揭示群落结构及其环境响应的联合物种分布模型的研究进展
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摘要
物种分布模型通常用于基础生态和应用生态研究,用来确定影响生物分布和物种丰富度的因素,量化物种与非生物条件的关系,预测物种对土地利用和气候变化的反应,并确定潜在的保护区.在传统的物种分布模型中,生物的相互作用很少被纳入,而联合物种分布模型(JSDMs)作为近年提出的一种新的可行方法,可以同时考虑环境因素和生物交互作用,因而成为分析生物群落结构和种间相互作用过程的有力工具.JSDMs以物种分布模型(SDMs)为基础,通常采用广义线性回归模型建立物种对环境变量的多变量响应,以随机效应的形式获取物种间的关联,同时结合隐变量模型(LVMs),并基于Laplace近似和马尔科夫蒙脱卡罗模拟的最大似然估计或贝叶斯方法来估算模型参数.本文对JSDMs的产生及理论基础进行归纳总结,重点介绍了不同类型JSDMs的特点及其在现代生态学中的应用,阐述了JSDMs的应用前景、使用过程中存在的问题及发展方向.随着对环境因素与多物种种间关系研究的深入,JSDMs将是今后物种分布模型研究的重点.
Species distribution models are commonly used in basic and applied ecological research to examine the factors driving the distribution and abundance of organisms. They are employed to quantify species' relationships with abiotic conditions,to predict species' response to land-use and climatic change,and to identify potential conservation areas. Biotic interactions have been rarely included in traditional species distribution models, wherein joint species distribution models( JSDMs) emerge as a feasible approach to simultaneously incorporate environmental factors and interspecific interactions,making it a powerful tool for analyzing the structure and assembly of biotic communities. Generally,the JSDMs are based on species distribution models( SDMs),with the abundance or occurrence of multiple species as response variables and environmental factors,species associations and species traits being incorporated in the modeling framework. These models commonly use generalized linear regression methods( GLM) to relate multivariate response to environmental variables,and capture species associations in the form of random effects. The limitation has been overcome by the introduction of latent variable models( LVMs). Typically,the model parameters are estimated using maximum likelihood estimation or Bayesian methods implemented by Laplace Approximation and Markov Chain Monte Carlo( MCMC) simulations,respectively. In this review,the generation and theoretical basis of JSDMs were summarized. The characteristics of different types of JSDMs and their applications in modern ecology were emphatically introduced. The problems and prospects of JSDMs were discussed. With the in-depth study of the relationship between environmental factors and multi-species interactions,JSDMs would be the focus of future studies of species distribution model.
Species distribution models are commonly used in basic and applied ecological research to examine the factors driving the distribution and abundance of organisms. They are employed to quantify species' relationships with abiotic conditions,to predict species' response to land-use and climatic change,and to identify potential conservation areas. Biotic interactions have been rarely included in traditional species distribution models, wherein joint species distribution models( JSDMs) emerge as a feasible approach to simultaneously incorporate environmental factors and interspecific interactions,making it a powerful tool for analyzing the structure and assembly of biotic communities. Generally,the JSDMs are based on species distribution models( SDMs),with the abundance or occurrence of multiple species as response variables and environmental factors,species associations and species traits being incorporated in the modeling framework. These models commonly use generalized linear regression methods( GLM) to relate multivariate response to environmental variables,and capture species associations in the form of random effects. The limitation has been overcome by the introduction of latent variable models( LVMs). Typically,the model parameters are estimated using maximum likelihood estimation or Bayesian methods implemented by Laplace Approximation and Markov Chain Monte Carlo( MCMC) simulations,respectively. In this review,the generation and theoretical basis of JSDMs were summarized. The characteristics of different types of JSDMs and their applications in modern ecology were emphatically introduced. The problems and prospects of JSDMs were discussed. With the in-depth study of the relationship between environmental factors and multi-species interactions,JSDMs would be the focus of future studies of species distribution model.
