Ecological niche model comparison under different climate scenarios: a case study of Olea spp. in Asia
Ecological niche modeling (and the related species distribution modeling) has been used as a tool with which to assess potential impacts of climate change processes on geographic distributions of species. However, the factors introducing variation into niche modeling outcomes are not well understood...
Saved in:
Published in | Ecosphere (Washington, D.C) Vol. 8; no. 5 |
---|---|
Main Authors | , , , , , , |
Format | Journal Article |
Language | English |
Published |
Washington
John Wiley & Sons, Inc
01.05.2017
|
Subjects | |
Online Access | Get full text |
ISSN | 2150-8925 2150-8925 |
DOI | 10.1002/ecs2.1825 |
Cover
Loading…
Abstract | Ecological niche modeling (and the related species distribution modeling) has been used as a tool with which to assess potential impacts of climate change processes on geographic distributions of species. However, the factors introducing variation into niche modeling outcomes are not well understood: To this end, we used seven algorithms to develop models (Maxent, GARP, BIOCLIM, artificial neural networks, support‐vector machines, climate envelope, and environmental distance) to estimate the potential geographic distribution of olives (Olea europaea sensu lato, including Olea ferruginea) under two climatic data sets (current 2000 and future 2050). Five general circulation models and two representative concentration pathway scenarios were used as predictor variables in future projections of the geographic potential of this species; models were fit at global extents (10′ spatial resolution) but transferred and interpreted for a region of particular interest in Central Asia, which largely avoids problems with truncation of niche estimates. We found marked differences among approaches in predicted distributions and model performance, as well as in the future distributional pattern reconstructed, from one algorithm to another. These general approaches, when model‐to‐model variation is managed appropriately, appear promising in predicting the potential geographic distribution of O. europaea sensu lato and thus can be an effective tool in restoration and conservation planning for wild populations, as well as possible commercial plantations of this species. |
---|---|
AbstractList | Ecological niche modeling (and the related species distribution modeling) has been used as a tool with which to assess potential impacts of climate change processes on geographic distributions of species. However, the factors introducing variation into niche modeling outcomes are not well understood: To this end, we used seven algorithms to develop models (Maxent, GARP, BIOCLIM, artificial neural networks, support‐vector machines, climate envelope, and environmental distance) to estimate the potential geographic distribution of olives (Olea europaea sensu lato, including Olea ferruginea) under two climatic data sets (current 2000 and future 2050). Five general circulation models and two representative concentration pathway scenarios were used as predictor variables in future projections of the geographic potential of this species; models were fit at global extents (10′ spatial resolution) but transferred and interpreted for a region of particular interest in Central Asia, which largely avoids problems with truncation of niche estimates. We found marked differences among approaches in predicted distributions and model performance, as well as in the future distributional pattern reconstructed, from one algorithm to another. These general approaches, when model‐to‐model variation is managed appropriately, appear promising in predicting the potential geographic distribution of O. europaea sensu lato and thus can be an effective tool in restoration and conservation planning for wild populations, as well as possible commercial plantations of this species. Ecological niche modeling (and the related species distribution modeling) has been used as a tool with which to assess potential impacts of climate change processes on geographic distributions of species. However, the factors introducing variation into niche modeling outcomes are not well understood: To this end, we used seven algorithms to develop models (Maxent, GARP, BIOCLIM, artificial neural networks, support‐vector