SDMtoolbox 2.0: the next generation Python-based GIS toolkit for landscape genetic, biogeographic and species distribution model analyses
SDMtoolbox 2.0 is a software package for spatial studies of ecology, evolution, and genetics. The release of SDMtoolbox 2.0 allows researchers to use the most current ArcGIS software and MaxEnt software, and reduces the amount of time that would be spent developing common solutions. The central aim...
Gespeichert in:
| Veröffentlicht in: | PeerJ (San Francisco, CA) Jg. 5; S. e4095 |
|---|---|
| Hauptverfasser: | , , |
| Format: | Journal Article |
| Sprache: | Englisch |
| Veröffentlicht: |
United States
PeerJ. Ltd
05.12.2017
PeerJ Inc |
| Schlagworte: | |
| ISSN: | 2167-8359, 2167-8359 |
| Online-Zugang: | Volltext |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| Abstract | SDMtoolbox 2.0 is a software package for spatial studies of ecology, evolution, and genetics. The release of SDMtoolbox 2.0 allows researchers to use the most current ArcGIS software and MaxEnt software, and reduces the amount of time that would be spent developing common solutions. The central aim of this software is to automate complicated and repetitive spatial analyses in an intuitive graphical user interface. One core tenant facilitates careful parameterization of species distribution models (SDMs) to maximize each model’s discriminatory ability and minimize overfitting. This includes carefully processing of occurrence data, environmental data, and model parameterization. This program directly interfaces with MaxEnt, one of the most powerful and widely used species distribution modeling software programs, although SDMtoolbox 2.0 is not limited to species distribution modeling or restricted to modeling in MaxEnt. Many of the SDM pre- and post-processing tools have ‘universal’ analogs for use with any modeling software. The current version contains a total of 79 scripts that harness the power of ArcGIS for macroecology, landscape genetics, and evolutionary studies. For example, these tools allow for biodiversity quantification (such as species richness or corrected weighted endemism), generation of least-cost paths and corridors among shared haplotypes, assessment of the significance of spatial randomizations, and enforcement of dispersal limitations of SDMs projected into future climates—to only name a few functions contained in SDMtoolbox 2.0. Lastly, dozens of generalized tools exists for batch processing and conversion of GIS data types or formats, which are broadly useful to any ArcMap user. |
|---|---|
| AbstractList | SDMtoolbox 2.0 is a software package for spatial studies of ecology, evolution, and genetics. The release of SDMtoolbox 2.0 allows researchers to use the most current ArcGIS software and MaxEnt software, and reduces the amount of time that would be spent developing common solutions. The central aim of this software is to automate complicated and repetitive spatial analyses in an intuitive graphical user interface. One core tenant facilitates careful parameterization of species distribution models (SDMs) to maximize each model’s discriminatory ability and minimize overfitting. This includes carefully processing of occurrence data, environmental data, and model parameterization. This program directly interfaces with MaxEnt, one of the most powerful and widely used species distribution modeling software programs, although SDMtoolbox 2.0 is not limited to species distribution modeling or restricted to modeling in MaxEnt. Many of the