引文
[1]Hutchinson GE.Concluding remarks.Cold Spring Harbor Symposia on Quantitative Biology,1957,22:415-427
[2]Wisz MS,Pottier J,Kissling WD,et al.The role of biotic interactions in shaping distributions and realised assemblages of species:Implications for species distribution modelling.Biological Reviews,2013,88:15-30
[3]Hutchinson GE.The Niche:An Abstractly Inhabited Hyper Volume.The Ecological Theatre and the Evolutionary Play.New Haven,CT,USA:Yale University Press,1995
[4]Luo M(罗玫),Wang H(王昊),Lyu Z(吕植).Evaluating the performance of species distribution models Biomod2 and MaxEnt using the giant panda distribution data.Chinese Journal of Applied Ecology(应用生态学报),2017,28(12):4001-4006(in Chinese)
[5]Zhao Z-F(赵泽芳),Wei H-Y(卫海燕),Guo Y-L(郭彦龙),et al.Potential distribution of Panax ginseng and its predicted responses to climate change.Chinese Journal of Applied Ecology(应用生态学报),2016,27(11):3607-3615(in Chinese)
[6]Zhang H(张慧),Gao J-X(高吉喜),Ma M-X(马孟枭),et al.Influence of road on breeding habitat of Nipponia nippon based on MaxEnt model.Chinese Journal of Applied Ecology(应用生态学报),2017,28(4):1352-1359(in Chinese)
[7]Latimer AM,Banerjee S,Sang H,et al.Hierarchical models facilitate spatial analysis of large data sets:Acase study on invasive plant species in the northeastern United States.Ecology Letters,2009,12:144-154
[8]Ovaskainen O,Hottola J,Siitonen J.Modeling species co-occurrence by multivariate logistic regression generates new hypotheses on fungal interactions.Ecology,2010,91:2514-2521
[9]Pollock LJ,Tingley R,Morris WK,et al.Understanding co-occurrence by modelling species simultaneously with a Joint Species Distribution Model(JSDM).Methods in Ecology&Evolution,2014,5:397-406
[10]Legendre P,Legendre L.Numerical Ecology.3rd Ed.London:Elsevier Science B.V.Press,2012
[11]Clark JS,Gelfand AE,Woodall CW,et al.More than the sum of the parts:Forest climate response from joint species distribution models.Ecological Applications,2014,24:990-999
[12]Warton DI,Blanchet FG,O’Hara RB,et al.So many variables:Joint modeling in community ecology.Trends in Ecology&Evolution,2015,30:766-779
[13]Thorson JT,Barnett LAK.Comparing estimates of abundance trends and distribution shifts using single and multispecies models of fishes and biogenic habitat.ICESJournal of Marine Science,2017,74:1311-1321
[14]Ovaskainen O,Tikhonov G,Norberg A,et al.How to make more out of community data?A conceptual framework and its implementation as models and software.Ecology Letters,2017,20:561-576
[15]Dawson TP,Jackson ST,House JI,et al.Scenarios for global biodiversity in the 21st century.Science,2010,330:1496-1501
[16]Elith J,Leathwick JR.Species distribution models:Ecological explanation andprediction across space and time.Annual Review of Ecology,Evolution&Systematics,2009,40:677-697
[17]Guisan A,Zimmermann NE.Predictive habitat distribution models in ecology.Ecological Modelling,2000,135:147-186
[18]Yalcin S,Leroux SJ.Diversity and suitability of existing methods and metrics for quantifying species range shifts.Global Ecology&Biogeography,2017,26:609-624
[19]Singer A,Johst K,Banitz T,et al.Community dynamics under environmental change:How can next generation mechanistic models improve projections of species distributions?Ecological Modelling,2016,326:63-74
[20]Urban MC,Bocedi G,Hendry AP,et al.Improving the forecast for biodiversity under climate change.Science,2016,353:8466
[21]Bocedi G,Palmer SCF,Peer G,et al.Range shifter:Aplatform for modelling spatial eco-evolutionary dynamics and species’responses to environmental changes.99th ESA Annual Convention Sacramento,CA,USA,2014,5:388-396