machines, climate envelope, and environmental distance) to estimate the potential geographic distribution of olives (Olea europaea sensu lato, including Olea ferruginea) under two climatic data sets (current 2000 and future 2050). Five general circulation models and two representative concentration pathway scenarios were used as predictor variables in future projections of the geographic potential of this species; models were fit at global extents (10′ spatial resolution) but transferred and interpreted for a region of particular interest in Central Asia, which largely avoids problems with truncation of niche estimates. We found marked differences among approaches in predicted distributions and model performance, as well as in the future distributional pattern reconstructed, from one algorithm to another. These general approaches, when model‐to‐model variation is managed appropriately, appear promising in predicting the potential geographic distribution of O. europaea sensu lato and thus can be an effective tool in restoration and conservation planning for wild populations, as well as possible commercial plantations of this species. Ecological niche modeling (and the related species distribution modeling) has been used as a tool with which to assess potential impacts of climate change processes on geographic distributions of species. However, the factors introducing variation into niche modeling outcomes are not well understood: To this end, we used seven algorithms to develop models (Maxent, GARP , BIOCLIM , artificial neural networks, support‐vector machines, climate envelope, and environmental distance) to estimate the potential geographic distribution of olives ( Olea europaea sensu lato, including Olea ferruginea ) under two climatic data sets (current 2000 and future 2050). Five general circulation models and two representative concentration pathway scenarios were used as predictor variables in future projections of the geographic potential of this species; models were fit at global extents (10′ spatial resolution) but transferred and interpreted for a region of particular interest in Central Asia, which largely avoids problems with truncation of niche estimates. We found marked differences among approaches in predicted distributions and model performance, as well as in the future distributional pattern reconstructed, from one algorithm to another. These general approaches, when model‐to‐model variation is managed appropriately, appear promising in predicting the potential geographic distribution of O. europaea sensu lato and thus can be an effective tool in restoration and conservation planning for wild populations, as well as possible commercial plantations of this species. |
Author | Chaudhry, Muhammad Nawaz Saqib, Zafeer Peterson, A. Townsend Ashraf, Irfan Ashraf, Uzma Ali, Hassan Rashid Ahmad, Sajid |
Author_xml | – sequence: 1 givenname: Uzma surname: Ashraf fullname: Ashraf, Uzma organization: Punjab University – sequence: 2 givenname: A. Townsend surname: Peterson fullname: Peterson, A. Townsend email: town@ku.edu organization: The University of Kansas – sequence: 3 givenname: Muhammad Nawaz surname: Chaudhry fullname: Chaudhry, Muhammad Nawaz organization: Punjab University – sequence: 4 givenname: Irfan surname: Ashraf fullname: Ashraf, Irfan organization: Lahore Development Authority – sequence: 5 givenname: Zafeer surname: Saqib fullname: Saqib, Zafeer organization: International Islamic University – sequence: 6 givenname: Sajid surname: Rashid Ahmad fullname: Rashid Ahmad, Sajid organization: Punjab University – sequence: 7 givenname: Hassan surname: Ali fullname: Ali, Hassan organization: Punjab Wildlife and Parks Department |
BookMark | eNp1kE1LAzEQhoNUsNYe_AcBTx62zSabbuKtlPoBhR7U85JmJ5qSJmuyi_Tfu7U9iOhcZhiedwaeSzTwwQNC1zmZ5ITQKehEJ7mg_AwNac5JJiTlgx_zBRqntCV98aIUBRsis9TBhTerlcPe6nfAu1CDwzrsGhVtCh53voaIa2sMRPAt1s7uVAs4afA9EtIdVlir1G_art7jYPDagcKpaSbYejxPVl2hc6NcgvGpj9Dr_fJl8Zit1g9Pi_kq01SWPON0I4t8JmFWGC2AsQKIqUtVs1IwwqBkxGwo1KbQtGDlRmiZaw3CUMqFkTM2QjfHu00MHx2kttqGLvr-ZUWpkDKnnPOemh4pHUNKEUylbataG3wblXVVTqqDz-rgszr47BO3vxJN7C3E_Z_s6fqndbD_H6yWi2f6nfgCutqGMA |