SDM pre- and post-processing tools have ‘universal’ analogs for use with any modeling software. The current version contains a total of 79 scripts that harness the power of ArcGIS for macroecology, landscape genetics, and evolutionary studies. For example, these tools allow for biodiversity quantification (such as species richness or corrected weighted endemism), generation of least-cost paths and corridors among shared haplotypes, assessment of the significance of spatial randomizations, and enforcement of dispersal limitations of SDMs projected into future climates—to only name a few functions contained in SDMtoolbox 2.0. Lastly, dozens of generalized tools exists for batch processing and conversion of GIS data types or formats, which are broadly useful to any ArcMap user. SDMtoolbox 2.0 is a software package for spatial studies of ecology, evolution, and genetics. The release of SDMtoolbox 2.0 allows researchers to use the most current ArcGIS software and MaxEnt software, and reduces the amount of time that would be spent developing common solutions. The central aim of this software is to automate complicated and repetitive spatial analyses in an intuitive graphical user interface. One core tenant facilitates careful parameterization of species distribution models (SDMs) to maximize each model's discriminatory ability and minimize overfitting. This includes carefully processing of occurrence data, environmental data, and model parameterization. This program directly interfaces with MaxEnt, one of the most powerful and widely used species distribution modeling software programs, although SDMtoolbox 2.0 is not limited to species distribution modeling or restricted to modeling in MaxEnt. Many of the SDM pre- and post-processing tools have 'universal' analogs for use with any modeling software. The current version contains a total of 79 scripts that harness the power of ArcGIS for macroecology, landscape genetics, and evolutionary studies. For example, these tools allow for biodiversity quantification (such as species richness or corrected weighted endemism), generation of least-cost paths and corridors among shared haplotypes, assessment of the significance of spatial randomizations, and enforcement of dispersal limitations of SDMs projected into future climates-to only name a few functions contained in SDMtoolbox 2.0. Lastly, dozens of generalized tools exists for batch processing and conversion of GIS data types or formats, which are broadly useful to any ArcMap user.SDMtoolbox 2.0 is a software package for spatial studies of ecology, evolution, and genetics. The release of SDMtoolbox 2.0 allows researchers to use the most current ArcGIS software and MaxEnt software, and reduces the amount of time that would be spent developing common solutions. The central aim of this software is to automate complicated and repetitive spatial analyses in an intuitive graphical user interface. One core tenant facilitates careful parameterization of species distribution models (SDMs) to maximize each model's discriminatory ability and minimize overfitting. This includes carefully processing of occurrence data, environmental data, and model parameterization. This program directly interfaces with MaxEnt, one of the most powerful and widely used species distribution modeling software programs, although SDMtoolbox 2.0 is not limited to species distribution modeling or restricted to modeling in MaxEnt. Many of the SDM pre- and post-processing tools have 'universal' analogs for use with any