[22]Cabral JS,Valente L,Hartig F.Mechanistic simulation models in macroecology and biogeography:State-of-art and prospects.Ecography,2017,40:267-280
[23]Evans MEK,Merow C,Record S,et al.Towards process-based range modeling of many species.Trends in Ecology&Evolution,2016,31:860-871
[24]Kissling WD,Dormann CF,Groeneveld J,et al.Towards novel approaches to modelling biotic interactions in multispecies assemblages at large spatial extents.Journal of Biogeography,2012,39:2163-2178
[25]Schurr FM,Pagel J,Cabral JS,et al.How to understand species’niches and range dynamics:A demographic research agenda for biogeography.Journal of Biogeography,2012,39:2146-2162
[26]Talluto MV,Boulangeat I,Ameztegui A,et al.Crossscale integration of knowledge for predicting species ranges:A meta modelling framework.Global Ecology&Biogeography,2016,25:238-249
[27]Zurell D.Integrating demography,dispersal and interspecific interactions into bird distribution models.Journal of Avian Biology,2017,48:1-12
[28]Zurell D,Thuiller W,Pagel J,et al.Bench marking novel approaches for modelling species range dynamics.Global Change Biology,2016,22:2651-2664
[29]Fordham DA,Ak9akaya HR,Araújo MB,et al.Tools for integrating range change,extinction risk and climate change information into conservation management.Ecography,2013,36:956-964
[30]Kissling WD,Field R,Korntheuer H,et al.Woody plants and the prediction of climate-change impacts on bird diversity.Philosophical Transactions Royal Society Biological Sciences,2010,365:2035-2045
[31]Meier ES,Kienast F,Pearman PB,et al.Biotic and abiotic variables show little redundancy in explaining tree species distributions.Ecography,2010,33:1038-1048
[32]Schweiger O,Heikkinen RK,Harpke A,et al.Increasing range mismatching of interacting species under global change is related to their ecological characteristics.Global Ecology&Biogeography,2012,21:88-99
[33]Swab RM,Regan HM,Matthies D,et al.The role of demography,intra-species variation,and species distribution models in species’projections under climate change.Ecography,2015,38:221-230
[34]Buse J,Griebeler EM.Incorporating classified dispersal assumptions in predictive distribution models:A case study with grasshoppers and bush-crickets.Ecological Modelling,2011,222:2130-2141
[35]Chapman DS,Makra L,Albertini R,et al.Modelling the introduction and spread of non-native species:International trade and climate change drive rag weed invasion.Global Change Biology,2016,22:3067-3079
[36]De Cceres M,Brotons L.Calibration of hybrid species distribution models:The value of general-purpose vs.targeted monitoring data.Diversity and Distributions,2012,18:977-989
[37]Alexander S,Oliver S,Ingolf K,et al.Constructing a hybrid species distribution model from standard largescale distribution data.Ecological Modelling,2018,373:39-52
[38]Pugnaire FI.Positive Plant Interactions and Community Dynamics.Boca Raton,FL,USA:CRC Press,2010
[39]Harris DJ.Generating realistic assemblages with a joint species distribution model.Methods in Ecology and Evolution,2015,6:465-473
[40]Warton DI,Blanchet FG,O’hara RB,et al.So many variables:Joint modeling in community ecology.Trends in Ecology and Evolution,2015,30:766-779
[41]Legendre P,Cceres M.Beta diversity as the variance of community data:Dissimilarity coefficients and partitioning.Ecology Letters,2013,16:951-963
[42]Ovaskainen O,Soininen J.Making more out of sparse data:Hierarchical modeling of species communities.Ecology,2011,92:289-295