CitedBy_id | crossref_primary_10_1007_s11355_023_00570_w crossref_primary_10_1016_j_jcz_2018_08_001 crossref_primary_10_3356_0892_1016_55_1_79 crossref_primary_10_1038_s41598_024_73055_x crossref_primary_10_1093_biolinnean_blaa056 crossref_primary_10_3390_agronomy9080442 crossref_primary_10_1007_s12517_023_11229_z crossref_primary_10_1016_j_ecoinf_2022_101675 crossref_primary_10_1007_s40808_022_01666_2 crossref_primary_10_1093_aesa_saac002 crossref_primary_10_1186_s13717_024_00511_x crossref_primary_10_1016_j_gecco_2024_e02853 crossref_primary_10_1016_j_rsase_2024_101273 crossref_primary_10_1016_j_ecoinf_2021_101396 crossref_primary_10_3390_d14100813 crossref_primary_10_1007_s41060_024_00517_w crossref_primary_10_1111_jbi_14796 crossref_primary_10_7717_peerj_13847 crossref_primary_10_1007_s10841_023_00487_7 crossref_primary_10_1016_j_sjbs_2022_103500 crossref_primary_10_1007_s42991_023_00377_0 crossref_primary_10_3389_fmars_2017_00288 crossref_primary_10_1371_journal_pone_0237701 crossref_primary_10_3389_fcosc_2024_1426488 crossref_primary_10_3390_f15060988 crossref_primary_10_1038_s41558_024_01941_3 crossref_primary_10_1111_2041_210X_13124 crossref_primary_10_1007_s10493_022_00760_5 crossref_primary_10_1016_j_agsy_2022_103429 crossref_primary_10_1371_journal_pone_0240225 crossref_primary_10_3390_f12081126 crossref_primary_10_1002_ecs2_3714 crossref_primary_10_3390_genes11080916 crossref_primary_10_1111_afe_12599 crossref_primary_10_3389_ffgc_2022_977691 crossref_primary_10_1016_j_ecolind_2021_108339 crossref_primary_10_1093_jisesa_ieaa035 crossref_primary_10_1111_ele_13453 crossref_primary_10_1007_s10661_025_13756_6 crossref_primary_10_15446_abc_v27n3_93485 crossref_primary_10_1111_1440_1703_12176 crossref_primary_10_1371_journal_pone_0257502 crossref_primary_10_1007_s10661_025_13734_y crossref_primary_10_1016_j_agwat_2022_107977 crossref_primary_10_1093_jee_toz252 crossref_primary_10_1093_cz_zox052 crossref_primary_10_1371_journal_pone_0189496 crossref_primary_10_1093_jme_tjz244 crossref_primary_10_22201_ib_20078706e_2019_90_2781 crossref_primary_10_3390_ijerph19116516 crossref_primary_10_3390_jmse12030445 crossref_primary_10_1007_s11629_020_6560_y crossref_primary_10_3390_agronomy12071592 crossref_primary_10_1002_ps_5183 crossref_primary_10_3800_pbr_20_46 crossref_primary_10_1111_jav_02242 crossref_primary_10_1016_j_ecoinf_2024_102644 crossref_primary_10_1007_s10336_021_01867_z crossref_primary_10_1371_journal_pone_0237208 crossref_primary_10_1371_journal_pone_0237527 crossref_primary_10_1111_aec_12867 crossref_primary_10_1002_ecm_1486 crossref_primary_10_1007_s10113_021_01751_9 crossref_primary_10_1155_2024_1382690 crossref_primary_10_1007_s10905_023_09844_5 crossref_primary_10_1111_mve_12326 crossref_primary_10_3390_f15111894 crossref_primary_10_3390_insects12090802 |
Cites_doi | 10.1111/2041-210X.12200 10.1016/j.ecolmodel.2011.02.011 10.1051/fruits/2012003 10.1016/S0304-3800(02)00056-X 10.1016/j.ecolmodel.2005.03.026 10.1111/j.1365-2664.2006.01141.x 10.1111/j.2006.0906-7590.04596.x 10.1111/j.1365-2486.2006.01116.x 10.1007/s10707-009-0090-7 10.1890/0012-9658(2002)083[2027:ENFAHT]2.0.CO;2 10.1111/ele.12348 10.1016/j.ecolmodel.2007.11.008 10.1046/j.1365-2486.2003.00666.x 10.1126/sciadv.1400071 10.1890/08-2257.1 10.1111/j.1365-2699.2006.01594.x 10.1111/j.1466-8238.2010.00646.x 10.1111/j.1472-4642.2007.00340.x 10.1007/BF00051966 10.1890/06-0539 10.1073/pnas.0705797105 10.1177/194008290800100408 10.1016/j.ecolmodel.2013.04.011 10.1146/annurev.ecolsys.110308.120159 10.1002/joc.1276 10.1016/j.ecoinf.2009.09.003 10.1016/j.envsoft.2015.05.004 10.1073/pnas.1300673111 10.1080/136588199241391 10.1111/j.2041-210X.2010.00036.x 10.1111/j.1365-2699.2006.01460.x 10.23943/princeton/9780691136868.001.0001 10.1007/s10651-012-0194-3 10.3390/su8080722 10.1126/science.1247579 10.1016/j.ecolmodel.2012.04.001 |
ContentType | Journal Article |
Copyright | 2017 Ashraf et al. 2017. This work is published under http://creativecommons.org/licenses/by/3.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. |
Copyright_xml | – notice: 2017 Ashraf et al. – notice: 2017. This work is published under http://creativecommons.org/licenses/by/3.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. |
DBID | 24P AAYXX CITATION ABUWG AEUYN AFKRA AZQEC BENPR BHPHI BKSAR CCPQU DWQXO HCIFZ PCBAR PHGZM PHGZT PIMPY PKEHL PQEST PQQKQ PQUKI |
DOI | 10.1002/ecs2.1825 |