modeling software. The current version contains a total of 79 scripts that harness the power of ArcGIS for macroecology, landscape genetics, and evolutionary studies. For example, these tools allow for biodiversity quantification (such as species richness or corrected weighted endemism), generation of least-cost paths and corridors among shared haplotypes, assessment of the significance of spatial randomizations, and enforcement of dispersal limitations of SDMs projected into future climates-to only name a few functions contained in SDMtoolbox 2.0. Lastly, dozens of generalized tools exists for batch processing and conversion of GIS data types or formats, which are broadly useful to any ArcMap user. |
| ArticleNumber | e4095 |
| Audience | Academic |
| Author | Bennett, Joseph R. French, Connor M. Brown, Jason L. |
| Author_xml | – sequence: 1 givenname: Jason L. surname: Brown fullname: Brown, Jason L. organization: Department of Zoology, Cooperative Wildlife Research Laboratory, Southern Illinois University at Carbondale, Carbondale, IL, USA – sequence: 2 givenname: Joseph R. surname: Bennett fullname: Bennett, Joseph R. organization: Department of Zoology, Cooperative Wildlife Research Laboratory, Southern Illinois University at Carbondale, Carbondale, IL, USA – sequence: 3 givenname: Connor M. surname: French fullname: French, Connor M. organization: Department of Zoology, Cooperative Wildlife Research Laboratory, Southern Illinois University at Carbondale, Carbondale, IL, USA |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/29230356$$D View this record in MEDLINE/PubMed |
| BookMark | eNptktFu0zAUhiM0xMbYDQ-ALCEhhGhx7DiOuUCaBoxKQyANri3HPklc0jizXbQ-Am-Nm46pRYsvYvl8_69z7P9pdjS4AbLseY7nnOf83Qjgl_MCC_YoOyF5yWcVZeJob3-cnYWwxOmrSIkr-iQ7JoJQTFl5kv25_vg1OtfX7haROX6PYgdogNuIWhjAq2jdgL5vYueGWa0CGHS5uEZbxS8bUeM86tVgglYjTIpo9VtUW9eCa70aO6tRqqMwgrYQkLEheluvJ9uVM9Cnsuo3AcKz7HGj-gBnd__T7OfnTz8uvsyuvl0uLs6vZpoVJM6aRjCjawWcaY0pFEYZo3WuqKh4VdXAakEaTAwTVSkqWjU1B6p0UzBtaE7oabbY-RqnlnL0dqX8Rjpl5XTgfCuVT2P0ILESpGSmqFghiqIRgnMjNOFVqTBwWiWvDzuvcV2vwGgYolf9gelhZbCdbN1vyTjJBebJ4PWdgXc3awhRrmzQ0KdLBbcOMhe8nF6OJvTlDm1Vas0OjUuOeovLc5bzgvGCbDuaP0ClZWBldYpOY9P5geDVnqAD1ccuuH56oXAIvtif9X7If1lKwJsdoL0LwUNzj-RYbrMqp6zKbVYTjP-DtY1T2lK_tn9I8hfqf-1Z |
| CitedBy_id | crossref_primary_10_1007_s00704_025_05369_3 crossref_primary_10_1016_j_scienta_2024_113533 crossref_primary_10_1007_s42991_021_00112_7 crossref_primary_10_3390_insects13100883 crossref_primary_10_1002_ece3_10606 crossref_primary_10_1002_ece3_10848 crossref_primary_10_1111_jbi_13582 crossref_primary_10_3390_su13063526 crossref_primary_10_1016_j_indcrop_2021_113959 crossref_primary_10_7717_peerj_14019 crossref_primary_10_1007_s11756_024_01854_8 crossref_primary_10_1186_s12879_021_06908_9 crossref_primary_10_1016_j_cropro_2025_107334 crossref_primary_10_3390_rs15041047 crossref_primary_10_1177_1940082919854058 crossref_primary_10_1007_s12038_025_00553_z crossref_primary_10_1016_j_ecolind_2021_107699 crossref_primary_10_1002_ece3_6700 crossref_primary_10_1111_ddi_13825 crossref_primary_10_1016_j_biocon_2022_109594 crossref_primary_10_1038_s41598_024_73248_4 crossref_primary_10_1016_j_pld_2020_09_001 crossref_primary_10_1038_s41598_023_42573_5 crossref_primary_10_3389_fevo_2021_667949 crossref_primary_10_1016_j_cropro_2024_107042 crossref_primary_10_1007_s40823_024_00104_6 crossref_primary_10_1038_s41598_025_15167_6 crossref_primary_10_1111_cobi_14310 crossref_primary_10_3390_f13071051 crossref_primary_10_3390_plants14050768 