[43]Ovaskainen O,Tikhonov G,Dunson D,et al.How are species interactions structured in species-rich communities?A new method for analysing time-series data.Proceedings Biological Sciences,2017,284:20170768
[44]Wilby RL,Charles SP,Zorita E,et al.Guidelines for Use of Climate Scenarios Developed from Statistical Downscaling Methods.IPCC Task Group on Data and Scenario Support for Impact and Climate Analysis(TGI-CA)[EB/OL].(2004-08-01)[2018-09-19].http://www.sysecol2.ethz.ch/AR4_Ch04/AR4-Ch4_Grey_LitSOD/Wi198.pdf
[45]Karyn T,John WW.Globally downscaled climate projections for assessing the conservation impacts of climate change.Ecological Applications,2010,20:554-565
[46]Fick S,Hijmans R.World Clim2:New 1-km spatial resolution climate surfaces for global land areas.International Journal of Climatology,2017,37:4302-4315
[47]Title PO,Bemmels JB.ENVIREM:An expanded set of bioclimatic and topographic variables increases flexibility and improves performance of ecological niche modeling.Ecography,2017,doi:10.1111/ecog.02880
[48]Kriticos DJ,Webber BL,Leriche A,et al.Cli Mond:Global high resolution historical and future scenario climate surfaces for bioclimatic modelling.Methods in Ecology and Evolution,2012,3:53-64
[49]Beckmann M,Vclavík T,Manceur AM,et al.gl UV:A global UV-B radiation dataset for macroecological studies.Methods in Ecology and Evolution,2014,5:372-383
[50]Hengl T,Mendes de Jesus J,Heuvelink GBM,et al.Soil Grids 250 m:Global gridded soil information based on machine learning.PLo S One,2017,12(2):e0169748
[51]Zhang Y,Ming P,Justin S,et al.A climate data record(CDR)for the global terrestrial water budget:1984-2010.Hydrology and Earth System Sciences,2018,22:241-263
[52]Nemani R,Hashimoto H,Votava P,et al.Monitoring and forecasting ecosystem dynamics using the terrestrial observation and prediction system(TOPS).Remote Sensing of Environment,2009,113:1497-1509
[53]Reuter HI,Nelson A,Jarvis A.An evaluation of void filling interpolation methods for SRTM data.International Journal of Geographic Information Science,2007,21:983-1008
[54]Pierce DW,Cayan DR,Thrasher BL.Statistical downscaling using localized constructed analogs(LOCA).Journal of Hydrometeorology,2014,15:2558-2585
[55]Fang J-Y(方精云),Wang X-P(王襄平),Shen Z-H(沈泽昊),et al.Methods and protocols for plant community inventory.Biodiversity Science(生物多样性),2009,17(6):533-548(in Chinese)
[56]Hui FKC.Boral:Bayesian ordination and regression analysis of multivariate abundance data in R.Methods in Ecology and Evolution,2016,7:744-750
[57]Hui FK,Warton DI,Ormerod JT,et al.Variational approximations for generalized linear latent variable models.Journal of Computational and Graphical Statistics,2017,26:35-43
[58]Zurell D,Pollock LJ,Thuiller W.Do joint species distribution models reliably detect interspecific interactions from co-occurrence data in homogenous environments?Ecography,2018,doi:10.1111/ECOG.03315
[59]Golding N,Harris DJ.Bayes Comm:Bayesian Community Ecology Analysis.R Package Version 0.1-2[EB/OL].(2015-07-23)[2018-09-19].https://cran.r-project.org/web/packages/Bayes Comm/Bayes Comm.pdf
[60]Clark JS,Nemergut D,Seyednasrollah B,et al.Generalized joint attribute modeling for biodiversity analysis:Median-zero,multivariate,multifarious data.Ecological Monographs,2017,87:34-56
[61]Thorson JT,Ianelli JN,Larsen EA,et al.Joint dynamic species distribution models:A tool for community ordination and spatio-temporal monitoring.Global Ecology&Biogeography,2016,25:1144-1158
[62]Zhang CL,Chen Y,Xu BD,et al.Comparing the prediction of joint species distribution models with respect to characteristics of sampling data.Ecography,2018,doi:10.1111/ecog.03571
