Towards novel approaches to modelling biotic interactions in multispecies assemblages at large spatial extents
Aim: Biotic interactions — within guilds or across trophic levels — have widely been ignored in species distribution models (SDMs). This synthesis outlines the development of 'species interaction distribution models' (SIDMs), which aim to incorporate multispecies interactions at large spat...
Saved in:
Published in | Journal of biogeography Vol. 39; no. 12; pp. 2163 - 2178 |
---|---|
Main Authors | , , , , , , , , , , , , , |
Format | Journal Article |
Language | English |
Published |
Oxford, UK
Blackwell Publishing Ltd
01.12.2012
Blackwell Publishing Wiley Subscription Services, Inc |
Subjects | |
Online Access | Get full text |
Cover
Loading…
Abstract | Aim: Biotic interactions — within guilds or across trophic levels — have widely been ignored in species distribution models (SDMs). This synthesis outlines the development of 'species interaction distribution models' (SIDMs), which aim to incorporate multispecies interactions at large spatial extents using interaction matrices. Location: Local to global. Methods: We review recent approaches for extending classical SDMs to incorporate biotic interactions, and identify some methodological and conceptual limitations. To illustrate possible directions for conceptual advancement we explore three principal ways of modelling multispecies interactions using interaction matrices: simple qualitative linkages between species, quantitative interaction coefficients reflecting interaction strengths, and interactions mediated by interaction currencies. We explain methodological advancements for static interaction data and multispecies time series, and outline methods to reduce complexity when modelling multispecies interactions. Results: Classical SDMs ignore biotic interactions and recent SDM extensions only include the unidirectional influence of one or a few species. However, novel methods using error matrices in multivariate regression models allow interactions between multiple species to be modelled explicitly with spatial co-occurrence data. If time series are available, multivariate versions of population dynamic models can be applied that account for the effects and relative importance of species interactions and environmental drivers. These methods need to be extended by incorporating the non-stationarity in interaction coefficients across space and time, and are challenged by the limited empirical knowledge on spatio-temporal variation in the existence and strength of species interactions. Model complexity may be reduced by: (1) using prior ecological knowledge to set a subset of interaction coefficients to zero, (2) modelling guilds and functional groups rather than individual species, and (3) modelling interaction currencies and species' effect and response traits. Main conclusions: There is great potential for developing novel approaches that incorporate multispecies interactions into the projection of species distributions and community structure at large spatial extents. Progress can be made by: (1) developing statistical models with interaction matrices for multispecies co-occurrence datasets across large-scale environmental gradients, (2) testing the potential and limitations of methods for complexity reduction, and (3) sampling and monitoring comprehensive spatio-temporal data on biotic interactions in multispecies communities. |
---|---|
AbstractList | Aim Biotic interactions – within guilds or across trophic levels – have widely been ignored in species distribution models (SDMs). This synthesis outlines the development of ‘species interaction distribution models’ (SIDMs), which aim to incorporate multispecies interactions at large spatial extents using interaction matrices.
Location Local to global.
Methods We review recent approaches for extending classical SDMs to incorporate biotic interactions, and identify some methodological and conceptual limitations. To illustrate possible directions for conceptual advancement we explore three principal ways of modelling multispecies interactions using interaction matrices: simple qualitative linkages between species, quantitative interaction coefficients reflecting interaction strengths, and interactions mediated by interaction currencies. We explain methodological advancements for static interaction data and multispecies time series, and outline methods to reduce complexity when modelling multispecies interactions.
Results Classical SDMs ignore biotic interactions and recent SDM extensions only include the unidirectional influence of one or a few species. However, novel methods using error matrices in multivariate regression models allow interactions between multiple species to be modelled explicitly with spatial co‐occurrence data. If time series are available, multivariate versions of population dynamic models can be applied that account for the effects and relative importance of species interactions and environmental drivers. These methods need to be extended by incorporating the non‐stationarity in interaction coefficients across space and time, and are challenged by the limited empirical knowledge on spatio‐temporal variation in the existence and strength of species interactions. Model complexity may be reduced by: (1) using prior ecological knowledge to set a subset of interaction coefficients to zero, (2) modelling guilds and functional groups rather than individual species, and (3) modelling interaction currencies and species’ effect and response traits.
Main conclusions There is great potential for developing novel approaches that incorporate multispecies interactions into the projection of species distributions and community structure at large spatial extents. Progress can be made by: (1) developing statistical models with interaction matrices for multispecies co‐occurrence datasets across large‐scale environmental gradients, (2) testing the potential and limitations of methods for complexity reduction, and (3) sampling and monitoring comprehensive spatio‐temporal data on biotic interactions in multispecies communities. Abstract Aim Biotic interactions – within guilds or across trophic levels – have widely been ignored in species distribution models (SDMs). This synthesis outlines the development of ‘species interaction distribution models’ (SIDMs), which aim to incorporate multispecies interactions at large spatial extents using interaction matrices. Location Local to global. Methods We review recent approaches for extending classical SDMs to incorporate biotic interactions, and identify some methodological and conceptual limitations. To illustrate possible directions for conceptual advancement we explore three principal ways of modelling multispecies interactions using interaction matrices: simple qualitative linkages between species, quantitative interaction coefficients reflecting interaction strengths, and interactions mediated by interaction currencies. We explain methodological advancements for static interaction data and multispecies time series, and outline methods to reduce complexity when modelling multispecies interactions. Results Classical SDMs ignore biotic interactions and recent SDM extensions only include the unidirectional influence of one or a few species. However, novel methods using error matrices in multivariate regression models allow interactions between multiple species to be modelled explicitly with spatial co‐occurrence data. If time series are available, multivariate versions of population dynamic models can be applied that account for the effects and relative importance of species interactions and environmental drivers. These methods need to be extended by incorporating the non‐stationarity in interaction coefficients across space and time, and are challenged by the limited empirical knowledge on spatio‐temporal variation in the existence and strength of species interactions. Model complexity may be reduced by: (1) using prior ecological knowledge to set a subset of interaction coefficients to zero, (2) modelling guilds and functional groups rather than individual species, and (3) modelling interaction currencies and species’ effect and response traits. Main conclusions There is great potential for developing novel approaches that incorporate multispecies interactions into the projection of species distributions and community structure at large spatial extents. Progress can be made by: (1) developing statistical models with interaction matrices for multispecies co‐occurrence datasets across large‐scale environmental gradients, (2) testing the potential and limitations of methods for complexity reduction, and (3) sampling and monitoring comprehensive spatio‐temporal data on biotic interactions in multispecies communities. Aim Biotic interactions - within guilds or across trophic levels - have widely been ignored in species distribution models (SDMs). This synthesis outlines the development of 'species interaction distribution models' (SIDMs), which aim to incorporate multispecies interactions at large spatial extents using interaction matrices. Location Local to global. Methods We review recent approaches for extending classical SDMs to incorporate biotic interactions, and identify some methodological and conceptual limitations. To illustrate possible directions for conceptual advancement we explore three principal ways of modelling multispecies interactions using interaction matrices: simple qualitative linkages between species, quantitative