crossref_primary_10_1016_j_heliyon_2019_e03101 crossref_primary_10_3390_tropicalmed7120431 crossref_primary_10_1016_j_biocon_2025_111491 crossref_primary_10_1002_ece3_5866 crossref_primary_10_1016_j_pocean_2019_102123 crossref_primary_10_1016_j_soh_2025_100107 crossref_primary_10_3389_fpls_2025_1601956 crossref_primary_10_1016_j_hisbio_2025_100025 crossref_primary_10_1093_jmammal_gyae131 crossref_primary_10_1016_j_pld_2023_07_006 crossref_primary_10_1016_j_gecco_2024_e03122 crossref_primary_10_1007_s11629_021_6966_1 crossref_primary_10_1111_jbi_70024 crossref_primary_10_1111_2041_210X_13452 crossref_primary_10_1186_s12870_025_06466_1 crossref_primary_10_1111_aje_12908 crossref_primary_10_1002_ps_8250 crossref_primary_10_3390_rs15194685 crossref_primary_10_3724_ahr_2095_0357_2024_0061 crossref_primary_10_1088_2515_7620_ad277d crossref_primary_10_7717_peerj_13260 crossref_primary_10_1016_j_ecolind_2023_111491 crossref_primary_10_3390_ani14091390 crossref_primary_10_7717_peerj_16533 crossref_primary_10_28979_jarnas_844850 crossref_primary_10_3389_fpls_2024_1364481 crossref_primary_10_1111_1749_4877_12713 crossref_primary_10_1111_2041_210X_13107 crossref_primary_10_1016_j_isci_2025_112909 crossref_primary_10_3390_biology13110902 crossref_primary_10_3390_insects15100820 crossref_primary_10_1002_aqc_70213 crossref_primary_10_1007_s10682_024_10290_8 crossref_primary_10_1016_j_scitotenv_2021_150782 crossref_primary_10_1007_s43388_023_00124_6 crossref_primary_10_1016_j_ecolind_2024_112694 crossref_primary_10_1007_s10530_022_02838_y crossref_primary_10_1111_mec_16082 crossref_primary_10_3390_pathogens12050651 crossref_primary_10_3390_biology10111169 crossref_primary_10_1007_s11252_021_01136_0 crossref_primary_10_1080_17538947_2024_2346266 crossref_primary_10_3389_ffgc_2021_756678 crossref_primary_10_1002_ecs2_4391 crossref_primary_10_3390_rs14092168 crossref_primary_10_3390_f14051049 crossref_primary_10_1016_j_ecolmodel_2023_110409 crossref_primary_10_1016_j_sajb_2023_02_032 crossref_primary_10_7717_peerj_16111 crossref_primary_10_1186_s40850_022_00153_6 crossref_primary_10_3390_insects13100942 crossref_primary_10_3389_fpls_2025_1564278 crossref_primary_10_3389_fpls_2022_1003368 crossref_primary_10_3389_fpls_2023_1326207 crossref_primary_10_1016_j_actatropica_2019_105319 crossref_primary_10_3389_fevo_2023_1165968 crossref_primary_10_1007_s12595_023_00496_z crossref_primary_10_1016_j_gecco_2025_e03573 crossref_primary_10_1080_10106049_2024_2421233 crossref_primary_10_1111_jbi_13651 crossref_primary_10_1093_aob_mcac142 crossref_primary_10_1002_ece3_10545 crossref_primary_10_1007_s11252_018_0778_2 crossref_primary_10_1016_j_ecolind_2023_111069 crossref_primary_10_3390_ani14203025 crossref_primary_10_3390_biology14030304 crossref_primary_10_3390_app122211449 crossref_primary_10_1038_s41598_024_78733_4 crossref_primary_10_1016_j_scitotenv_2020_142416 crossref_primary_10_1007_s13127_020_00454_z crossref_primary_10_3389_fmars_2021_742209 crossref_primary_10_3390_plants12071559 crossref_primary_10_1007_s10336_020_01822_4 crossref_primary_10_1016_j_compenvurbsys_2024_102177 crossref_primary_10_3389_fpls_2024_1445764 crossref_primary_10_1080_14614103_2023_2266631 crossref_primary_10_3390_f15050883 crossref_primary_10_1111_ppa_13111 crossref_primary_10_1007_s11356_024_33391_x crossref_primary_10_2989_00306525_2021_1998239 crossref_primary_10_3390_f15050766 crossref_primary_10_1111_jbi_14841 crossref_primary_10_1111_jbi_14962 crossref_primary_10_3389_fpls_2024_1413707 crossref_primary_10_1007_s10530_024_03290_w