepopulationswithstrongandstablegrowth(e.g.,theBaoshanpopulation)maybeselectedassuperiorseedsourcesforregenerationandreforestation.Germinationpercentagewasnegativelycorrelatedwithelevation,andpositivelywithprecipitation.Inaddition,germinationindex,vigorindexandseedlingheightwerepositivelycorrelatedwithprecipitation.Seedgerminationindex,cotyledonnumberandlength,andseedlingdiameterweresignificantlyandpositivelycorrelatedwithseedweight.Strongcorrelationsbetweenpairsoftraitswillbehelpfulinearlyevaluationsfortheselectionofcertainprominenttraits.

简介：Background:Biomassregressionequationsareclaimedtoyieldthemostaccuratebiomassestimatesthanbiomassexpansionfactors(BEFs).Yet,nationalandregionalbiomassestimatesaregenerallycalculatedbasedonBEFs,especiallywhenusingnationalforestinventorydata.ComparisonofregressionequationsbasedandBEF-basedbiomassestimatesarescarce.Thus,thisstudywasintendedtocomparethesetwocommonlyusedmethodsforestimatingtreeandforestbiomasswithregardtoerrorsandbiases.Methods:Thedatawerecollectedin2012and2014.In2012,atwo-phasesamplingdesignwasusedtofittreecomponentbiomassregressionmodelsanddeterminetreeBEFs.In2014,additionaltreeswerefelledoutsidesamplingplotstoestimatethebiasesassociatedwithregressionequationbasedandBEF-basedbiomassestimates;thoseestimateswerethencomparedintermsofthefollowingsourcesoferror:plotselectionandvariability,biomassmodel,modelparameterestimates,andresidualvariabilityaroundmodelprediaion.Results:Theregressionequationbasedbelow-,abovegroundandwholetreebiomassstockswere,approximately,7.7,8.5and8.3%largerthantheBEF-basedones.Forthewholetreebiomassstock,thepercentageofthetotalerrorattributedtofirstphase(randomplotselectionandvariability)was90and88%forregression-andBEF-basedestimates,respeaively,beingtheremainingattributedtobiomassmodels(regressionandBEFmodels,respeaively).ThepercentbiasofregressionequationbasedandBEF-basedbiomassestimatesforthewholetreebiomassstockwere-2.7and5.4%,respeaively.Theerrorsduetomodelparameterestimates,thoseduetoresidualvariabilityaroundmodelprediaion,andthepercentageofthetotalerrorattributedtobiomassmodelwerelargerforBEFmodels(thanforregressionmodels),exceptforstemandstemwoodcomponents.Conclusions:Theregressionequationbasedbiomassstockswerefoundtobeslightlylarger,associatedwithrelativelysmallererrorsandleastbiasedthantheBEF-basedones.Forstemandst