DatabaseName | Wiley Online Library Open Access CrossRef ProQuest Central (Alumni) ProQuest One Sustainability ProQuest Central UK/Ireland ProQuest Central Essentials ProQuest Central Natural Science Collection Earth, Atmospheric & Aquatic Science Collection ProQuest One Community College ProQuest Central SciTech Premium Collection Earth, Atmospheric & Aquatic Science Database ProQuest Central Premium ProQuest One Academic Publicly Available Content Database ProQuest One Academic Middle East (New) ProQuest One Academic Eastern Edition (DO NOT USE) ProQuest One Academic ProQuest One Academic UKI Edition |
DatabaseTitle | CrossRef Publicly Available Content Database ProQuest One Academic Middle East (New) ProQuest Central Essentials ProQuest One Academic Eastern Edition Earth, Atmospheric & Aquatic Science Database ProQuest Central (Alumni Edition) SciTech Premium Collection ProQuest One Community College Earth, Atmospheric & Aquatic Science Collection ProQuest Central ProQuest One Sustainability ProQuest One Academic UKI Edition Natural Science Collection ProQuest Central Korea ProQuest Central (New) ProQuest One Academic ProQuest One Academic (New) |
DatabaseTitleList | Publicly Available Content Database CrossRef |
Database_xml | – sequence: 1 dbid: 24P name: Wiley Online Library Open Access url: https://authorservices.wiley.com/open-science/open-access/browse-journals.html sourceTypes: Publisher – sequence: 2 dbid: BENPR name: ProQuest Central url: https://www.proquest.com/central sourceTypes: Aggregation Database |
DeliveryMethod | fulltext_linktorsrc |
Discipline | Ecology |
EISSN | 2150-8925 |
EndPage | n/a |
ExternalDocumentID | 10_1002_ecs2_1825 ECS21825 |
Genre | article |
GeographicLocations | South Asia |
GeographicLocations_xml | – name: South Asia |
GroupedDBID | ..I 0R~ 1OC 24P 5VS 8FE 8FH AAHBH AAHHS ACCFJ ACCMX ACXQS ADBBV ADKYN ADZMN ADZOD AEEZP AENEX AEQDE AEUYN AFKRA AIWBW AJBDE ALMA_UNASSIGNED_HOLDINGS ALUQN AVUZU BCNDV BENPR BHPHI BKSAR CCPQU E3Z EBS ECGQY EJD FRP GROUPED_DOAJ HCIFZ IAO IEP ITC KQ8 LK5 M7R M~E OK1 P2P PCBAR PIMPY PROAC RSZ WIN AAYXX CITATION PHGZM PHGZT AAMMB ABUWG AEFGJ AGXDD AIDQK AIDYY AZQEC DWQXO PKEHL PQEST PQQKQ PQUKI |
ID | FETCH-LOGICAL-c2975-52b94169e64fc8e334e0fd7ad378303e730fb2edf4c2437b8c91cce8f2258f963 |
IEDL.DBID | BENPR |
ISSN | 2150-8925 |
IngestDate | Wed Aug 13 04:00:36 EDT 2025 Thu Apr 24 23:02:01 EDT 2025 Tue Jul 01 00:38:00 EDT 2025 Wed Jan 22 16:58:19 EST 2025 |
IsDoiOpenAccess | true |
IsOpenAccess | true |
IsPeerReviewed | true |
IsScholarly | true |
Issue | 5 |
Language | English |
License | Attribution http://creativecommons.org/licenses/by/3.0 |
LinkModel | DirectLink |
MergedId | FETCHMERGED-LOGICAL-c2975-52b94169e64fc8e334e0fd7ad378303e730fb2edf4c2437b8c91cce8f2258f963 |
Notes | ObjectType-Case Study-2 SourceType-Scholarly Journals-1 content type line 14 ObjectType-Feature-4 ObjectType-Report-1 ObjectType-Article-3 |
OpenAccessLink | https://www.proquest.com/docview/2289912555?pq-origsite=%requestingapplication% |
PQID | 2289912555 |
PQPubID | 4368365 |
PageCount | 13 |
ParticipantIDs | proquest_journals_2289912555 crossref_citationtrail_10_1002_ecs2_1825 crossref_primary_10_1002_ecs2_1825 wiley_primary_10_1002_ecs2_1825_ECS21825 |
ProviderPackageCode | CITATION AAYXX |
PublicationCentury | 2000 |
PublicationDate | May 2017 2017-05-00 20170501 |
PublicationDateYYYYMMDD | 2017-05-01 |
PublicationDate_xml | – month: 05 year: 2017 text: May 2017 |
PublicationDecade | 2010 |
PublicationPlace | Washington |
PublicationPlace_xml | – name: Washington |
PublicationTitle | Ecosphere (Washington, D.C) |
PublicationYear | 2017 |
Publisher | John Wiley & Sons, Inc |
Publisher_xml | – name: John Wiley & Sons, Inc |
References | 2015; 14 2009; 41 2009; 40 2010; 14 2000; 135 2002; 154 2011 2006; 1903 2010 2015; 72 2007; 341 2010; 201 2008; 14 2007 2006 1995 2012; 19 1993 2002; 837 2013; 263 2011; 151 1993; 2 2007; 13 2006; 433 2009; 14 2008; 2131 2011; 205 1986; 7 2015; 610 2011; 22211 2014; 11138 2008; 10528 2003; 9 1999; 132 2006; 29 2014; 57 2017 2006; 3310 2013 2005; 2515 2012; 67 2012; 237 2016; 8 2006; 123 2010; 51 2014; 1711 2014; 344 Hines D. A. (e_1_2_5_21_1) 1993 e_1_2_5_28_1 e_1_2_5_49_1 e_1_2_5_48_1 Pearson R. G. (e_1_2_5_33_1) 2007; 341 e_1_2_5_47_1 e_1_2_5_23_1 e_1_2_5_46_1 e_1_2_5_24_1 Pearson R. G. (e_1_2_5_34_1) 2006; 3310 e_1_2_5_45_1 e_1_2_5_44_1 e_1_2_5_43_1 Qiao H. (e_1_2_5_40_1) 2015; 610 Nix H. A. (e_1_2_5_29_1) 1986; 7 Hirzel A. H. (e_1_2_5_22_1) 2002; 837 e_1_2_5_42_1 e_1_2_5_20_1 e_1_2_5_41_1 e_1_2_5_15_1 e_1_2_5_38_1 e_1_2_5_14_1 e_1_2_5_39_1 e_1_2_5_17_1 e_1_2_5_36_1 e_1_2_5_9_1 e_1_2_5_16_1 e_1_2_5_37_1 e_1_2_5_8_1 e_1_2_5_11_1 e_1_2_5_7_1 e_1_2_5_10_1 e_1_2_5_35_1 e_1_2_5_6_1 e_1_2_5_13_1 e_1_2_5_32_1 e_1_2_5_5_1 e_1_2_5_12_1 e_1_2_5_4_1 Kumar S. (e_1_2_5_25_1) 2009; 14 e_1_2_5_3_1 e_1_2_5_19_1 e_1_2_5_18_1 Pearson R. G. (e_1_2_5_31_1) 2007 Ahmed M. (e_1_2_5_2_1) 2009; 41 Nasir Y. J. (e_1_2_5_27_1) 1995 e_1_2_5_30_1 Márquez R. J. G. (e_1_2_5_26_1) 2006 |