crossref_primary_10_20935_AcadEnvSci7582 crossref_primary_10_1002_ajp_23493 crossref_primary_10_1016_j_ympev_2023_107781 crossref_primary_10_3389_fevo_2021_742524 crossref_primary_10_1016_j_scitotenv_2024_176854 crossref_primary_10_1007_s11355_023_00575_5 crossref_primary_10_1111_jbi_14828 crossref_primary_10_1093_pubmed_fdac125 crossref_primary_10_1111_oik_10283 crossref_primary_10_3389_fpls_2023_1141470 crossref_primary_10_1111_mms_12719 crossref_primary_10_1371_journal_pone_0229178 crossref_primary_10_1007_s10531_023_02749_x crossref_primary_10_1007_s10661_025_14369_9 crossref_primary_10_1016_j_ufug_2023_128183 crossref_primary_10_1007_s10530_023_03016_4 crossref_primary_10_1038_s41598_022_11600_2 crossref_primary_10_1016_j_ecolind_2019_105930 crossref_primary_10_1111_geb_12979 crossref_primary_10_1007_s10530_021_02596_3 crossref_primary_10_1002_ece3_71390 crossref_primary_10_1002_ece3_8288 crossref_primary_10_1016_j_jnc_2023_126547 crossref_primary_10_1017_S0030605321000764 crossref_primary_10_1002_jwmg_22235 crossref_primary_10_3832_ifor4196_015 crossref_primary_10_3390_land12010221 crossref_primary_10_2179_0008_7475_86_2_173 crossref_primary_10_1016_j_pce_2025_103938 crossref_primary_10_1111_aec_70085 crossref_primary_10_3389_fpls_2025_1552770 crossref_primary_10_3390_plants12183254 crossref_primary_10_1038_s41598_025_06976_w crossref_primary_10_3390_agronomy15020362 crossref_primary_10_1002_ece3_9382 crossref_primary_10_1016_j_rama_2021_06_007 crossref_primary_10_1111_jbi_13716 crossref_primary_10_1007_s10531_024_02831_y crossref_primary_10_1016_j_scitotenv_2022_160962 crossref_primary_10_3390_plants13202846 crossref_primary_10_1016_j_ecoinf_2022_101813 crossref_primary_10_1002_ece3_70169 crossref_primary_10_1016_j_ecoinf_2022_101930 crossref_primary_10_3390_f16060900 crossref_primary_10_3390_su16145975 crossref_primary_10_1002_ece3_70160 crossref_primary_10_1007_s00343_025_4358_z crossref_primary_10_1002_ps_8887 crossref_primary_10_1002_ps_7554 crossref_primary_10_1111_1462_2920_15799 crossref_primary_10_1007_s10531_024_02997_5 crossref_primary_10_3389_fenvs_2024_1429718 crossref_primary_10_1016_j_scitotenv_2021_146031 crossref_primary_10_3390_biology11040588 crossref_primary_10_1016_j_biocon_2021_109238 crossref_primary_10_1016_j_ecolind_2022_109311 crossref_primary_10_1080_21564574_2024_2337638 crossref_primary_10_3390_agriculture15111144 crossref_primary_10_3390_f15020272 crossref_primary_10_3989_pirineos_2022_177004 crossref_primary_10_7717_peerj_18799 crossref_primary_10_1002_ece3_70059 crossref_primary_10_1016_j_actatropica_2022_106382 crossref_primary_10_3390_w15112091 crossref_primary_10_1007_s10980_020_01125_2 crossref_primary_10_3390_f15081321 crossref_primary_10_1038_s41598_024_64760_8 crossref_primary_10_1186_s43088_024_00553_2 crossref_primary_10_3390_f15081449 crossref_primary_10_3390_f11060673 crossref_primary_10_3390_f13060859 crossref_primary_10_1002_jwmg_22204 crossref_primary_10_1002_jwmg_22325 crossref_primary_10_3389_fpls_2025_1561031 crossref_primary_10_1016_j_heliyon_2023_e20199 crossref_primary_10_1007_s00436_024_08284_0 crossref_primary_10_1134_S1995425520050030 crossref_primary_10_1515_mammalia_2020_0094 crossref_primary_10_3390_plants14162539 crossref_primary_10_3390_ani13233726 crossref_primary_10_1002_ps_6886 crossref_primary_10_1016_j_biocon_2024_110826 crossref_primary_10_1007_s10531_023_02727_3 crossref_primary_10_3390_insects14080714 crossref_primary_10_1002_ps_6891 crossref_primary_10_3390_f11060684 