interaction coefficients reflecting interaction strengths, and interactions mediated by interaction currencies. We explain methodological advancements for static interaction data and multispecies time series, and outline methods to reduce complexity when modelling multispecies interactions. Results Classical SDMs ignore biotic interactions and recent SDM extensions only include the unidirectional influence of one or a few species. However, novel methods using error matrices in multivariate regression models allow interactions between multiple species to be modelled explicitly with spatial co-occurrence data. If time series are available, multivariate versions of population dynamic models can be applied that account for the effects and relative importance of species interactions and environmental drivers. These methods need to be extended by incorporating the non-stationarity in interaction coefficients across space and time, and are challenged by the limited empirical knowledge on spatio-temporal variation in the existence and strength of species interactions. Model complexity may be reduced by: (1) using prior ecological knowledge to set a subset of interaction coefficients to zero, (2) modelling guilds and functional groups rather than individual species, and (3) modelling interaction currencies and species' effect and response traits. Main conclusions There is great potential for developing novel approaches that incorporate multispecies interactions into the projection of species distributions and community structure at large spatial extents. Progress can be made by: (1) developing statistical models with interaction matrices for multispecies co-occurrence datasets across large-scale environmental gradients, (2) testing the potential and limitations of methods for complexity reduction, and (3) sampling and monitoring comprehensive spatio-temporal data on biotic interactions in multispecies communities. Aim Biotic interactions - within guilds or across trophic levels - have widely been ignored in species distribution models (SDMs). This synthesis outlines the development of 'species interaction distribution models' (SIDMs), which aim to incorporate multispecies interactions at large spatial extents using interaction matrices. Location Local to global. Methods We review recent approaches for extending classical SDMs to incorporate biotic interactions, and identify some methodological and conceptual limitations. To illustrate possible directions for conceptual advancement we explore three principal ways of modelling multispecies interactions using interaction matrices: simple qualitative linkages between species, quantitative interaction coefficients reflecting interaction strengths, and interactions mediated by interaction currencies. We explain methodological advancements for static interaction data and multispecies time series, and outline methods to reduce complexity when modelling multispecies interactions. Results Classical SDMs ignore biotic interactions and recent SDM extensions only include the unidirectional influence of one or a few species. However, novel methods using error matrices in multivariate regression models allow interactions between multiple species to be modelled explicitly with spatial co-occurrence data. If time series are available, multivariate versions of population dynamic models can be applied that account for the effects and relative importance of species interactions and environmental drivers. These methods need to be extended by incorporating the non-stationarity in interaction coefficients across space and time, and are challenged by the limited empirical knowledge on spatio-temporal variation in the existence and strength of species interactions. Model complexity may be reduced by: (1) using prior ecological knowledge to set a subset of interaction coefficients to zero, (2) modelling guilds and functional groups rather than individual species, and (3) modelling interaction currencies and species' effect and response traits. Main conclusions There is great potential for developing novel approaches that incorporate multispecies interactions into the projection of species distributions and community structure at large spatial extents. Progress can be made by: (1) developing statistical models with interaction matrices for multispecies co-occurrence datasets across large-scale environmental gradients, (2) testing the potential and limitations of methods for complexity reduction, and (3) sampling and monitoring comprehensive spatio-temporal data on biotic interactions in multispecies communities. [PUBLICATION ABSTRACT] |
Author | McInerny, Greg J. Montoya, José M. Kühn, Ingolf Hickler, Thomas Singer, Alexander Zimmermann, Niklaus E. Groeneveld, Jürgen Dormann, Carsten F. Schiffers, Katja Svenning, Jens-Christian Römermann, Christine O'Hara, Robert B. Kissling, W. D. Schurr, Frank M. |
Author_xml | – sequence: 1 givenname: W. D. surname: Kissling fullname: Kissling, W. D. email: danielkissling@web.de organization: Ecoinformatics & Biodiversity Group, Department of Bioscience, Aarhus University, DK-8000 Aarhus C, Denmark – sequence: 2 givenname: Carsten F. surname: Dormann fullname: Dormann, Carsten F. organization: Biometry and Environmental System Analysis, Faculty of Forest and Environmental Sciences, University of Freiburg, 79106 Freiburg, Germany – sequence: 3 givenname: Jürgen surname: Groeneveld fullname: Groeneveld, Jürgen organization: Helmholtz Centre for Environmental Research - UFZ, Department of Ecological Modelling, 04318 Leipzig, Germany – sequence: 4 givenname: Thomas surname: Hickler fullname: Hickler, Thomas organization: Biodiversity and Climate Research Centre (BiK-F), 60325 Frankfurt am Main, Germany – sequence: 5 givenname: Ingolf surname: Kühn fullname: Kühn, Ingolf organization: Helmholtz Centre for Environmental Research - UFZ, Department of Community Ecology, 06120 Halle, Germany – sequence: 6 givenname: Greg J. surname: McInerny fullname: McInerny, Greg J. organization: Computational Ecology and Environmental Science Group, Computational Science Laboratory, Microsoft Research, Cambridge CB3 0FB, UK – sequence: 7 givenname: José M. surname: Montoya fullname: Montoya, José M. organization: Instituto de Ciencias del Mar, Consejo Superior de Investigaciones Científicas, E-08003 Barcelona, Spain – sequence: 8 givenname: Christine surname: Römermann fullname: Römermann, Christine organization: Institute for Physical Geography, Goethe University Frankfurt, 60438 Frankfurt am Main, Germany – sequence: 9 givenname: Katja surname: Schiffers fullname: Schiffers, Katja organization: Laboratoire d'Ecologie Alpine, UMR-CNRS 5553, Université J. Fourier, 38041 Grenoble Cedex 9, France – sequence: 10 givenname: Frank M. surname: Schurr fullname: Schurr, Frank M. organization: Institute for Physical Geography, Goethe University Frankfurt, 60438 Frankfurt am Main, Germany – sequence: 11 givenname: Alexander surname: Singer fullname: Singer, Alexander organization: Helmholtz Centre for Environmental Research - UFZ, Department of Ecological Modelling, 04318 Leipzig, Germany – sequence: 12 givenname: Jens-Christian surname: Svenning fullname: Svenning, Jens-Christian organization: Ecoinformatics & Biodiversity Group, Department of Bioscience, Aarhus University, DK-8000 Aarhus C, Denmark – sequence: 13 givenname: Niklaus E. surname: Zimmermann fullname: Zimmermann, Niklaus E. organization: Landscape Dynamics, Swiss Federal Research Institute WSL, CH-8903 Birmensdorf, Switzerland – sequence: 14 givenname: Robert B. surname: O'Hara fullname: O'Hara, Robert B. organization: Biodiversity and Climate Research Centre (BiK-F), 60325 Frankfurt am Main, Germany |
BookMark | eNqNkctu1DAUhi3USkxbHgHJEhs2Cb7EcbJgAVUphRY2g1haTnIyOCR2sD10-vZ1GjSLrvDG5_j_P-tcztCJdRYQwpTkNJ13Q055KTJW1nXOCKU5YWXJ88MLtDkKJ2hDOBEZYZK8RGchDISQWvBig-zW3WvfBWzdXxixnmfvdPsLAo4OT66DcTR2hxvjommxsRG8bqNxNqQET_sxmjBDaxKgQ4CpGfVuiSMetd8BDrOORo8YDhFsDBfotNdjgFf_7nP049PV9vJzdvv9-ubyw23WFrzgWUkZLRpJBa172WpNGs0BGtlVnSjrDqAEDrpgvOI9F0tSdz1UdQO07NMjP0dv139TO3_2EKKaTGhTM9qC2wdFmZCykJKIZH3zzDq4vbepOkWppEUleE2Sq1pdrXcheOjV7M2k_YOiRC2LUINa5q2WeatlEeppEeqQ0Pcrem9GePhvTn35eLNEiX-98kOIzh95xrkoBFtKy1bdhAiHo679b1VKLoX6-e1aEXm3FVRy9ZU_AqNEqt4 |
CODEN | JBIODN |