[63]Tikhonov G,Abrego N,Dunson D,et al.Using joint species distribution models for evaluating how species-tospecies associations depend on the environmental context.Methods in Ecology&Evolution,2017,8:443-452
[64]Plummer M.JAGS:A Program for Analysis of Bayesian Graphical Models using Gibbs Sampling.[EB/OL](2003-03-20)[2018-09-19].https://www.r-project.org/conferences/DSC-2003/Drafts/Plummer.pdf
[65]Ovaskainen O,Roy DB,Fox R,et al.Uncovering hidden spatial structure in species communities with spatially explicit joint species distribution models.Methods in Ecology&Evolution,2016,7:428-436
[66]Tang Y,Salakhutdinov R.Learning stochastic feedforward neural networks.Advances in Neural Information Processing Systems,2013,2:530-538
[67]Hui FKC,Warton DI,Foster SD,et al.To mix or not to mix:Comparing the predictive performance of mixture models vs.separate species distribution models.Ecology,2013,94:1913-1919
[68]Guisan A,Thuiller W.Predicting species distribution:Offering more than simple habitat models.Ecology Letters,2005,8:993-1009
[69]Araújo MB,Guisan A.Five(or so)challenges for species distribution modelling.Journal of Biogeography,2006,33:1677-1688
[70]Van Buskirk J.Local and landscape influence on amphibian occurrence and abundance.Ecology,2005,86:1936-1947
[71]Blanchet FG,Legendre P,Bergeron JAC,et al.Consensus RDA across dissimilarity coefficients for canonical ordination of community composition data.Ecological Monographs,2014,84:491-511
[72]Mouquet N,Lagadeuc Y,Devictor V,et al.Predictive ecology in a changing world.Journal of Applied Ecology,2015,52:1293-1310
[73]Chase JM,Myers JA.Disentangling the importance of ecological niches from stochastic processes across scales.Philosophical Transactions of the Royal Society of London,2011,366:2351-2363
[2]Wisz MS,Pottier J,Kissling WD,et al.The role of biotic interactions in shaping distributions and realised assemblages of species:Implications for species distribution modelling.Biological Reviews,2013,88:15-30
[3]Hutchinson GE.The Niche:An Abstractly Inhabited Hyper Volume.The Ecological Theatre and the Evolutionary Play.New Haven,CT,USA:Yale University Press,1995
[4]Luo M(罗玫),Wang H(王昊),Lyu Z(吕植).Evaluating the performance of species distribution models Biomod2 and MaxEnt using the giant panda distribution data.Chinese Journal of Applied Ecology(应用生态学报),2017,28(12):4001-4006(in Chinese)
[5]Zhao Z-F(赵泽芳),Wei H-Y(卫海燕),Guo Y-L(郭彦龙),et al.Potential distribution of Panax ginseng and its predicted responses to climate change.Chinese Journal of Applied Ecology(应用生态学报),2016,27(11):3607-3615(in Chinese)
[6]Zhang H(张慧),Gao J-X(高吉喜),Ma M-X(马孟枭),et al.Influence of road on breeding habitat of Nipponia nippon based on MaxEnt model.Chinese Journal of Applied Ecology(应用生态学报),2017,28(4):1352-1359(in Chinese)
[7]Latimer AM,Banerjee S,Sang H,et al.Hierarchical models facilitate spatial analysis of large data sets:Acase study on invasive plant species in the northeastern United States.Ecology Letters,2009,12:144-154
[8]Ovaskainen O,Hottola J,Siitonen J.Modeling species co-occurrence by multivariate logistic regression generates new hypotheses on fungal interactions.Ecology,2010,91:2514-2521
[9]Pollock LJ,Tingley R,Morris WK,et al.Understanding co-occurrence by modelling species simultaneously with a Joint Species Distribution Model(JSDM).Methods in Ecology&Evolution,2014,5:397-406
[10]Legendre P,Legendre L.Numerical Ecology.3rd Ed.London:Elsevier Science B.V.Press,2012