References_xml | – year: 2011 – volume: 51 start-page: 59 year: 2010 end-page: 66 article-title: Ecological niche modeling and geographical distribution of pollinator and plants: a case study of Smith, 1879 Eucerini: Apidae and species Cucurbitaceae publication-title: Ecological Informatics – volume: 344 start-page: 1247579 year: 2014 article-title: Multiple dimensions of climate change and their implications for biodiversity publication-title: Science – volume: 1903 start-page: 231 year: 2006 end-page: 259 article-title: Maximum entropy modeling of species geographic distributions publication-title: Ecological Modelling – volume: 67 start-page: 121 year: 2012 end-page: 126 article-title: Royle, Indian olive: an underutilised fruit tree crop of north‐west Himalaya publication-title: Fruit – volume: 19 start-page: 437 year: 2012 end-page: 457 article-title: Comparison of six correlative models in predictive vegetation mapping on a local scale publication-title: Environmental and Ecological Statistics – volume: 132 start-page: 143 year: 1999 end-page: 158 article-title: The GARP modelling system: problems and solutions to automated spatial prediction publication-title: International Journal of Geographical Information Science – volume: 2 start-page: 667 year: 1993 end-page: 680 article-title: DOMAIN: a flexible modelling procedure for mapping potential distributions of plants and animals publication-title: Biodiversity and Conservation – 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 – volume: 2131 start-page: 63 year: 2008 end-page: 72 article-title: Rethinking receiver operating characteristic analysis applications in ecological niche modeling publication-title: Ecological Modelling – year: 2007 – volume: 1711 start-page: 1351 year: 2014 end-page: 1364 article-title: The effects of phenotypic plasticity and local adaptation on forecasts of species range shifts under climate change publication-title: Ecology Letters – volume: 41 start-page: 2683 year: 2009 end-page: 2695 article-title: Vegetation structure of Royle forests of lower Dir district of Pakistan publication-title: Pakistan Journal of Botany – volume: 205 start-page: 766 year: 2011 end-page: 778 article-title: Intra‐specific variability and plasticity influence potential tree species distributions under climate change publication-title: Global Ecology and Biogeography – volume: 154 start-page: 289 year: 2002 end-page: 300 article-title: Species: a spatial evaluation of climate impact on the envelope of species publication-title: Ecological Modelling – volume: 151 start-page: 111 year: 2011 end-page: 135 article-title: openModeller: a generic approach to species' potential distribution modelling publication-title: GeoInformatica – volume: 2515 start-page: 1965 year: 2005 end-page: 1978 article-title: Very high resolution interpolated climate surfaces for global land areas publication-title: International Journal of Climatology – volume: 9 start-page: 1353 year: 2003 end-page: 1362 article-title: BIOMOD ‐ Optimizing predictions of species distributions and projecting potential future shifts under global change publication-title: Global Change Biology – volume: 263 start-page: 10 year: 2013 end-page: 18 article-title: Constraints on interpretation of ecological niche models by limited environmental ranges on calibration areas publication-title: Ecological Modelling – year: 2010 – volume: 341 start-page: 102 year: 2007 end-page: 117 article-title: Predicting species distributions from small numbers of occurrence records: a test case using cryptic geckos in Madagascar publication-title: Journal of Biogeography – volume: 14 start-page: 094 year: 2009 end-page: 098 article-title: Maxent modeling for predicting suitable habitat for threatened and endangered