CitedBy_id | crossref_primary_10_1111_ddi_13498 crossref_primary_10_1111_gcb_12469 crossref_primary_10_1371_journal_pbio_1002323 crossref_primary_10_1111_gcb_12466 crossref_primary_10_1111_gcb_12467 crossref_primary_10_1890_12_1482_1 crossref_primary_10_1002_ecy_1944 crossref_primary_10_1111_acv_12894 crossref_primary_10_1111_jbi_12009 crossref_primary_10_1007_s00035_013_0117_4 crossref_primary_10_1111_ecog_04006 crossref_primary_10_3389_fevo_2021_700962 crossref_primary_10_1111_1365_2656_12578 crossref_primary_10_1098_rspb_2013_2495 crossref_primary_10_1111_ecog_01892 crossref_primary_10_1098_rspb_2015_0516 crossref_primary_10_1111_jbi_12258 crossref_primary_10_1111_jbi_13463 crossref_primary_10_1111_geb_12464 crossref_primary_10_1016_j_jenvman_2019_109479 crossref_primary_10_1038_s41598_021_83457_w crossref_primary_10_1002_ecs2_1349 crossref_primary_10_3897_BDJ_9_e61866 crossref_primary_10_1007_s13253_022_00520_3 crossref_primary_10_1002_ece3_784 crossref_primary_10_1111_geb_12216 crossref_primary_10_1007_s12080_015_0281_9 crossref_primary_10_1002_ecy_2920 crossref_primary_10_1111_j_1600_0706_2013_00706_x crossref_primary_10_1073_pnas_1918363117 crossref_primary_10_1016_j_gecco_2023_e02441 crossref_primary_10_1111_jbi_12485 crossref_primary_10_1111_jbi_13699 crossref_primary_10_1111_2041_210X_12359 crossref_primary_10_1002_rse2_7 crossref_primary_10_1111_2041_210X_12357 crossref_primary_10_1016_j_funeco_2013_10_003 crossref_primary_10_1002_ecm_1545 crossref_primary_10_1371_journal_pone_0280922 crossref_primary_10_1098_rspb_2017_1841 crossref_primary_10_1111_ele_12401 crossref_primary_10_1371_journal_pone_0199292 crossref_primary_10_1038_srep35303 crossref_primary_10_1093_conphys_cov056 crossref_primary_10_1002_ece3_897 crossref_primary_10_1111_ecog_01557 crossref_primary_10_1111_1365_2745_12713 crossref_primary_10_1111_jbi_12106 crossref_primary_10_1016_j_ecolmodel_2015_12_016 crossref_primary_10_1093_biolinnean_blac134 crossref_primary_10_1002_ece3_1387 crossref_primary_10_1111_ddi_13834 crossref_primary_10_1016_j_ecolmodel_2018_02_002 crossref_primary_10_1111_ecog_00580 crossref_primary_10_1016_j_ecolmodel_2017_07_027 crossref_primary_10_1111_jbi_12234 crossref_primary_10_1007_s11284_015_1263_5 crossref_primary_10_1556_168_2019_20_3_4 crossref_primary_10_1016_j_actao_2020_103526 crossref_primary_10_1111_brv_12222 crossref_primary_10_1016_j_landusepol_2015_10_004 crossref_primary_10_1086_679440 crossref_primary_10_1002_ajb2_1809 crossref_primary_10_3354_cr01463 crossref_primary_10_1007_s10531_017_1379_8 crossref_primary_10_1093_icb_icx057 crossref_primary_10_1002_eap_1434 crossref_primary_10_1111_geb_12678 crossref_primary_10_1590_0102_33062018abb0220 crossref_primary_10_1111_ddi_12872 crossref_primary_10_1016_j_ecoinf_2024_102644 crossref_primary_10_1111_ele_12770 crossref_primary_10_1002_ecy_2221 crossref_primary_10_1007_s10764_015_9875_8 crossref_primary_10_1111_jbi_12224 crossref_primary_10_1111_2041_210X_13106 crossref_primary_10_1111_geb_12311 crossref_primary_10_1111_jav_01225 crossref_primary_10_1016_j_pt_2014_02_003 crossref_primary_10_1111_ecog_04728 crossref_primary_10_1111_ddi_12520 crossref_primary_10_1111_ddi_12406 crossref_primary_10_1111_j_1365_2699_2012_02752_x crossref_primary_10_1038_s41598_018_28291_3 crossref_primary_10_1080_22221751_2019_1661217 crossref_primary_10_1139_cjfas_2015_0598 crossref_primary_10_1093_icesjms_fsu107 crossref_primary_10_1186_s13071_015_0915_1 crossref_primary_10_1016_j_jnc_2016_09_005 crossref_primary_10_1080_00063657_2017_1358251 crossref_primary_10_1111_j_1600_0587_2013_00345_x crossref_primary_10_1016_j_ecolmodel_2014_06_005 crossref_primary_10_1111_1365_2664_12862 crossref_primary_10_1186_s12898_020_00308_4 crossref_primary_10_1016_j_scitotenv_2018_03_212 crossref_primary_10_1111_ddi_12895 crossref_primary_10_1126_science_1222732 crossref_primary_10_1111_ele_12757 crossref_primary_10_1111_ecog_03625 crossref_primary_10_24072_pci_ecology_100082 crossref_primary_10_1016_j_apm_2018_09_032 crossref_primary_10_1111_nyas_12211 crossref_primary_10_1111_1365_2745_12049 crossref_primary_10_1111_aec_12569 crossref_primary_10_1111_jbi_12200 crossref_primary_10_1111_oik_07502 crossref_primary_10_1371_journal_pone_0126524 crossref_primary_10_3897_natureconservation_6_6498 crossref_primary_10_1111_nph_12929 crossref_primary_10_1111_2041_210X_12397 crossref_primary_10_1002_ecm_1469 crossref_primary_10_1016_j_seares_2013_04_008 crossref_primary_10_1038_s41598_020_80027_4 crossref_primary_10_1038_s41598_017_07009_x crossref_primary_10_3389_fmars_2016_00202 crossref_primary_10_1002_ece3_4691 crossref_primary_10_1016_j_ppees_2013_03_003 crossref_primary_10_1111_oik_01426 crossref_primary_10_1126_science_1237184 crossref_primary_10_1016_j_ecss_2018_05_001 crossref_primary_10_1016_j_tree_2021_01_002 crossref_primary_10_1111_ele_12043 crossref_primary_10_1111_jbi_12794 crossref_primary_10_1111_ecog_02441 crossref_primary_10_1111_jbi_12435 crossref_primary_10_1111_2041_210X_12723 crossref_primary_10_1111_1365_2745_13280 crossref_primary_10_1111_j_1600_0587_2013_00077_x crossref_primary_10_1080_09670874_2018_1533664 crossref_primary_10_1111_ecog_00819 crossref_primary_10_1016_j_ecocom_2014_01_002 crossref_primary_10_1016_j_tree_2015_09_007 crossref_primary_10_1038_s41559_023_02287_3 crossref_primary_10_1111_geb_12357 crossref_primary_10_1890_14_0739_1 crossref_primary_10_1111_1365_2656_13297 crossref_primary_10_1111_oik_09818 crossref_primary_10_1086_683606 crossref_primary_10_1111_2041_210X_12731 crossref_primary_10_3390_rs10060814 crossref_primary_10_1016_j_biocon_2014_09_042 crossref_primary_10_1111_j_1557_9263_2012_00361_x crossref_primary_10_1002_ece3_6096 crossref_primary_10_1111_2041_210X_12180 crossref_primary_10_1111_ecog_00845 crossref_primary_10_1007_s11258_013_0266_y crossref_primary_10_1111_geb_12741 crossref_primary_10_1111_nyas_12264 crossref_primary_10_1111_ecog_01134 crossref_primary_10_1111_ecog_03315 crossref_primary_10_1371_journal_pone_0194650 crossref_primary_10_1093_cz_zow069 crossref_primary_10_1111_ecog_05973 crossref_primary_10_1111_fwb_12580 crossref_primary_10_1111_2041_210X_12502 crossref_primary_10_1111_ecog_01490 crossref_primary_10_1111_2041_210X_12501 crossref_primary_10_1111_ecog_06143 crossref_primary_10_1098_rstb_2012_0238 crossref_primary_10_1016_j_biocon_2016_11_008 crossref_primary_10_1111_jeb_12619 crossref_primary_10_1111_gcb_12853 crossref_primary_10_1111_ddi_12694 crossref_primary_10_1016_j_ecolmodel_2015_06_032 crossref_primary_10_1016_j_jmarsys_2014_03_003 crossref_primary_10_1111_jbi_13606 crossref_primary_10_1111_ecog_01129 crossref_primary_10_1111_jbi_13608 crossref_primary_10_1111_oik_01559 crossref_primary_10_1111_ecog_00954 crossref_primary_10_1016_j_tree_2017_03_008 crossref_primary_10_1002_ece3_4800 crossref_primary_10_1111_jvs_12232 crossref_primary_10_1111_oik_08756 crossref_primary_10_1007_s41745_021_00237_1 crossref_primary_10_1111_jvs_12597 crossref_primary_10_1007_s00114_013_1132_4 crossref_primary_10_1016_j_tree_2016_08_003 crossref_primary_10_1038_s41598_019_56515_7 crossref_primary_10_1086_682336 crossref_primary_10_1007_s10841_017_0001_4 crossref_primary_10_4236_oje_2023_137030 crossref_primary_10_1146_annurev_ecolsys_112414_054441 crossref_primary_10_1111_geb_12967 crossref_primary_10_1111_geb_12726 crossref_primary_10_1111_ecog_01714 crossref_primary_10_1111_j_1600_0587_2013_00643_x crossref_primary_10_1139_cjfas_2017_0150 crossref_primary_10_1007_s10530_018_1864_3 crossref_primary_10_1111_oik_03893 crossref_primary_10_1002_ecm_1370 crossref_primary_10_1111_ecog_02480 crossref_primary_10_1111_ecog_03571 crossref_primary_10_1111_j_1600_0587_2013_00574_x crossref_primary_10_1111_ecog_02481 crossref_primary_10_1111_nph_19453 crossref_primary_10_3389_fmars_2020_00300 crossref_primary_10_1002_ece3_3294 crossref_primary_10_1111_bij_12567 crossref_primary_10_1111_ele_12372 crossref_primary_10_1007_s11258_016_0598_5 crossref_primary_10_1038_nclimate1954 crossref_primary_10_1016_j_jasrep_2017_11_016 crossref_primary_10_1016_j_foreco_2022_120356 crossref_primary_10_3354_meps13163 crossref_primary_10_1086_695984 crossref_primary_10_1103_PhysRevE_109_024309 crossref_primary_10_1111_gcb_13167 crossref_primary_10_3897_rio_3_e14944 crossref_primary_10_1111_geb_12666 crossref_primary_10_1111_geb_12422 crossref_primary_10_1002_ece3_4399 crossref_primary_10_1016_j_ecolmodel_2020_108956 crossref_primary_10_1007_s10980_018_0622_3 crossref_primary_10_1111_2041_210X_13518 crossref_primary_10_1086_679505 crossref_primary_10_1655_HERPETOLOGICA_D_17_00064_1 crossref_primary_10_1007_s10336_022_01970_9 crossref_primary_10_1371_journal_pone_0128675 crossref_primary_10_1016_j_rse_2014_04_026 crossref_primary_10_1016_j_ecolmodel_2016_06_008 crossref_primary_10_3390_d15010061 crossref_primary_10_1111_fwb_13476 crossref_primary_10_1126_sciadv_aat4858 crossref_primary_10_1111_brv_12366 crossref_primary_10_1111_1365_2435_14429 crossref_primary_10_1111_2041_210X_14050 crossref_primary_10_1016_j_ecolmodel_2015_04_003 crossref_primary_10_1093_mollus_eyu045 crossref_primary_10_1186_s40462_014_0016_3 crossref_primary_10_1038_s41598_023_48204_3 crossref_primary_10_1002_ece3_508 crossref_primary_10_1111_jbi_14106 crossref_primary_10_1007_s00300_015_1820_y crossref_primary_10_1016_j_ecolmodel_2018_11_013 crossref_primary_10_1016_j_ecolind_2015_11_003 crossref_primary_10_1002_ecs2_2004 crossref_primary_10_1111_ddi_12265 crossref_primary_10_1111_gcb_12289 crossref_primary_10_3390_pr2040711 crossref_primary_10_1007_s10113_023_02052_z crossref_primary_10_1016_j_biocon_2016_11_030 crossref_primary_10_11609_jott_8357_15_12_24331_24344 crossref_primary_10_1111_jbi_12029 crossref_primary_10_1038_s41598_019_57280_3 crossref_primary_10_1111_oik_01704 crossref_primary_10_1073_pnas_1415442111 crossref_primary_10_1111_jbi_12825 crossref_primary_10_1111_jvs_12444 crossref_primary_10_1016_j_ecolmodel_2015_11_007 crossref_primary_10_1111_jbi_12032 crossref_primary_10_1515_mammalia_2018_0035 crossref_primary_10_1016_j_scitotenv_2022_160370 crossref_primary_10_1111_jbi_12031 crossref_primary_10_1007_s00114_013_1088_4 crossref_primary_10_1111_ele_13664 crossref_primary_10_1111_geb_12759 crossref_primary_10_1111_j_1600_0587_2013_00195_x crossref_primary_10_1111_cobi_13797 crossref_primary_10_1111_ecog_02836 crossref_primary_10_1029_2018MS001352 crossref_primary_10_1007_s00227_020_03791_x crossref_primary_10_1016_j_envsoft_2017_02_012 crossref_primary_10_1002_ece3_843 crossref_primary_10_1111_oik_03438 crossref_primary_10_3354_meps10659 crossref_primary_10_1016_j_jnc_2023_126490 crossref_primary_10_1111_tbed_14376 crossref_primary_10_1093_jmammal_gyx070 crossref_primary_10_1890_ES14_00323_1 crossref_primary_10_1111_oik_01149 crossref_primary_10_1071_BT21124 crossref_primary_10_1111_bij_12884 crossref_primary_10_1038_s41598_019_52256_9 crossref_primary_10_1111_2041_210X_12332 crossref_primary_10_1371_journal_pone_0054179 crossref_primary_10_1016_j_ecolind_2016_07_043 |