[11]Clark JS,Gelfand AE,Woodall CW,et al.More than the sum of the parts:Forest climate response from joint species distribution models.Ecological Applications,2014,24:990-999
[12]Warton DI,Blanchet FG,O’Hara RB,et al.So many variables:Joint modeling in community ecology.Trends in Ecology&Evolution,2015,30:766-779
[13]Thorson JT,Barnett LAK.Comparing estimates of abundance trends and distribution shifts using single and multispecies models of fishes and biogenic habitat.ICESJournal of Marine Science,2017,74:1311-1321
[14]Ovaskainen O,Tikhonov G,Norberg A,et al.How to make more out of community data?A conceptual framework and its implementation as models and software.Ecology Letters,2017,20:561-576
[15]Dawson TP,Jackson ST,House JI,et al.Scenarios for global biodiversity in the 21st century.Science,2010,330:1496-1501
[16]Elith J,Leathwick JR.Species distribution models:Ecological explanation andprediction across space and time.Annual Review of Ecology,Evolution&Systematics,2009,40:677-697
[17]Guisan A,Zimmermann NE.Predictive habitat distribution models in ecology.Ecological Modelling,2000,135:147-186
[18]Yalcin S,Leroux SJ.Diversity and suitability of existing methods and metrics for quantifying species range shifts.Global Ecology&Biogeography,2017,26:609-624
[19]Singer A,Johst K,Banitz T,et al.Community dynamics under environmental change:How can next generation mechanistic models improve projections of species distributions?Ecological Modelling,2016,326:63-74
[20]Urban MC,Bocedi G,Hendry AP,et al.Improving the forecast for biodiversity under climate change.Science,2016,353:8466
[21]Bocedi G,Palmer SCF,Peer G,et al.Range shifter:Aplatform for modelling spatial eco-evolutionary dynamics and species’responses to environmental changes.99th ESA Annual Convention Sacramento,CA,USA,2014,5:388-396
[22]Cabral JS,Valente L,Hartig F.Mechanistic simulation models in macroecology and biogeography:State-of-art and prospects.Ecography,2017,40:267-280
[23]Evans MEK,Merow C,Record S,et al.Towards process-based range modeling of many species.Trends in Ecology&Evolution,2016,31:860-871
[24]Kissling WD,Dormann CF,Groeneveld J,et al.Towards novel approaches to modelling biotic interactions in multispecies assemblages at large spatial extents.Journal of Biogeography,2012,39:2163-2178
[25]Schurr FM,Pagel J,Cabral JS,et al.How to understand species’niches and range dynamics:A demographic research agenda for biogeography.Journal of Biogeography,2012,39:2146-2162
[26]Talluto MV,Boulangeat I,Ameztegui A,et al.Crossscale integration of knowledge for predicting species ranges:A meta modelling framework.Global Ecology&Biogeography,2016,25:238-249
[27]Zurell D.Integrating demography,dispersal and interspecific interactions into bird distribution models.Journal of Avian Biology,2017,48:1-12
[28]Zurell D,Thuiller W,Pagel J,et al.Bench marking novel approaches for modelling species range dynamics.Global Change Biology,2016,22:2651-2664
[29]Fordham DA,Ak9akaya HR,Araújo MB,et al.Tools for integrating range change,extinction risk and climate change information into conservation management.Ecography,2013,36:956-964
[30]Kissling WD,Field R,Korntheuer H,et al.Woody plants and the prediction of climate-change impacts on bird diversity.Philosophical Transactions Royal Society Biological Sciences,2010,365:2035-2045
[31]Meier ES,Kienast F,Pearman PB,et al.Biotic and abiotic variables show little redundancy in explaining tree species distributions.Ecography,2010,33:1038-1048
[32]Schweiger O,Heikkinen RK,Harpke A,et al.Increasing range mismatching of interacting species under global change is related to their ecological characteristics.Global Ecology&Biogeography,2012,21:88-99