tree in New Caledonia publication-title: Journal of Ecology and Natural Science – volume: 57 start-page: 694 year: 2014 end-page: 700 article-title: SDMtoolbox: a python‐based GIS toolkit for landscape genetic, biogeographic and species distribution model analyses publication-title: Methods in Ecology and Evolution – volume: 72 start-page: 250 year: 2015 end-page: 260 article-title: Decomposition of the maximum entropy niche function–A step beyond modelling species distribution publication-title: Environmental Modelling and Software – volume: 3310 start-page: 1704 year: 2006 end-page: 1711 article-title: Model‐based uncertainty in species range prediction publication-title: Journal of Biogeography – volume: 14 start-page: e1400071 year: 2015 article-title: Twentieth century turnover of Mexican endemic avifaunas: landscape change versus climate drivers publication-title: Science Advances – volume: 135 start-page: 147 year: 2000 end-page: 186 article-title: Predictive habitat distribution models in ecology publication-title: Ecological Modelling – volume: 433 start-page: 424 year: 2006 end-page: 432 article-title: Modelling ecological niches with support vector machines publication-title: Journal of Applied Ecology – volume: 610 start-page: 126 year: 2015 end-page: 1136 article-title: No silver bullets in correlative ecological niche modelling: insights from testing among many potential algorithms for niche estimation publication-title: Methods in Ecology and Evolution – volume: 22211 start-page: 1810 year: 2011 end-page: 1819 article-title: The crucial role of the accessible area in ecological niche modeling and species distribution modeling publication-title: Ecological Modelling – volume: 837 start-page: 2027 year: 2002 end-page: 2036 article-title: Ecological‐niche factor analysis: How to compute habitat‐suitability maps without absence data? publication-title: Ecology – volume: 13 start-page: 165 year: 2007 end-page: 175 article-title: Predicting species' distributions from museum and herbarium records using multiresponse models fitted with multivariate adaptive regression splines publication-title: Diversity and Distributions – 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: 123 start-page: 450 year: 2006 end-page: 455 article-title: The distributions of a wide range of taxonomic groups are expanding polewards publication-title: Global Change Biology – volume: 7 start-page: 4 year: 1986 end-page: 15 article-title: A biogeographic analysis of Australian snakes publication-title: Atlas of Elapid Snakes of Australia – year: 2006 – volume: 8 start-page: 722 year: 2016 article-title: Predicting the potential distribution of in Pakistan incorporating climate change by using Maxent model publication-title: Sustainability – year: 1995 – volume: 201 start-page: 153 year: 2010 end-page: 163 article-title: Integrating environmental and genetic effects to predict responses of tree populations to climate publication-title: Ecological Applications – volume: 237 start-page: 11 year: 2012 end-page: 22 article-title: Variation in niche and distribution model performance: the need for a priori assessment of key causal factors publication-title: Ecological Modelling – volume: 14 start-page: 417 year: 2008 end-page: 445 article-title: Applying niche‐based models to predict endangered‐hylid potential distributions: Are neotropical protected areas effective enough? publication-title: Tropical Conservation Science – volume: 14 start-page: 330 year: 2010 end-page: 342 article-title: The art of modelling range‐shifting species publication-title: Methods in Ecology and Evolution – volume: 11138 start-page: 13739 year: 2014 end-page: 13744 article-title: Predicting species' range limits from functional traits for the tree flora of North America publication-title: Proceedings of the National Academy of Sciences of the United States of America – year: 2017 – year: 2007 article-title: Species' distribution modeling for conservation educators and practitioners publication-title: Synthesis. American Museum of Natural History – year: 1993 – volume: 10528 start-page: 9457 year: 2008 end-page: 9464 article-title: An ecosystem services framework to support both practical conservation and economic development publication-title: Proceedings of the National Academy of Sciences of the United