Cites_doi | 10.1101/SQB.1957.022.01.039 10.1146/annurev.ecolsys.34.011802.132342 10.1073/pnas.0905137106 10.1126/science.1123412 10.1098/rspb.2009.0693 10.2307/1426632 10.1111/j.1600-0587.2010.06229.x 10.1007/s10531-011-9995-1 10.1098/rspb.2006.0311 10.1111/j.1365-2486.2008.01557.x 10.1098/rspb.2010.1371 10.1046/j.1466-822X.2003.00042.x 10.1111/j.1466-8238.2007.00359.x 10.1038/414716a 10.1111/j.1365-2745.2004.00838.x 10.1890/09-1340.1 10.1111/j.0021-8790.2004.00833.x 10.1214/09-BA403 10.1890/02-0344 10.1016/j.tree.2005.04.005 10.1111/j.1365-2656.2010.01699.x 10.1201/b16974 10.1890/10-0173.1 10.1016/j.ecolmodel.2005.10.003 10.1890/10-1086.1 10.1146/annurev-ecolsys-102209-144718 10.1093/aob/mcp027 10.1111/j.2041-210X.2010.00060.x 10.1111/j.1461-0248.2008.01270.x 10.1016/j.tree.2008.08.003 10.7208/chicago/9780226101811.001.0001 10.1111/j.1466-8238.2010.00613.x 10.1016/j.ecocom.2007.05.002 10.1890/07-0451.1 10.1371/journal.pone.0012092 10.1146/annurev.ecolsys.39.110707.173430 10.1111/j.1461-0248.2005.00810.x 10.1890/0012-9658(2001)082[2560:CIBTSI]2.0.CO;2 10.1890/08-0657.1 10.1093/aob/mcp031 10.1111/j.1466-8238.2011.00679.x 10.1111/j.1365-2486.2008.01671.x 10.1098/rspb.2009.0523 10.1146/annurev.ecolsys.36.091704.175535 10.1111/j.1096-3642.1935.tb01680.x 10.1034/j.1600-0706.2003.12031.x 10.1146/annurev.ecolsys.39.110707.173434 10.1515/9781400842933 10.1016/j.tree.2010.03.002 10.1111/j.1461-0248.2008.01170.x 10.1016/j.ecolmodel.2005.11.046 10.2307/3237001 10.1046/j.1365-2745.1998.00268.x 10.1146/annurev.ecolsys.38.091206.095818 10.1146/annurev.ecolsys.110308.120159 10.1890/03-9000 10.1023/A:1012525626267 10.1098/rspb.2010.0244 10.1093/biomet/85.2.347 10.1111/j.1365-2699.2010.02405.x 10.1890/07-1748.1 10.1111/j.1461-0248.2005.00792.x 10.1086/282505 10.1214/09-AOAS250 10.1111/j.1466-8238.2010.00607.x 10.1111/j.1467-9868.2008.00663.x 10.1098/rstb.2010.0008 10.1073/pnas.0706375104 10.1890/09-1175.1 10.1111/j.1466-8238.2006.00293.x 10.1046/j.1365-2435.2002.00664.x 10.1111/j.1600-0587.2010.05892.x 10.1111/j.1461-0248.2008.01250.x 10.1111/j.1466-8238.2007.00345.x 10.1890/10-0602.1 10.1007/978-3-540-32730-1_15 10.1111/j.2517-6161.1996.tb02080.x 10.1111/j.1365-2656.2008.01460.x 10.1146/annurev.es.22.110191.000555 10.1111/j.1365-2656.2010.01743.x 10.1126/science.1065973 10.1111/j.2006.0906-7590.04596.x 10.2307/2845499 10.1007/s10021-008-9166-8 10.1038/35004572 |
ContentType | Journal Article |
Copyright | Copyright © 2012 Blackwell Publishing Ltd. 2011 Blackwell Publishing Ltd |
Copyright_xml | – notice: Copyright © 2012 Blackwell Publishing Ltd. – notice: 2011 Blackwell Publishing Ltd |
DBID | BSCLL AAYXX CITATION 7SN 7SS 8FD C1K FR3 P64 RC3 7ST 7U6 |
DOI | 10.1111/j.1365-2699.2011.02663.x |
DatabaseName | Istex CrossRef Ecology Abstracts Entomology Abstracts (Full archive) Technology Research Database Environmental Sciences and Pollution Management Engineering Research Database Biotechnology and BioEngineering Abstracts Genetics Abstracts Environment Abstracts Sustainability Science Abstracts |
DatabaseTitle | CrossRef Entomology Abstracts Genetics Abstracts Technology Research Database Engineering Research Database Ecology Abstracts Biotechnology and BioEngineering Abstracts Environmental Sciences and Pollution Management Environment Abstracts Sustainability Science Abstracts |
DatabaseTitleList | CrossRef Ecology Abstracts Entomology Abstracts |
DeliveryMethod | fulltext_linktorsrc |
Discipline | Geography Biology Ecology |
EISSN | 1365-2699 |
EndPage | 2178 |
ExternalDocumentID | 2819504311 10_1111_j_1365_2699_2011_02663_x JBI2663 23354520 ark_67375_WNG_07MT5173_K |
Genre | article |
GroupedDBID | -~X .3N .GA .Y3 05W 0R~ 10A 1OB 1OC 29J 31~ 33P 3SF 4.4 50Y 50Z 51W 51X 52M 52N 52O 52P 52S 52T 52U 52W 52X 53G 5GY 5HH 5LA 5VS 66C 702 7PT 8-0 8-1 8-3 8-4 8-5 8UM 930 A03 AAESR AAEVG AAHHS AAHKG AAISJ AAKGQ AANLZ AAONW AASGY AAXRX AAZKR ABBHK ABCQN ABCUV ABEML ABHUG ABJNI ABLJU ABPLY ABPPZ ABPTK ABPVW ABTLG ACAHQ ACBWZ ACCFJ ACCZN ACGFS ACPOU ACPRK ACSCC ACSTJ ACXBN ACXME ACXQS ADAWD ADBBV ADDAD ADEOM ADIZJ ADKYN ADMGS ADOZA ADULT ADXAS ADZLD ADZMN ADZOD AEEZP AEIGN AEIMD AENEX AEQDE AESBF AEUPB AEUQT AEUYR AFAZZ AFBPY AFEBI AFFPM AFGKR AFPWT AFRAH AFVGU AFZJQ AGJLS AGUYK AHBTC AI. AIRJO AIURR AIWBW AJBDE AJXKR ALAGY ALMA_UNASSIGNED_HOLDINGS ALUQN AMBMR AMYDB ANHSF ASPBG ATUGU AUFTA AVWKF AZBYB AZFZN AZVAB BAFTC BDRZF BFHJK BHBCM BMNLL BMXJE BNHUX BROTX BRXPI BSCLL BY8 CAG CBGCD COF CS3 CUYZI CWIXF D-E D-F DCZOG DEVKO DOOOF DPXWK DR2 DRFUL DRSTM DU5 DWIUU EBS ECGQY EJD EQZMY ESX F00 F01 F04 F5P FEDTE G-S G.N GODZA GTFYD H.T H.X HF~ HGD HQ2 HTVGU HVGLF HZI HZ~ H~9 IHE IX1 J0M JAAYA JBMMH JBS JEB JENOY JHFFW JKQEH JLS JLXEF JPM JSODD JST K48 LATKE LC2 LC3 LEEKS LH4 LITHE LOXES LP6 LP7 LUTES LW6 LYRES MEWTI MK4 MRFUL MRSTM MSFUL MSSTM MXFUL MXSTM N04 N05 N9A NF~ O66 O9- P2P P2W P2X P4D Q.N Q11 QB0 R.K ROL RX1 SA0 SAMSI SUPJJ TN5 UB1 VH1 VOH VQP W8V W99 WBKPD WIH WIK WMRSR WOHZO WQJ WRC WSUWO WXSBR XG1 YQT ZZTAW ~02 ~IA ~KM ~WT ABXSQ AQVQM AAHBH ADACV AITYG HGLYW IPSME OIG AAYXX CITATION 7SN 7SS 8FD C1K FR3 P64 RC3 7ST 7U6 |
ID | FETCH-LOGICAL-c4343-61214b71519f7caa0ba3eeb7d8d569dee6e3ea42383f356e3e9dfe89be16f3833 |
IEDL.DBID | DR2 |
ISSN | 0305-0270 |
IngestDate | Thu Aug 15 23:50:09 EDT 2024 Fri Sep 13 08:50:18 EDT 2024 Fri Aug 23 03:04:25 EDT 2024 Sat Aug 24 01:06:15 EDT 2024 Fri Feb 02 07:02:45 EST 2024 Wed Jan 17 05:02:30 EST 2024 |
IsPeerReviewed | true |
IsScholarly | true |
Issue | 12 |
Language | English |
LinkModel | DirectLink |
MergedId | FETCHMERGED-LOGICAL-c4343-61214b71519f7caa0ba3eeb7d8d569dee6e3ea42383f356e3e9dfe89be16f3833 |
Notes | ArticleID:JBI2663 ark:/67375/WNG-07MT5173-K istex:C604C0F2F2EB34DE0EB0DCF042C40F36584383C6 ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 |
PQID | 1171485390 |
PQPubID | 1086398 |
PageCount | 16 |
ParticipantIDs | proquest_miscellaneous_1257747705 proquest_journals_1171485390 crossref_primary_10_1111_j_1365_2699_2011_02663_x wiley_primary_10_1111_j_1365_2699_2011_02663_x_JBI2663 jstor_primary_23354520 istex_primary_ark_67375_WNG_07MT5173_K |
PublicationCentury | 2000 |
PublicationDate | 2012-12 20121201 December 2012 2012-12-00 |
PublicationDateYYYYMMDD | 2012-12-01 |
PublicationDate_xml | – month: 12 year: 2012 text: 2012-12 |
PublicationDecade | 2010 |
PublicationPlace | Oxford, UK |
PublicationPlace_xml | – name: Oxford, UK – name: Oxford |
PublicationTitle | Journal of biogeography |
PublicationYear | 2012 |
Publisher | Blackwell Publishing Ltd Blackwell Publishing Wiley Subscription Services, Inc |
Publisher_xml | – name: Blackwell Publishing Ltd – name: Blackwell Publishing – name: Wiley Subscription Services, Inc |
References | Schweiger, O., Heikkinen, R.K., Harpke, A., Hickler, T., Klotz, S., Kudrna, O., Kühn, I., Pöyry, J. & Settele, J. (2012) Increasing range mismatching of interacting species under global change is related to their ecological characteristics. Global Ecology and Biogeography, 21, 88-99. Chib, S. & Greenberg, E. (1998) Analysis of multivariate probit models. Biometrika, 85, 347-361. Kissling, W.D., Rahbek, C. & Böhning-Gaese, K. (2007) Food plant diversity as broad-scale determinant of avian frugivore richness. Proceedings of the Royal Society B: Biological Sciences, 274, 799-808. Holt, R.D. (2009) Bringing the Hutchinsonian niche into the 21st century: ecological and evolutionary perspectives. Proceedings of the National Academy of Sciences USA, 106, 19,659-19,665. Solé, R.V. & Bascompte, J. (2006) Self-organization in complex ecosystems. Princeton University Press, Princeton, NJ. Amarasekare, P. (2008) Spatial dynamics of foodwebs. Annual Review of Ecology, Evolution, and Systematics, 39, 479-500. Eviner, V.T. & Chapin, F.S., III (2003) Functional matrix: a conceptual framework for predicting multiple plant effects on ecosystem processes. Annual Review of Ecology, Evolution, and Systematics, 34, 455-485. Williams, R.J., Anandanadesan, A. & Purves, D. (2010) The probabilistic niche model reveals the niche structure and role of body size in a complex food web. PLoS ONE, 5, e12092. Olesen, J.M., Bascompte, J., Elberling, H. & Jordano, P. (2008) Temporal dynamics in a pollination network. Ecology, 89, 1573-1582. Lotka, A.J. (1925) Elements of physical biology. Williams and Wilkins, Baltimore, MD. MacArthur, R.H. (1972) Geographical ecology. Princeton University Press, Princeton, NJ. Ovaskainen, O., Hottola, J. & Siitonen, J. (2010) Modeling species co-occurrence by multivariate logistic regression generates new hypotheses on fungal interactions. Ecology, 91, 2514-2521. Lavorel, S. & Garnier, E. (2002) Predicting changes in community composition and ecosystem functioning from plant traits: revisiting the Holy Grail. Functional Ecology, 16, 545-556. Scotti, M., Podani, J. & Jordán, F. (2007) Weighting, scale dependence and indirect effects in ecological networks: a comparative study. Ecological Complexity, 4, 148-159. Vázquez, D.P., Morris, W.F. & Jordano, P. (2005) Interaction frequency as a surrogate for the total effect of animal mutualists on plants. Ecology Letters, 8, 1088-1094. Rüger, N., Williams-Linera, G., Kissling, W.D. & Huth, A. (2008) Long-term impacts of fuelwood extraction on a tropical montane cloud forest. Ecosystems, 11, 868-881. Manly, B.F.G. (2004) Multivariate statistical methods: a primer, 3rd edn. Chapman & Hall/CRC, Boca Raton, FL. Banerjee, S., Gelfand, A.E., Finley, A.O. & Sang, H. (2008) Gaussian predictive process models for large spatial data sets. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 70, 825-848. Schurr, F.M., Spiegel, O., Steinitz, O., Trakhtenbrot, A., Tsoar, A. & Nathan, R. (2009) Long-distance seed dispersal. Annual Plant Reviews, 38, 204-237. Stang, M., Klinkhamer, P.G.L., Waser, N.M., Stang, I. & van der Meijden, E. (2009) Size-specific interaction patterns and size matching in a plant-pollinator interaction web. Annals of Botany, 103, 1459-1469. Pearson, R.G. & Dawson, T.P. (2003) Predicting the impacts of climate change on the distribution of species: are bioclimate envelope models useful?Global Ecology and Biogeography, 12, 361-371. Reineking, B. & Schröder, B. (2006) Constrain to perform: regularization of habitat models. Ecological Modelling, 193, 675-690. Hutchinson, G.E. (1957) Population studies: animal ecology and demography. Concluding remarks. Cold Spring Harbor Symposia on Quantitative Biology, 22, 415-427. Hickler, T., Smith, B., Sykes, M.T., Davis, M.B., Sugita, S. & Walker, K. (2004) Using a generalized vegetation model to simulate vegetation dynamics in northeastern USA. Ecology, 85, 519-530. MacArthur, R.H. & Levins, R. (1967) Limiting similarity, convergence, and divergence of coexisting species. The American Naturalist, 101, 377-385. Araújo, M.B. & Luoto, M. (2007) The importance of biotic interactions for modelling species distributions under climate change. Global Ecology and Biogeography, 16, 743-753. Finley, A.O., Banerjee, S. & McRoberts, R.E. (2009) Hierarchical spatial models for predicting tree species assemblages across large domains. The Annals of Applied Statistics, 3, 1052-1079. Ollerton, J., Alarcón, R., Waser, N.M., Price, M.V., Watts, S., Cranmer, L., Hingston, A., Peter, C.I. & Rotenberry, J. (2009) A global test of the pollination syndrome hypothesis. Annals of Botany, 103, 1471-1480. Grenfell, B.T., Bjørnstad, O.N. & Kappey, J. (2001) Travelling waves and spatial hierarchies in measles epidemics. Nature, 414, 716-723. Gustafson, P. (2004) Measurement error and misclassification in statistics and epidemiology. Chapman & Hall/CRC, Boca Raton, FL. Woodward, G., Ebenman, B., Emmerson, M., Montoya, J.M., Olesen, J.M., Valido, A. & Warren, P.H. (2005) Body size in ecological networks. Trends in Ecology and Evolution, 20, 402-409. Elith, J. & Leathwick, J.R. (2009) Species distribution models: ecological explanation and prediction across space and time. Annual Review of Ecology, Evolution, and Systematics, 40, 677-697. Pagel, J. & Schurr, F.M. (2011) Forecasting species ranges by statistical estimation of ecological niches and spatial population dynamics. Global Ecology and Biogeography, doi: 10.1111/j.1466-8238.2011.00663.x. Schiffers, K., Tielbörger, K., Tietjen, B. & Jeltsch, F. (2011) Root plasticity buffers competition among plants: theory meets experimental data. Ecology, 92, 610-620. Brown, J.H., Gillooly, J.F., Allen, A.P., Savage, V.M. & West, G.B. (2004) Toward a metabolic theory of ecology. Ecology, 85, 1771-1789. Prentice, I.C., Cramer, W., Harrison, S.P., Leemans, R., Monserud, R.A. & Solomon, A.M. (1992) A global biome model based on plant physiology and dominance, soil properties and climate. Journal of Biogeography, 19, 117-134. Fink, D., Hochachka, W.M., Zuckerberg, B., Winkler, D.W., Shaby, B., Munson, M.A., Hooker, G., Riedewald, M., Sheldon, D. & Kelling, S. (2010) Spatiotemporal exploratory models for broad-scale survey data. Ecological Applications, 20, 2131-2147. Schemske, D.W., Mittelbach, G.G., Cornell, H.V., Sobel, J.M. & Roy, K. (2009) Is there a latitudinal gradient in the importance of biotic interactions?Annual Review of Ecology, Evolution, and Systematics, 40, 245-269. Gilman, S.E., Urban, M.C., Tewksbury, J., Gilchrist, G.W. & Holt, R.D. (2010) A framework for community interactions under climate change. Trends in Ecology and Evolution, 25, 325-331. Blaum, N., Mosner, E., Schwager, M. & Jeltsch, F. (2011) How functional is functional? Ecological groupings in terrestrial animal ecology: towards an animal functional type approach. Biodiversity and Conservation, 20, 2333-2345. Nicholson, A.J. & Bailey, V.A. (1935) The balance of animal populations.-Part I. Proceedings of the Zoological Society of London, 105, 551-598. Montoya, J.M. & Solé, R.V. (2003) Topological properties of food webs: from real data to community assembly models. Oikos, 102, 614-622. Hutchinson, G.E. (1978) An introduction to population biology. Yale University Press, New Haven, CT. Suding, K.N., Lavorel, S., Chapin, F.S., III, Cornelissen, J.H.C., Díaz, S., Garnier, E., Goldberg, D., Hooper, D.U., Jackson, S.T. & Navas, M.-L. (2008) Scaling environmental change through the community-level: a trait-based response-and-effect framework for plants. Global Change Biology, 14, 1125-1140. Kuparinen, A., Katul, G., Nathan, R. & Schurr, F.M. (2009) Increases in air temperature can promote wind-driven dispersal and spread of plants. Proceedings of the Royal Society B: Biological Sciences, 276, 3081-3087. Schurr, F.M., Midgley, G.F., Rebelo, A.G., Reeves, G., Poschlod, P. & Higgins, S.I. (2007) Colonization and persistence ability explain the extent to which plant species fill their potential range. Global Ecology and Biogeography, 16, 449-459. Bascompte, J., Jordano, P. & Olesen, J.M. (2006) Asymmetric coevolutionary networks facilitate biodiversity maintenance. Science, 312, 431-433. Olesen, J.M., Bascompte, J., Dupont, Y.L., Elberling, H., Rasmussen, C. & Jordano, P. (2011) Missing and forbidden links in mutualistic networks. Proceedings of the Royal Society B: Biological Sciences, 278, 725-732. Fotheringham, A.S., Brunsdon, C. & Charlton, M. (2002) Geographically weighted regression: the analysis of spatially varying relationships. John Wiley & Sons, Chichester. Smith, T.M., Shugart, H.H. & Woodward, F.I. (1997) Plant functional types: their relevance to ecosystem properties and global change. Cambridge University Press, Cambridge. Meier, E.S., Lischke, H., Schmatz, D.R. & Zimmermann, N.E. (2011b) Climate, competition and connectivity affect future migration and ranges of European trees. Global Ecology and Biogeography, doi: 10.1111/j.1466-8238.2011.00669.x. Mutshinda, C.M., O'Hara, R.B. & Woiwod, I.P. (2009) What drives community dynamics?Proceedings of the Royal Society B: Biological Sciences, 276, 2923-2929. Tibshirani, R. (1996) Regression shrinkage and selection via the lasso. Journal of the Royal Statistical Society. Series B (Methodological), 58, 267-288. Rouget, M., Richardson, D.M., Lavorel, S., Vayreda, J., Gracia, C. & Milton, S.J. (2001) Determinants of distribution of six Pinus species in Catalonia, Spain. Journal of Vegetation Science, 12, 491-502. Hickler, T., Vohland, K., Feehan, J., Miller, P., Smith, B., Costa, L., Giesecke, T., Fronzek, S., Carter, T., Cramer, W., Kühn, I. & Sykes, M. (2012) Projecting the future distribution of European potential natural vegetation zones with a generalized, tree species-based dynamic vegetation model. Global Ecology and Biogeography, 21, 50-63. Faegri, K. & van der Pijl, L. (1979) The principles of pollination biology. Pergamon Press, Oxford. Guimerá, R., Stouffer, D.B., Sales-Pardo, M., Leicht, E.A., Newman, M.E.J. & Ama 2002; 16 2007; 104 2011; 278 2009; 40 2008; 39 2009; 276 1992; 19 2005; 20 1973 2011b 1972 1998; 85 2008; 70 2011a; 38 1998; 86 1979 1978 1935; 105 2007; 38 2003; 12 2009; 12 2010; 20 2001; 294 2004; 73 2010; 25 2006; 21 2000; 404 2009; 90 2010; 277 2011; 20 1967; 101 2006; 29 2008; 23 2007; 4 2001; 12 2010; 5 2001; 51 2012; 21 2006; 442 2001; 414 2005; 36 2010; 33 2004; 85 2011; 2 2010; 79 2011 1978; 10 2011; 80 2011; 81 2010; 365 2008; 14 1997 2007 2006 1994 2004 2011; 34 2008; 11 2003 2002 2006; 193 1996; 58 2006; 199 2006; 312 2007; 16 2003; 34 1957; 22 2009; 78 2001; 82 1991; 22 2005; 8 2011; 92 2007; 274 2011; 41 2008; 89 2009; 4 2009; 3 2010; 91 2003; 102 1926 1925 2009; 103 2009; 38 2009; 106 Faegri K. (e_1_2_8_18_1) 1979 e_1_2_8_26_1 e_1_2_8_49_1 e_1_2_8_68_1 e_1_2_8_5_1 e_1_2_8_9_1 e_1_2_8_45_1 e_1_2_8_64_1 e_1_2_8_87_1 Pagel J. (e_1_2_8_69_1) 2011 e_1_2_8_41_1 e_1_2_8_60_1 Hutchinson G.E. (e_1_2_8_35_1) 1978 Meier E.S. (e_1_2_8_55_1) 2011 e_1_2_8_19_1 Schurr F.M. (e_1_2_8_83_1) 2009; 38 e_1_2_8_15_1 e_1_2_8_38_1 e_1_2_8_57_1 e_1_2_8_91_1 e_1_2_8_95_1 e_1_2_8_99_1 e_1_2_8_34_1 e_1_2_8_53_1 e_1_2_8_76_1 e_1_2_8_101_1 Burnham K.P. (e_1_2_8_11_1) 2002 e_1_2_8_30_1 e_1_2_8_72_1 e_1_2_8_29_1 e_1_2_8_25_1 Sauer J.R. (e_1_2_8_79_1) 2007 e_1_2_8_2_1 Volterra V. (e_1_2_8_96_1) 1926 e_1_2_8_6_1 e_1_2_8_21_1 e_1_2_8_67_1 e_1_2_8_44_1 e_1_2_8_86_1 e_1_2_8_63_1 e_1_2_8_40_1 e_1_2_8_82_1 e_1_2_8_14_1 e_1_2_8_37_1 Smith T.M. (e_1_2_8_89_1) 1997 May R.M. (e_1_2_8_51_1) 1973 e_1_2_8_94_1 e_1_2_8_90_1 Fotheringham A.S. (e_1_2_8_22_1) 2002 e_1_2_8_98_1 e_1_2_8_10_1 e_1_2_8_56_1 e_1_2_8_33_1 e_1_2_8_75_1 e_1_2_8_52_1 e_1_2_8_102_1 Lotka A.J. (e_1_2_8_47_1) 1925 e_1_2_8_71_1 e_1_2_8_24_1 e_1_2_8_3_1 e_1_2_8_81_1 e_1_2_8_7_1 e_1_2_8_20_1 e_1_2_8_43_1 e_1_2_8_66_1 e_1_2_8_62_1 e_1_2_8_85_1 e_1_2_8_17_1 e_1_2_8_13_1 e_1_2_8_36_1 e_1_2_8_59_1 e_1_2_8_70_1 e_1_2_8_97_1 e_1_2_8_32_1 e_1_2_8_78_1 e_1_2_8_74_1 MacArthur R.H. (e_1_2_8_48_1) 1972 e_1_2_8_93_1 e_1_2_8_46_1 e_1_2_8_27_1 e_1_2_8_80_1 e_1_2_8_4_1 e_1_2_8_8_1 e_1_2_8_42_1 e_1_2_8_88_1 e_1_2_8_23_1 e_1_2_8_65_1 e_1_2_8_84_1 Woiwod I.P. (e_1_2_8_100_1) 1994 e_1_2_8_61_1 e_1_2_8_39_1 e_1_2_8_16_1 e_1_2_8_58_1 Gustafson P. (e_1_2_8_28_1) 2004 e_1_2_8_92_1 e_1_2_8_31_1 e_1_2_8_77_1 e_1_2_8_12_1 e_1_2_8_54_1 e_1_2_8_73_1 e_1_2_8_50_1 |