[33]Swab RM,Regan HM,Matthies D,et al.The role of demography,intra-species variation,and species distribution models in species’projections under climate change.Ecography,2015,38:221-230
[34]Buse J,Griebeler EM.Incorporating classified dispersal assumptions in predictive distribution models:A case study with grasshoppers and bush-crickets.Ecological Modelling,2011,222:2130-2141
[35]Chapman DS,Makra L,Albertini R,et al.Modelling the introduction and spread of non-native species:International trade and climate change drive rag weed invasion.Global Change Biology,2016,22:3067-3079
[36]De Cceres M,Brotons L.Calibration of hybrid species distribution models:The value of general-purpose vs.targeted monitoring data.Diversity and Distributions,2012,18:977-989
[37]Alexander S,Oliver S,Ingolf K,et al.Constructing a hybrid species distribution model from standard largescale distribution data.Ecological Modelling,2018,373:39-52
[38]Pugnaire FI.Positive Plant Interactions and Community Dynamics.Boca Raton,FL,USA:CRC Press,2010
[39]Harris DJ.Generating realistic assemblages with a joint species distribution model.Methods in Ecology and Evolution,2015,6:465-473
[40]Warton DI,Blanchet FG,O’hara RB,et al.So many variables:Joint modeling in community ecology.Trends in Ecology and Evolution,2015,30:766-779
[41]Legendre P,Cceres M.Beta diversity as the variance of community data:Dissimilarity coefficients and partitioning.Ecology Letters,2013,16:951-963
[42]Ovaskainen O,Soininen J.Making more out of sparse data:Hierarchical modeling of species communities.Ecology,2011,92:289-295
[43]Ovaskainen O,Tikhonov G,Dunson D,et al.How are species interactions structured in species-rich communities?A new method for analysing time-series data.Proceedings Biological Sciences,2017,284:20170768
[44]Wilby RL,Charles SP,Zorita E,et al.Guidelines for Use of Climate Scenarios Developed from Statistical Downscaling Methods.IPCC Task Group on Data and Scenario Support for Impact and Climate Analysis(TGI-CA)[EB/OL].(2004-08-01)[2018-09-19].http://www.sysecol2.ethz.ch/AR4_Ch04/AR4-Ch4_Grey_LitSOD/Wi198.pdf
[45]Karyn T,John WW.Globally downscaled climate projections for assessing the conservation impacts of climate change.Ecological Applications,2010,20:554-565
[46]Fick S,Hijmans R.World Clim2:New 1-km spatial resolution climate surfaces for global land areas.International Journal of Climatology,2017,37:4302-4315
[47]Title PO,Bemmels JB.ENVIREM:An expanded set of bioclimatic and topographic variables increases flexibility and improves performance of ecological niche modeling.Ecography,2017,doi:10.1111/ecog.02880
[48]Kriticos DJ,Webber BL,Leriche A,et al.Cli Mond:Global high resolution historical and future scenario climate surfaces for bioclimatic modelling.Methods in Ecology and Evolution,2012,3:53-64
[49]Beckmann M,Vclavík T,Manceur AM,et al.gl UV:A global UV-B radiation dataset for macroecological studies.Methods in Ecology and Evolution,2014,5:372-383
[50]Hengl T,Mendes de Jesus J,Heuvelink GBM,et al.Soil Grids 250 m:Global gridded soil information based on machine learning.PLo S One,2017,12(2):e0169748
[51]Zhang Y,Ming P,Justin S,et al.A climate data record(CDR)for the global terrestrial water budget:1984-2010.Hydrology and Earth System Sciences,2018,22:241-263
[52]Nemani R,Hashimoto H,Votava P,et al.Monitoring and forecasting ecosystem dynamics using the terrestrial observation and prediction system(TOPS).Remote Sensing of Environment,2009,113:1497-1509