States of America – year: 2013 – ident: e_1_2_5_6_1 doi: 10.1111/2041-210X.12200 – volume-title: Wild flowers of Pakistan year: 1995 ident: e_1_2_5_27_1 – ident: e_1_2_5_4_1 doi: 10.1016/j.ecolmodel.2011.02.011 – ident: e_1_2_5_23_1 doi: 10.1051/fruits/2012003 – ident: e_1_2_5_32_1 doi: 10.1016/S0304-3800(02)00056-X – ident: e_1_2_5_38_1 doi: 10.1016/j.ecolmodel.2005.03.026 – ident: e_1_2_5_10_1 doi: 10.1111/j.1365-2664.2006.01141.x – ident: e_1_2_5_15_1 doi: 10.1111/j.2006.0906-7590.04596.x – ident: e_1_2_5_19_1 doi: 10.1111/j.1365-2486.2006.01116.x – ident: e_1_2_5_11_1 doi: 10.1007/s10707-009-0090-7 – ident: e_1_2_5_42_1 – volume: 837 start-page: 2027 year: 2002 ident: e_1_2_5_22_1 article-title: Ecological‐niche factor analysis: How to compute habitat‐suitability maps without absence data? publication-title: Ecology doi: 10.1890/0012-9658(2002)083[2027:ENFAHT]2.0.CO;2 – ident: e_1_2_5_48_1 doi: 10.1111/ele.12348 – ident: e_1_2_5_36_1 doi: 10.1016/j.ecolmodel.2007.11.008 – ident: e_1_2_5_47_1 doi: 10.1046/j.1365-2486.2003.00666.x – volume: 14 start-page: 094 year: 2009 ident: e_1_2_5_25_1 article-title: Maxent modeling for predicting suitable habitat for threatened and endangered tree Canacomyrica monticola in New Caledonia publication-title: Journal of Ecology and Natural Science – year: 2007 ident: e_1_2_5_31_1 article-title: Species' distribution modeling for conservation educators and practitioners publication-title: Synthesis. American Museum of Natural History – ident: e_1_2_5_35_1 doi: 10.1126/sciadv.1400071 – ident: e_1_2_5_28_1 – ident: e_1_2_5_49_1 doi: 10.1890/08-2257.1 – volume: 341 start-page: 102 year: 2007 ident: e_1_2_5_33_1 article-title: Predicting species distributions from small numbers of occurrence records: a test case using cryptic geckos in Madagascar publication-title: Journal of Biogeography doi: 10.1111/j.1365-2699.2006.01594.x – ident: e_1_2_5_5_1 doi: 10.1111/j.1466-8238.2010.00646.x – ident: e_1_2_5_13_1 doi: 10.1111/j.1472-4642.2007.00340.x – ident: e_1_2_5_8_1 doi: 10.1007/BF00051966 – volume-title: Multi‐scale assessment of the potential distribution of two herpetofaunal species. International institute for geo‐information science and earth observation year: 2006 ident: e_1_2_5_26_1 – ident: e_1_2_5_18_1 doi: 10.1890/06-0539 – ident: e_1_2_5_45_1 doi: 10.1073/pnas.0705797105 – ident: e_1_2_5_7_1 doi: 10.1177/194008290800100408 – ident: e_1_2_5_30_1 doi: 10.1016/j.ecolmodel.2013.04.011 – volume: 41 start-page: 2683 year: 2009 ident: e_1_2_5_2_1 article-title: Vegetation structure of Olea ferruginea Royle forests of lower Dir district of Pakistan publication-title: Pakistan Journal of Botany – ident: e_1_2_5_14_1 doi: 10.1146/annurev.ecolsys.110308.120159 – ident: e_1_2_5_39_1 – volume: 610 start-page: 126 year: 2015 ident: e_1_2_5_40_1 article-title: No silver bullets in correlative ecological niche modelling: insights from testing among many potential algorithms for niche estimation publication-title: Methods in Ecology and Evolution – ident: e_1_2_5_20_1 doi: 10.1002/joc.1276 – ident: e_1_2_5_17_1 doi: 10.1016/j.ecoinf.2009.09.003 – ident: e_1_2_5_9_1 doi: 10.1016/j.envsoft.2015.05.004 – ident: e_1_2_5_43_1 doi: 10.1073/pnas.1300673111 – ident: e_1_2_5_44_1 doi: 10.1080/136588199241391 – ident: e_1_2_5_12_1 doi: 10.1111/j.2041-210X.2010.00036.x – volume: 3310 start-page: 1704 year: 2006 ident: e_1_2_5_34_1 article-title: Model‐based uncertainty in species range prediction publication-title: Journal of Biogeography doi: 10.1111/j.1365-2699.2006.01460.x – ident: e_1_2_5_24_1 – ident: e_1_2_5_37_1 doi: 10.23943/princeton/9780691136868.001.0001 – volume-title: Indigenous multipurpose trees of Tanzania: uses and economic benefits for people year: 1993 ident: e_1_2_5_21_1 – ident: e_1_2_5_46_1 doi: 10.1007/s10651-012-0194-3 – ident: e_1_2_5_3_1 doi: 10.3390/su8080722 – volume: 7 start-page: 4 year: 1986 ident: e_1_2_5_29_1 article-title: A biogeographic analysis of Australian elapid snakes publication-title: Atlas of Elapid Snakes of Australia – ident: e_1_2_5_16_1 doi: 10.1126/science.1247579 – ident: e_1_2_5_41_1 doi: 10.1016/j.ecolmodel.2012.04.001 |
SSID | ssj0000547843 |
Score | 2.3833103 |
Snippet | Ecological niche modeling (and the related species distribution modeling) has been used as a tool with which to assess potential impacts of climate change... |