References_xml | – volume: 34 start-page: 455 year: 2003 end-page: 485 article-title: Functional matrix: a conceptual framework for predicting multiple plant effects on ecosystem processes publication-title: Annual Review of Ecology, Evolution, and Systematics – volume: 80 start-page: 101 year: 2011 end-page: 107 article-title: A multispecies perspective on ecological impacts of climatic forcing publication-title: Journal of Animal Ecology – volume: 85 start-page: 347 year: 1998 end-page: 361 article-title: Analysis of multivariate probit models publication-title: Biometrika – volume: 16 start-page: 754 year: 2007 end-page: 763 article-title: Biotic interactions improve prediction of boreal bird distributions at macro‐scales publication-title: Global Ecology and Biogeography – volume: 73 start-page: 585 year: 2004 end-page: 598 article-title: Interaction strengths in food webs: issues and opportunities publication-title: Journal of Animal Ecology – volume: 40 start-page: 245 year: 2009 end-page: 269 article-title: Is there a latitudinal gradient in the importance of biotic interactions? publication-title: Annual Review of Ecology, Evolution, and Systematics – start-page: 175 year: 2007 end-page: 192 – volume: 294 start-page: 813 year: 2001 end-page: 817 article-title: Dynamics of the 2001 UK foot and mouth epidemic: stochastic dispersal in a heterogeneous landscape publication-title: Science – volume: 11 start-page: 1351 year: 2008 end-page: 1363 article-title: Global change and species interactions in terrestrial ecosystems publication-title: Ecology Letters – volume: 85 start-page: 1771 year: 2004 end-page: 1789 article-title: Toward a metabolic theory of ecology publication-title: Ecology – year: 2011b article-title: Climate, competition and connectivity affect future migration and ranges of European trees publication-title: Global Ecology and Biogeography – volume: 33 start-page: 1038 year: 2010 end-page: 1048 article-title: Biotic and abiotic variables show little redundancy in explaining tree species distributions publication-title: Ecography – volume: 90 start-page: 2426 year: 2009 end-page: 2433 article-title: Press perturbations and indirect effects in real food webs publication-title: Ecology – volume: 20 start-page: 402 year: 2005 end-page: 409 article-title: Body size in ecological networks publication-title: Trends in Ecology and Evolution – year: 2011 article-title: Forecasting species ranges by statistical estimation of ecological niches and spatial population dynamics publication-title: Global Ecology and Biogeography – volume: 38 start-page: 371 year: 2011a end-page: 382 article-title: Co‐occurrence patterns of trees along macro‐climatic gradients and their potential influence on the present and future distribution of L publication-title: Journal of Biogeography – volume: 19 start-page: 117 year: 1992 end-page: 134 article-title: A global biome model based on plant physiology and dominance, soil properties and climate publication-title: Journal of Biogeography – volume: 11 start-page: 868 year: 2008 end-page: 881 article-title: Long‐term impacts of fuelwood extraction on a tropical montane cloud forest publication-title: Ecosystems – volume: 414 start-page: 716 year: 2001 end-page: 723 article-title: Travelling waves and spatial hierarchies in measles epidemics publication-title: Nature – volume: 81 start-page: 329 year: 2011 end-page: 347 article-title: Decomposing environmental, spatial, and spatiotemporal components of species distributions publication-title: Ecological Monographs – year: 1972 – volume: 103 start-page: 1459 year: 2009 end-page: 1469 article-title: Size‐specific interaction patterns and size matching in a plant–pollinator interaction web publication-title: Annals of Botany – volume: 16 start-page: 545 year: 2002 end-page: 556 article-title: Predicting changes in community composition and ecosystem functioning from plant traits: revisiting the Holy Grail publication-title: Functional Ecology – volume: 105 start-page: 551 year: 1935 end-page: 598 article-title: The balance of animal populations.—Part I publication-title: Proceedings of the Zoological Society of London – volume: 92 start-page: 610 year: 2011 end-page: 620 article-title: Root plasticity buffers competition among plants: theory meets experimental data publication-title: Ecology – volume: 312 start-page: 431 year: 2006 end-page: 433 article-title: Asymmetric coevolutionary networks facilitate biodiversity maintenance publication-title: Science – volume: 79 start-page: 824 year: 2010 end-page: 835 article-title: Bird community response to fruit energy publication-title: Journal of Animal Ecology – volume: 38 start-page: 204 year: 2009 end-page: 237 article-title: Long‐distance seed dispersal publication-title: Annual Plant Reviews – volume: 278 start-page: 725 year: 2011 end-page: 732 article-title: Missing and forbidden links in mutualistic networks publication-title: Proceedings of the Royal Society B: Biological Sciences – volume: 21 start-page: 88 year: 2012 end-page: 99 article-title: Increasing range mismatching of interacting species under global change is related to their ecological characteristics publication-title: Global Ecology and Biogeography – volume: 40 start-page: 677 year: 2009 end-page: 697 article-title: Species distribution models: ecological explanation and prediction across space and time publication-title: Annual Review of Ecology, Evolution, and Systematics – year: 2007 – volume: 5 year: 2010 article-title: The probabilistic niche model reveals the niche structure and role of body size in a complex food web publication-title: PLoS ONE – volume: 91 start-page: 2941 year: 2010 end-page: 2951 article-title: Origin of compartmentalization in food webs publication-title: Ecology – year: 1973 – volume: 22 start-page: 115 year: 1991 end-page: 143 article-title: The guild concept and the structure of ecological communities publication-title: Annual Review of Ecology and Systematics – year: 1925 – volume: 12 start-page: 491 year: 2001 end-page: 502 article-title: Determinants of distribution of six species in Catalonia, Spain publication-title: Journal of Vegetation Science – volume: 14 start-page: 1125 year: 2008 end-page: 1140 article-title: Scaling environmental change through the community‐level: a trait‐based response‐and‐effect framework for plants publication-title: Global Change Biology – year: 2011 article-title: Bird dietary guild richness across latitudes, environments and biogeographic regions publication-title: Global Ecology and Biogeography – volume: 14 start-page: 2501 year: 2008 end-page: 2515 article-title: Habitat shifts of endangered species under altered climate conditions: importance of biotic interactions publication-title: Global Change Biology – volume: 8 start-page: 993 year: 2005 end-page: 1009 article-title: Predicting species distribution: offering more than simple habitat models publication-title: Ecology Letters – volume: 4 start-page: 85 year: 2009 end-page: 118 article-title: A review of Bayesian variable selection methods: what, how and which publication-title: Bayesian Analysis – year: 2002 – volume: 106 start-page: 19,659 year: 2009 end-page: 19,665 article-title: Bringing the Hutchinsonian niche into the 21st century: ecological and evolutionary perspectives publication-title: Proceedings of the National Academy of Sciences USA – volume: 102 start-page: 614 year: 2003 end-page: 622 article-title: Topological properties of food webs: from real data to community assembly models publication-title: Oikos – volume: 22 start-page: 415 year: 1957 end-page: 427 article-title: Population studies: animal ecology and demography. Concluding remarks publication-title: Cold Spring Harbor Symposia on Quantitative Biology – year: 1978 – volume: 10 start-page: 499 year: 1978 end-page: 537 article-title: Zone of influence models for competition in plantations publication-title: Advances in Applied Probability – volume: 82 start-page: 2560 year: 2001 end-page: 2573 article-title: Competitive interactions between tree species in New Zealand’s old‐growth indigenous forests publication-title: Ecology – volume: 58 start-page: 267 year: 1996 end-page: 288 article-title: Regression shrinkage and selection via the lasso publication-title: Journal of the Royal Statistical Society. Series B (Methodological) – volume: 11 start-page: 564 year: 2008 end-page: 575 article-title: Long‐term observation of a pollination network: fluctuation in species and interactions, relative invariance of network structure and implications for estimates of specialization publication-title: Ecology Letters – volume: 404 start-page: 180 year: 2000 end-page: 183 article-title: Simple rules yield complex food webs publication-title: Nature – volume: 276 start-page: 2923 year: 2009 end-page: 2929 article-title: What drives community dynamics? publication-title: Proceedings of the Royal Society B: Biological Sciences – volume: 277 start-page: 2983 year: 2010 end-page: 2990 article-title: Testing the heterospecific attraction hypothesis with time‐series data on species co‐occurrence publication-title: Proceedings of the Royal Society B: Biological Sciences – volume: 2 start-page: 143 year: 2011 end-page: 154 article-title: Comparing spatially‐varying coefficients models for analysis of ecological data with non‐stationary and anisotropic residual dependence publication-title: Methods in Ecology and Evolution – volume: 21 start-page: 50 year: 2012 end-page: 63 article-title: Projecting the future distribution of European potential natural vegetation zones with a generalized, tree species‐based dynamic vegetation model publication-title: Global Ecology and Biogeography – volume: 85 start-page: 519 year: 2004 end-page: 530 article-title: Using a generalized vegetation model to simulate vegetation dynamics in northeastern USA publication-title: Ecology – year: 1979 – volume: 16 start-page: 743 year: 2007 end-page: 753 article-title: The importance of biotic interactions for modelling species distributions under climate change publication-title: Global Ecology and Biogeography – start-page: 409 year: 1926 end-page: 448 – volume: 70 start-page: 825 year: 2008 end-page: 848 article-title: Gaussian predictive process models for large spatial data sets publication-title: Journal of the Royal Statistical Society: Series B (Statistical Methodology) – volume: 276 start-page: 3081 year: 2009 end-page: 3087 article-title: Increases in air temperature can promote wind‐driven dispersal and spread of plants publication-title: Proceedings of the Royal Society B: Biological Sciences – volume: 16 start-page: 449 year: 2007 end-page: 459 article-title: Colonization and persistence ability explain the extent to which plant species fill their potential range publication-title: Global Ecology and Biogeography – volume: 8 start-page: 1088 year: 2005 end-page: 1094 article-title: Interaction frequency as a surrogate for the total effect of animal mutualists on plants publication-title: Ecology Letters – volume: 78 start-page: 253 year: 2009 end-page: 269 article-title: Ecological networks – beyond food webs publication-title: Journal of Animal Ecology – year: 2004 – year: 1997 – volume: 12 start-page: 144 year: 2009 end-page: 154 article-title: Hierarchical models facilitate spatial analysis of large data sets: a case study on invasive plant species in the northeastern United States publication-title: Ecology Letters – volume: 34 start-page: 507 year: 2011 end-page: 518 article-title: Modelling the impact of climate and land use change on the geographical distribution of leaf anatomy in a temperate flora publication-title: Ecography – volume: 39 start-page: 479 year: 2008 end-page: 500 article-title: Spatial dynamics of foodwebs publication-title: Annual Review of Ecology, Evolution, and Systematics – volume: 38 start-page: 567 year: 2007 end-page: 593 article-title: Plant–animal mutualistic networks: the architecture of biodiversity publication-title: Annual Review of Ecology, Evolution, and Systematics – volume: 101 start-page: 377 year: 1967 end-page: 385 article-title: Limiting similarity, convergence, and divergence of coexisting species publication-title: The American Naturalist – volume: 3 start-page: 1052 year: 2009 end-page: 1079 article-title: Hierarchical spatial models for predicting tree species assemblages across large domains publication-title: The Annals of Applied Statistics – year: 2003 – volume: 103 start-page: 1471 year: 2009 end-page: 1480 article-title: A global test of the pollination syndrome hypothesis publication-title: Annals of Botany – volume: 51 start-page: 259 year: 2001 end-page: 305 article-title: A review of forest gap models publication-title: Climatic Change – volume: 91 start-page: 2514 year: 2010 end-page: 2521 article-title: Modeling species co‐occurrence by multivariate logistic regression generates new hypotheses on fungal interactions publication-title: Ecology – volume: 41 start-page: 21 year: 2011 end-page: 38 article-title: From graphs to spatial graphs publication-title: Annual Review of Ecology, Evolution, and Systematics – volume: 89 start-page: 1573 year: 2008 end-page: 1582 article-title: Temporal dynamics in a pollination network publication-title: Ecology – volume: 20 start-page: 2333 year: 2011 end-page: 2345 article-title: How functional is functional? Ecological groupings in terrestrial animal ecology: towards an animal functional type approach publication-title: Biodiversity and Conservation – volume: 21 start-page: 178 year: 2006 end-page: 185 article-title: Rebuilding community ecology from functional traits publication-title: Trends in Ecology and Evolution – volume: 36 start-page: 419 year: 2005 end-page: 444 article-title: Measurement of interaction strength in nature publication-title: Annual Review of Ecology, Evolution, and Systematics – volume: 104 start-page: 19,891 year: 2007 end-page: 19,896 article-title: The modularity of pollination networks publication-title: Proceedings of the National Academy of Sciences of the USA – volume: 25 start-page: 325 year: 2010 end-page: 331 article-title: A framework for community interactions under climate change publication-title: Trends in Ecology and Evolution – volume: 12 start-page: 361 year: 2003 end-page: 371 article-title: Predicting the impacts of climate change on the distribution of species: are bioclimate envelope models useful? publication-title: Global Ecology and Biogeography – volume: 193 start-page: 675 year: 2006 end-page: 690 article-title: Constrain to perform: regularization of habitat models publication-title: Ecological Modelling – volume: 4 start-page: 148 year: 2007 end-page: 159 article-title: Weighting, scale dependence and indirect effects in ecological networks: a comparative study publication-title: Ecological Complexity – year: 2006 – volume: 365 start-page: 2035 year: 2010 end-page: 2045 article-title: Woody plants and the prediction of climate‐change impacts on bird diversity publication-title: Philosophical Transactions of the Royal Society B: Biological Sciences – volume: 20 start-page: 2131 year: 2010 end-page: 2147 article-title: Spatiotemporal exploratory models for broad‐scale survey data publication-title: Ecological Applications – volume: 86 start-page: 405 year: 1998 end-page: 420 article-title: Can comparative approaches based on plant ecophysiological traits predict the nature of biotic interactions and individual plant species effects in ecosystems? publication-title: Journal of Ecology – volume: 274 start-page: 799 year: 2007 end-page: 808 article-title: Food plant diversity as broad‐scale determinant of avian frugivore richness publication-title: Proceedings of the Royal Society B: Biological Sciences – volume: 29 start-page: 129 year: 2006 end-page: 151 article-title: Novel methods improve prediction of species’ distributions from occurrence data publication-title: Ecography – volume: 23 start-page: 638 year: 2008 end-page: 647 article-title: Mechanisms of long‐distance seed dispersal publication-title: Trends in Ecology and Evolution – volume: 89 start-page: 3472 year: 2008 end-page: 3479 article-title: Climate change can cause spatial mismatch of trophically interacting species publication-title: Ecology – start-page: 321 year: 1994 end-page: 342 – volume: 442 start-page: 259 year: 2006 end-page: 264 article-title: Ecological networks and their fragility publication-title: Nature – volume: 199 start-page: 409 year: 2006 end-page: 420 article-title: TreeMig: a forest‐landscape model for simulating spatio‐temporal patterns from stand to landscape scale publication-title: Ecological Modelling – ident: e_1_2_8_34_1 doi: 10.1101/SQB.1957.022.01.039 – ident: e_1_2_8_17_1 doi: 10.1146/annurev.ecolsys.34.011802.132342 – ident: e_1_2_8_32_1 doi: 10.1073/pnas.0905137106 – ident: e_1_2_8_6_1 doi: 10.1126/science.1123412 – ident: e_1_2_8_41_1 doi: 10.1098/rspb.2009.0693 – volume-title: Geographically weighted regression: the analysis of spatially varying relationships year: 2002 ident: e_1_2_8_22_1 contributor: fullname: Fotheringham A.S. – volume-title: Plant functional types: their relevance to ecosystem properties and global change year: 1997 ident: e_1_2_8_89_1 contributor: fullname: Smith T.M. – ident: e_1_2_8_23_1 doi: 10.2307/1426632 – ident: e_1_2_8_53_1 doi: 10.1111/j.1600-0587.2010.06229.x – ident: e_1_2_8_8_1 doi: 10.1007/s10531-011-9995-1 – ident: e_1_2_8_38_1 doi: 10.1098/rspb.2006.0311 – year: 2011 ident: e_1_2_8_55_1 article-title: Climate, competition and connectivity affect future migration and ranges of European trees publication-title: Global Ecology and Biogeography contributor: fullname: Meier E.S. – ident: e_1_2_8_92_1 doi: 10.1111/j.1365-2486.2008.01557.x – ident: e_1_2_8_66_1 doi: 10.1098/rspb.2010.1371 – ident: e_1_2_8_70_1 doi: 10.1046/j.1466-822X.2003.00042.x – ident: e_1_2_8_3_1 doi: 10.1111/j.1466-8238.2007.00359.x – ident: e_1_2_8_25_1 doi: 10.1038/414716a – ident: e_1_2_8_52_1 doi: 10.1111/j.1365-2745.2004.00838.x – ident: e_1_2_8_19_1 doi: 10.1890/09-1340.1 – ident: e_1_2_8_57_1 doi: 10.1111/j.0021-8790.2004.00833.x – ident: e_1_2_8_63_1 doi: 10.1214/09-BA403 – ident: e_1_2_8_30_1 doi: 10.1890/02-0344 – ident: e_1_2_8_101_1 doi: 10.1016/j.tree.2005.04.005 – ident: e_1_2_8_72_1 doi: 10.1111/j.1365-2656.2010.01699.x – volume-title: The North American breeding bird survey, results and analysis 1966–2007, version 10.13.2007 year: 2007 ident: e_1_2_8_79_1 contributor: fullname: Sauer J.R. – ident: e_1_2_8_50_1 doi: 10.1201/b16974 – volume-title: Measurement error and misclassification in statistics and epidemiology year: 2004 ident: e_1_2_8_28_1 contributor: fullname: Gustafson P. – ident: e_1_2_8_68_1 doi: 10.1890/10-0173.1 – ident: e_1_2_8_76_1 doi: 10.1016/j.ecolmodel.2005.10.003 – ident: e_1_2_8_81_1 doi: 10.1890/10-1086.1 – ident: e_1_2_8_14_1 doi: 10.1146/annurev-ecolsys-102209-144718 – ident: e_1_2_8_91_1 doi: 10.1093/aob/mcp027 – ident: e_1_2_8_20_1 doi: 10.1111/j.2041-210X.2010.00060.x – ident: e_1_2_8_43_1 doi: 