[53]Reuter HI,Nelson A,Jarvis A.An evaluation of void filling interpolation methods for SRTM data.International Journal of Geographic Information Science,2007,21:983-1008
[54]Pierce DW,Cayan DR,Thrasher BL.Statistical downscaling using localized constructed analogs(LOCA).Journal of Hydrometeorology,2014,15:2558-2585
[55]Fang J-Y(方精云),Wang X-P(王襄平),Shen Z-H(沈泽昊),et al.Methods and protocols for plant community inventory.Biodiversity Science(生物多样性),2009,17(6):533-548(in Chinese)
[56]Hui FKC.Boral:Bayesian ordination and regression analysis of multivariate abundance data in R.Methods in Ecology and Evolution,2016,7:744-750
[57]Hui FK,Warton DI,Ormerod JT,et al.Variational approximations for generalized linear latent variable models.Journal of Computational and Graphical Statistics,2017,26:35-43
[58]Zurell D,Pollock LJ,Thuiller W.Do joint species distribution models reliably detect interspecific interactions from co-occurrence data in homogenous environments?Ecography,2018,doi:10.1111/ECOG.03315
[59]Golding N,Harris DJ.Bayes Comm:Bayesian Community Ecology Analysis.R Package Version 0.1-2[EB/OL].(2015-07-23)[2018-09-19].https://cran.r-project.org/web/packages/Bayes Comm/Bayes Comm.pdf
[60]Clark JS,Nemergut D,Seyednasrollah B,et al.Generalized joint attribute modeling for biodiversity analysis:Median-zero,multivariate,multifarious data.Ecological Monographs,2017,87:34-56
[61]Thorson JT,Ianelli JN,Larsen EA,et al.Joint dynamic species distribution models:A tool for community ordination and spatio-temporal monitoring.Global Ecology&Biogeography,2016,25:1144-1158
[62]Zhang CL,Chen Y,Xu BD,et al.Comparing the prediction of joint species distribution models with respect to characteristics of sampling data.Ecography,2018,doi:10.1111/ecog.03571
[63]Tikhonov G,Abrego N,Dunson D,et al.Using joint species distribution models for evaluating how species-tospecies associations depend on the environmental context.Methods in Ecology&Evolution,2017,8:443-452
[64]Plummer M.JAGS:A Program for Analysis of Bayesian Graphical Models using Gibbs Sampling.[EB/OL](2003-03-20)[2018-09-19].https://www.r-project.org/conferences/DSC-2003/Drafts/Plummer.pdf
[65]Ovaskainen O,Roy DB,Fox R,et al.Uncovering hidden spatial structure in species communities with spatially explicit joint species distribution models.Methods in Ecology&Evolution,2016,7:428-436
[66]Tang Y,Salakhutdinov R.Learning stochastic feedforward neural networks.Advances in Neural Information Processing Systems,2013,2:530-538
[67]Hui FKC,Warton DI,Foster SD,et al.To mix or not to mix:Comparing the predictive performance of mixture models vs.separate species distribution models.Ecology,2013,94:1913-1919
[68]Guisan A,Thuiller W.Predicting species distribution:Offering more than simple habitat models.Ecology Letters,2005,8:993-1009
[69]Araújo MB,Guisan A.Five(or so)challenges for species distribution modelling.Journal of Biogeography,2006,33:1677-1688
[70]Van Buskirk J.Local and landscape influence on amphibian occurrence and abundance.Ecology,2005,86:1936-1947
[71]Blanchet FG,Legendre P,Bergeron JAC,et al.Consensus RDA across dissimilarity coefficients for canonical ordination of community composition data.Ecological Monographs,2014,84:491-511
[72]Mouquet N,Lagadeuc Y,Devictor V,et al.Predictive ecology in a changing world.Journal of Applied Ecology,2015,52:1293-1310
[73]Chase JM,Myers JA.Disentangling the importance of ecological niches from stochastic processes across scales.Philosophical Transactions of the Royal Society of London,2011,366:2351-2363