SourceID | proquest crossref wiley |
SourceType | Aggregation Database Enrichment Source Index Database Publisher |
SubjectTerms | Algorithms Calibration Climate change Climatic data ecological niche model Environmental impact Genetic algorithms Geographical distribution Introduced species invasive species Neural networks Niches partial receiver‐operating characteristic (ROC) Precipitation species distribution model |
SummonAdditionalLinks | – databaseName: Wiley Online Library Open Access dbid: 24P link: http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwjV1LS8NAEF58IHgRn1itMoiHXqJxs5tu9CRSEcEHqOAtZF9QKGlp6sF_78ymSRUUvAUyyeHb_TLfbna-Yey070wsTIEjUHgVCRebSCX0l117IbmV0mmqHX54TO_exP27fF9iV00tTO0P0W64ETPC95oIXujqfGEa6kzFz1Ady2W2SqW1ZJzPxXO7wRKTU1U4NYdZLY5UxmXjLBTz8_bpn_loITK_S9WQa2432cZcJMJ1PapbbMmV22xtEAymP3eYr68IXijpKCeEfjZg2p6CQKVhU2i6n8zAjIYoTR2QdxOGjKtLKMBgBoPgLwtjD08jV0A1mZzBsITraljssrfbwevNXTTvlxAZqo_FNaXOUF9lLhXeKJckCL23_cImfcQ_cUhmr7mzXhiyIdTKZBfGOOWR08ojE_fYSjku3T4DZTPjdFYgo6XQIlXWonbwFzy1NtVOdVivQS03czNx6mkxymsbZJ4TwDkB3GEnbeikdtD4LajbQJ_PSVTlnBaDKMAk3u6F4fj7Bfng5oXM6OXB_0MP2TqnJB2OL3bZymz64Y5QYsz0cZhKX3WAy4o priority: 102 providerName: Wiley-Blackwell |
Title | Ecological niche model comparison under different climate scenarios: a case study of Olea spp. in Asia |
URI | https://onlinelibrary.wiley.com/doi/abs/10.1002%2Fecs2.1825 https://www.proquest.com/docview/2289912555 |
Volume | 8 |
hasFullText | 1 |
inHoldings | 1 |
isFullTextHit | |
isPrint | |
link | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwfV1LS8NAEF60IngRn1gfZRAPXmLbzW668SJVWoqgFh_gLWRfUChpNfXgv3cmTVoF9ZbDksA3mZ1v9vF9jJ11nGkJk2IEUq8C4VomUCHtsmsvJLdSOk13h-_uo8GLuH2Vr-WCW14eq6zmxGKithNDa-RNTp0BVmMpr6ZvAblG0e5qaaGxytZwClbYfK1d9-6Hj4tVlhbJVYmwkhRq8aYzOb9AUi1_FqIlu_zOUYsi099imyU7hO48nNtsxWU7bL1XKEt_7jI_fyJcIaMznFAY2YBZmAkC3Ql7h8r2ZAZmPEJO6oBEm3DIJL-EFAyWLiiEZWHi4WHsUsin0wsYZdDNR-kee-n3nm8GQWmUEBi6GIvNpI6RWMUuEt4oF4aIubed1IYdBD50mMVec2e9MKQ_qJWJ28Y45TGZlccU3Ge1bJK5AwbKxsbpOMVUlkKLSFmLpMG3eWRtpJ2qs_MKtcSUKuJkZjFO5vrHPCGAEwK4zk4XQ6dz6YzfBh1X0Cdl9uTJMtb4uSIcf78g6d08kQq9PPz_TUdsg1NFLs4qHrPa7P3DnSCfmOkGW-Vi2Ch_nUbRlX8BIx3M7Q |
linkProvider | ProQuest |
linkToHtml | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwtV1LTxRBEK7gEqMXg6IRBawYSLwMLD3dsz0khAAuWV6rUUi4jdOvZJPN7MqsMfwpfyNV81g0UW_c5tDp6VR90_VVT9dXABs9b7vS5uSBPOhI-q6NdMx_2U2QSjilvOHa4YthMriSp9fqegF-tbUwfK2y3ROrjdpNLJ-RbwvODCgaK7U__R5x1yj-u9q20KhhceZvf1LKVu6dfCT_bgpx3L88GkRNV4HIchUpZV4mJRaS-kQGq30c0wKD6-Uu7tEqY0-QD0Z4F6RlsT6jbbpjrdeBkK8D4ZXmfQSLMqZUpgOLh_3h5y_zU50uy2PJuJUw6optb0uxRSRe_Rn47tns75y4CmrHS_CsYaN4UMPnOSz44gU87ldK1rfLEOon9iMWfGcUq8Y5aOfNC5Fr0G6wbbMyQzseEQf2yCJRNGRS7mKOlkIlVkK2OAn4aexzLKfTLRwVeFCO8pdw9SAmfAWdYlL414DapdabNKetQ0kjE-0ckZSwIxLnEuP1CnxorZbZRrWcm2eMs1pvWWRs4IwNvALv50OntVTH3wattqbPmq-1zO6xRa-r3PHvCbL-0VdWvVdv_j_TO3gyuLw4z85Phmdv4algNlDdk1yFzuzmh18jLjMz6w2AEL49NGbvALZ5B5E |
linkToPdf | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwtV1LaxRBEC7iBsVL8IkxMRai4GWyk57u2R4hhJjskhhdgxrIbTL9goVlds2sSP6avy5V89goqLfc5tD0NFXVXV91V30F8HrgbSxtQRoogo6kj22kE35lN0Eq4ZTyhmuHP43TozP54Vydr8CvrhaG0yq7M7E-qN3M8h15X3BkQN5YqX5o0yJOD0d78-8Rd5Dil9aunUZjIif-6ieFb9Xu8SHp-o0Qo-G3g6Oo7TAQWa4opSjMZIRIMp_KYLVPElpscIPCJQNaceLJ_IMR3gVpmbjPaJvtWOt1oF2gA9kuzXsHVgcUFcU9WH0_HJ9-Wd7wxEyVJZOOzigWfW8rsU2AXv3pBG-Q7e_4uHZwowew1iJT3G9M6SGs-PIR3B3WrNZXjyE0X6xTLDl_FOsmOmiXjQyR69EusWu5skA7nRAe9siEUTRkVr3DAi25TaxJbXEW8PPUF1jN59s4KXG_mhRP4OxWRPgUeuWs9M8AtcusN1lBx4iSRqbaOQIsYUekzqXG63V420ktty2DOTfSmOYN97LIWcA5C3gdXi2Hzhvajr8N2uxEn7c7t8pv7Ix-V6vj3xPkw4OvzICvnv9_ppdwj2w1_3g8PtmA-4KBQZ0yuQm9xeUP_4JgzcJstfaDcHHbJnsNujALxg |
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=Ecological+niche+model+comparison+under+different+climate+scenarios%3A+a+case+study+of+Olea+spp.+in+Asia&rft.jtitle=Ecosphere+%28Washington%2C+D.C%29&rft.au=Ashraf%2C+Uzma&rft.au=A.+Townsend+Peterson&rft.au=Muhammad+Nawaz+Chaudhry&rft.au=Ashraf%2C+Irfan&rft.date=2017-05-01&rft.pub=John+Wiley+%26+Sons%2C+Inc&rft.eissn=2150-8925&rft.volume=8&rft.issue=5&rft_id=info:doi/10.1002%2Fecs2.1825&rft.externalDBID=HAS_PDF_LINK |
thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=2150-8925&client=summon |
thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=2150-8925&client=summon |
thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=2150-8925&client=summon |