10.1111/j.1461-0248.2008.01270.x – ident: e_1_2_8_61_1 doi: 10.1016/j.tree.2008.08.003 – ident: e_1_2_8_12_1 doi: 10.7208/chicago/9780226101811.001.0001 – ident: e_1_2_8_31_1 doi: 10.1111/j.1466-8238.2010.00613.x – ident: e_1_2_8_86_1 doi: 10.1016/j.ecocom.2007.05.002 – ident: e_1_2_8_65_1 doi: 10.1890/07-0451.1 – ident: e_1_2_8_99_1 doi: 10.1371/journal.pone.0012092 – ident: e_1_2_8_80_1 doi: 10.1146/annurev.ecolsys.39.110707.173430 – ident: e_1_2_8_95_1 doi: 10.1111/j.1461-0248.2005.00810.x – ident: e_1_2_8_45_1 doi: 10.1890/0012-9658(2001)082[2560:CIBTSI]2.0.CO;2 – ident: e_1_2_8_58_1 doi: 10.1890/08-0657.1 – ident: e_1_2_8_67_1 doi: 10.1093/aob/mcp031 – ident: e_1_2_8_40_1 doi: 10.1111/j.1466-8238.2011.00679.x – ident: e_1_2_8_75_1 doi: 10.1111/j.1365-2486.2008.01671.x – ident: e_1_2_8_59_1 doi: 10.1098/rspb.2009.0523 – ident: e_1_2_8_102_1 doi: 10.1146/annurev.ecolsys.36.091704.175535 – ident: e_1_2_8_62_1 doi: 10.1111/j.1096-3642.1935.tb01680.x – ident: e_1_2_8_56_1 doi: 10.1034/j.1600-0706.2003.12031.x – ident: e_1_2_8_2_1 doi: 10.1146/annurev.ecolsys.39.110707.173434 – ident: e_1_2_8_90_1 doi: 10.1515/9781400842933 – ident: e_1_2_8_24_1 doi: 10.1016/j.tree.2010.03.002 – ident: e_1_2_8_71_1 doi: 10.1111/j.1461-0248.2008.01170.x – ident: e_1_2_8_46_1 doi: 10.1016/j.ecolmodel.2005.11.046 – ident: e_1_2_8_77_1 doi: 10.2307/3237001 – ident: e_1_2_8_97_1 doi: 10.1046/j.1365-2745.1998.00268.x – ident: e_1_2_8_5_1 doi: 10.1146/annurev.ecolsys.38.091206.095818 – volume-title: Geographical ecology year: 1972 ident: e_1_2_8_48_1 contributor: fullname: MacArthur R.H. – ident: e_1_2_8_15_1 doi: 10.1146/annurev.ecolsys.110308.120159 – ident: e_1_2_8_9_1 doi: 10.1890/03-9000 – ident: e_1_2_8_10_1 doi: 10.1023/A:1012525626267 – volume: 38 start-page: 204 year: 2009 ident: e_1_2_8_83_1 article-title: Long‐distance seed dispersal publication-title: Annual Plant Reviews contributor: fullname: Schurr F.M. – ident: e_1_2_8_87_1 doi: 10.1098/rspb.2010.0244 – start-page: 409 volume-title: Animal ecology year: 1926 ident: e_1_2_8_96_1 contributor: fullname: Volterra V. – volume-title: The principles of pollination biology year: 1979 ident: e_1_2_8_18_1 contributor: fullname: Faegri K. – volume-title: Elements of physical biology year: 1925 ident: e_1_2_8_47_1 contributor: fullname: Lotka A.J. – ident: e_1_2_8_13_1 doi: 10.1093/biomet/85.2.347 – ident: e_1_2_8_54_1 doi: 10.1111/j.1365-2699.2010.02405.x – year: 2011 ident: e_1_2_8_69_1 article-title: Forecasting species ranges by statistical estimation of ecological niches and spatial population dynamics publication-title: Global Ecology and Biogeography contributor: fullname: Pagel J. – ident: e_1_2_8_84_1 doi: 10.1890/07-1748.1 – ident: e_1_2_8_27_1 doi: 10.1111/j.1461-0248.2005.00792.x – ident: e_1_2_8_49_1 doi: 10.1086/282505 – ident: e_1_2_8_21_1 doi: 10.1214/09-AOAS250 – ident: e_1_2_8_85_1 doi: 10.1111/j.1466-8238.2010.00607.x – ident: e_1_2_8_4_1 doi: 10.1111/j.1467-9868.2008.00663.x – ident: e_1_2_8_39_1 doi: 10.1098/rstb.2010.0008 – ident: e_1_2_8_7_1 doi: 10.1111/j.0021-8790.2004.00833.x – ident: e_1_2_8_64_1 doi: 10.1073/pnas.0706375104 – ident: e_1_2_8_26_1 doi: 10.1890/09-1175.1 – ident: e_1_2_8_82_1 doi: 10.1111/j.1466-8238.2006.00293.x – volume-title: An introduction to population biology year: 1978 ident: e_1_2_8_35_1 contributor: fullname: Hutchinson G.E. – ident: e_1_2_8_44_1 doi: 10.1046/j.1365-2435.2002.00664.x – ident: e_1_2_8_42_1 doi: 10.1111/j.1600-0587.2010.05892.x – ident: e_1_2_8_94_1 doi: 10.1111/j.1461-0248.2008.01250.x – ident: e_1_2_8_29_1 doi: 10.1111/j.1466-8238.2007.00345.x – ident: e_1_2_8_33_1 doi: 10.1890/10-0602.1 – ident: e_1_2_8_74_1 doi: 10.1007/978-3-540-32730-1_15 – start-page: 321 volume-title: Long‐term experiments in agricultural and ecological sciences year: 1994 ident: e_1_2_8_100_1 contributor: fullname: Woiwod I.P. – ident: e_1_2_8_93_1 doi: 10.1111/j.2517-6161.1996.tb02080.x – ident: e_1_2_8_36_1 doi: 10.1111/j.1365-2656.2008.01460.x – ident: e_1_2_8_88_1 doi: 10.1146/annurev.es.22.110191.000555 – ident: e_1_2_8_60_1 doi: 10.1111/j.1365-2656.2010.01743.x – ident: e_1_2_8_37_1 doi: 10.1126/science.1065973 – ident: e_1_2_8_16_1 doi: 10.1111/j.2006.0906-7590.04596.x – ident: e_1_2_8_73_1 doi: 10.2307/2845499 – ident: e_1_2_8_78_1 doi: 10.1007/s10021-008-9166-8 – volume-title: Model selection and multimodel inference: a practical information‐theoretic approach year: 2002 ident: e_1_2_8_11_1 contributor: fullname: Burnham K.P. – ident: e_1_2_8_98_1 doi: 10.1038/35004572 – volume-title: Stability and complexity in model ecosystems year: 1973 ident: e_1_2_8_51_1 contributor: fullname: May R.M. |
SSID | ssj0009534 |
Score | 2.5664644 |
Snippet | Aim: Biotic interactions — within guilds or across trophic levels — have widely been ignored in species distribution models (SDMs). This synthesis outlines the... Aim Biotic interactions – within guilds or across trophic levels – have widely been ignored in species distribution models (SDMs). This synthesis outlines the... Abstract Aim Biotic interactions – within guilds or across trophic levels – have widely been ignored in species distribution models (SDMs). This synthesis... Aim Biotic interactions - within guilds or across trophic levels - have widely been ignored in species distribution models (SDMs). This synthesis outlines the... |
SourceID | proquest crossref wiley jstor istex |
SourceType | Aggregation Database Publisher |
StartPage | 2163 |
SubjectTerms | Animal ecology Community ecology Ecological modeling ecological networks Ecology global change guild assembly Modeling multidimensional complexity niche theory Plant interaction Plants prediction Spatial models Species species distribution model species interactions Studies Synecology trait-based community modules Trophic relationships |
Title | Towards novel approaches to modelling biotic interactions in multispecies assemblages at large spatial extents |
URI | https://api.istex.fr/ark:/67375/WNG-07MT5173-K/fulltext.pdf https://www.jstor.org/stable/23354520 https://onlinelibrary.wiley.com/doi/abs/10.1111%2Fj.1365-2699.2011.02663.x https://www.proquest.com/docview/1171485390/abstract/ https://search.proquest.com/docview/1257747705 |
Volume | 39 |
hasFullText | 1 |
inHoldings | 1 |
isFullTextHit | |
isPrint | |
link | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV1Nb9QwELVQKwQXvqsGWmQkxC0rbxzb8ZFWLaWoPaCt6M2yE0egXZJqk0Utv54ZO1ntIg4IcXM-HCUTj_0mefOGkLesdNlUiizltiogQClk6kpl07y2mnFeFqzCROGLS3l2lZ9fi-uB_4S5MFEfYv3BDT0jzNfo4NZ1204eGVpaD0qcsNbwCeJJ1NVDfPQ529Df5VFJCrlqmWLbpJ4_XmhrpdpFo9-OpMUtOLoJasOqdPqYzMfniWSU-WTVu0n58zepx__zwE_IowG80vdxtD0l93zzjNyP5SzvoHVSDq0HQ231r3fPSTMLzNyONu0Pv6CjiLnvaN_SUIgHM-Kp-9bCVSnKVyxjskUHGzQQHjEdFCJ6CkDff3cLmAKh3dMFsthph5xwuKvwOb_vXpCr05PZ8Vk6VHlIS8xqTVHCLHcKkIeuVWktc5Z771RVVELqynvpubeA-gpec4Ebuqp9oZ2fyhp28j2y07SN3ydUKgt4q2SqliqvmS4qKwV3FvoUVV7ohEzHN2puopiH2QiCwLoGrWvQuiZY19wm5F149esOdjlHMpwS5svlB8PUxUxMFTefErIXxsb6xIxzLODOEnIwDhYzTBAdaqxDICq4hsNv1ofBtfF_jW18u4JzYDqFaE8xkRAZRsZf37U5P_qIrZf_2vEVeQi7s0jcOSA7_XLlDwF-9e51cKxfZE8gSA |
link.rule.ids | 315,786,790,1382,27957,27958,46329,46753 |
linkProvider | Wiley-Blackwell |
linkToHtml | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV1Lb9QwELZQK1QuvCtSChgJccvKG8d2fATUsn3sHtBW9GbZiaOiLkm1yaKWX8-Mk6x2EQeEuNmxHSUTjz3jfPMNIe9Y7pKxFEnMbZGBg5LJ2OXKxmlpNeM8z1iBgcLTmZxcpKeX4rJPB4SxMB0_xPrADTUjrNeo4Hggva3lHURL656KEzYbPgKDche0XwT_6kuywcDLOy4pRKslim3Dev54p629ahfFfjvAFrcM0k2zNuxLx4_IYnijDo5yPVq1bpT__I3s8T-98mPysLdf6Yduwj0h93z1lNzvMlreQeko70t7fXr1q7tnpJoHcG5Dq_qHX9CBx9w3tK1pyMWDQfHUfavhrhQZLJZdvEUDFRowjxgRCk49BVvff3cLWAWh3NIFAtlpg7BweKpwot82z8nF8dH80yTuEz3EOQa2xshiljoFxocuVW4tc5Z771SRFULqwnvpubdg-GW85AIruih9pp0fyxIu8n2yU9WVf0GoVBZMrpypUqq0ZDorrBTcWRiTFWmmIzIePqm56fg8zIYfBNI1KF2D0jVBuuY2Iu_Dt18PsMtrxMMpYb7OPhumpnMxVtycRWQ_TI51x4RzzOHOInI4zBbTrxEN0qyDLyq4hua362bQbvxlYytfr6APrKjg8CkmIiLD1PjrpzanH0-wdPCvA9-Qvcl8em7OT2ZnL8kD6JJ0OJ5DstMuV_4VWGOtex207BeEAyRq |
linkToPdf | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV1Lb9QwELZQKx4X3hWBAkZC3LLyxokdH4F26YOuENqK3iw7dgTaJak2WdTy65lxktUu4oAQNyexI2cyY88k33xDyGtW2GQssiTmxuUQoOQitoU0cVoaxTgvcuYwUfhsKo7O05OL7KLHP2EuTMcPsf7ghpYR1ms08EtXbht5h9BSqmfihL2Gj8Cf3E0FT1DDDz4nGwS8vKOSQrBaItk2quePd9raqnZR6lcDanHLH930asO2NLlH5sMDdWiU-WjV2lHx8zeux__zxPfJ3d57pW87dXtAbvjqIbnZ1bO8htZh0bdu98XVv14_ItUsQHMbWtU__IIOLOa-oW1NQyUeTImn9lsNd6XIX7Hssi0aOKAB8Yj5oBDSU_D0_Xe7gDUQ2i1dIIydNggKh1mF7_lt85icTw5n74_ivsxDXGBaa4wcZqmV4HqoUhbGMGu491a63GVCOe-F596A25fzkmd4oFzpc2X9WJRwku-Rnaqu_BNChTTgcBVMlkKmJVO5MyLj1sCY3KW5ish4eKP6smPz0BtREEhXo3Q1SlcH6eqriLwJr349wCzniIaTmf4y_aCZPJtlY8n1aUT2gm6sOyacYwV3FpH9QVl0v0I0SLIOkWjGFVx-tb4Mto0_bEzl6xX0gfUUwj3JsoiIoBl_PWt98u4YW0__deBLcuvTwUR_PJ6ePiN3oEfSgXj2yU67XPnn4Iq19kWwsV_nsiMZ |
openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Towards+novel+approaches+to+modelling+biotic+interactions+in+multispecies+assemblages+at+large+spatial+extents&rft.jtitle=Journal+of+biogeography&rft.au=Kissling%2C+W.+D.&rft.au=Dormann%2C+Carsten+F.&rft.au=Groeneveld%2C+J%C3%BCrgen&rft.au=Hickler%2C+Thomas&rft.date=2012-12-01&rft.pub=Blackwell+Publishing+Ltd&rft.issn=0305-0270&rft.eissn=1365-2699&rft.volume=39&rft.issue=12&rft.spage=2163&rft.epage=2178&rft_id=info:doi/10.1111%2Fj.1365-2699.2011.02663.x&rft.externalDBID=10.1111%252Fj.1365-2699.2011.02663.x&rft.externalDocID=JBI2663 |
thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=0305-0270&client=summon |
thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=0305-0270&client=summon |
thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=0305-0270&client=summon |