crossref_primary_10_1002_ece3_9054 crossref_primary_10_1016_j_pecon_2021_04_002 crossref_primary_10_3389_fmars_2021_776965 crossref_primary_10_3390_f12091263 crossref_primary_10_1007_s10661_025_14302_0 crossref_primary_10_1016_j_pocean_2019_04_007 crossref_primary_10_3390_f10070565 crossref_primary_10_1002_ps_7987 crossref_primary_10_1111_njb_04502 crossref_primary_10_7717_peerj_6128 crossref_primary_10_1038_s41598_023_36358_z crossref_primary_10_1111_ddi_13116 crossref_primary_10_3389_fpls_2024_1470653 crossref_primary_10_1038_s41438_020_00376_z crossref_primary_10_3390_ani15020235 crossref_primary_10_3390_plants11060731 crossref_primary_10_1002_ece3_72159 crossref_primary_10_1007_s10661_022_10524_8 crossref_primary_10_1016_j_jnc_2024_126791 crossref_primary_10_3389_fevo_2023_1218149 crossref_primary_10_1007_s11756_021_00936_1 crossref_primary_10_1002_ps_7753 crossref_primary_10_1111_maec_12706 crossref_primary_10_2989_16085914_2024_2421797 crossref_primary_10_1088_1755_1315_743_1_012027 crossref_primary_10_1002_ece3_71054 crossref_primary_10_1007_s11676_019_01009_5 crossref_primary_10_1016_j_jia_2023_06_022 crossref_primary_10_1016_j_scitotenv_2024_175794 crossref_primary_10_3390_su15065604 crossref_primary_10_1016_j_ecolind_2021_108489 crossref_primary_10_1088_1755_1315_1276_1_012054 crossref_primary_10_1093_biolinnean_blae094 crossref_primary_10_3389_fevo_2022_1095188 crossref_primary_10_3389_fpls_2025_1563127 crossref_primary_10_1007_s42995_023_00188_9 crossref_primary_10_3390_insects16020227 crossref_primary_10_1016_j_envsoft_2021_105234 crossref_primary_10_3390_insects13020145 crossref_primary_10_3390_biology11111659 crossref_primary_10_1016_j_heliyon_2024_e30273 crossref_primary_10_1002_ece3_9083 crossref_primary_10_3390_su14031638 crossref_primary_10_1111_ddi_13362 crossref_primary_10_1111_csp2_13266 crossref_primary_10_3390_d15121172 crossref_primary_10_1016_j_smallrumres_2024_107370 crossref_primary_10_1002_ece3_6492 crossref_primary_10_1007_s10531_023_02587_x crossref_primary_10_1007_s10530_020_02332_3 crossref_primary_10_1002_ece3_11010 crossref_primary_10_1002_ece3_8798 crossref_primary_10_3390_d15101038 crossref_primary_10_1016_j_ecoinf_2024_102896 crossref_primary_10_3390_agriculture14091629 crossref_primary_10_7589_JWD_D_24_00099 crossref_primary_10_3390_plants14111669 crossref_primary_10_1007_s10336_020_01828_y crossref_primary_10_3390_ijgi14010031 crossref_primary_10_1007_s10113_024_02185_9 crossref_primary_10_3390_su15065469 crossref_primary_10_1016_j_jnc_2025_127106 crossref_primary_10_1016_j_jaridenv_2025_105317 crossref_primary_10_1186_s13717_023_00423_2 crossref_primary_10_1111_1748_5967_12671 crossref_primary_10_3390_insects12030229 crossref_primary_10_1111_ddi_70058 crossref_primary_10_1016_j_scitotenv_2024_173616 crossref_primary_10_3390_ani15131907 crossref_primary_10_1007_s40808_024_01995_4 crossref_primary_10_1088_2515_7620_ad853c crossref_primary_10_3390_insects11110805 crossref_primary_10_1002_ece3_71551 crossref_primary_10_3390_su15065349 crossref_primary_10_1016_j_biocontrol_2025_105754 crossref_primary_10_3390_ijerph16183416 crossref_primary_10_1002_ece3_70235 crossref_primary_10_1111_tbed_14113 crossref_primary_10_1002_ece3_70354 crossref_primary_10_1186_s12870_024_04830_1 crossref_primary_10_1007_s42965_024_00351_y crossref_primary_10_3389_fpls_2024_1498229 crossref_primary_10_3390_f10030220 crossref_primary_10_3390_su151813669 crossref_primary_10_1007_s12145_024_01626_7 crossref_primary_10_1111_jzs_12519 crossref_primary_10_1371_journal_pone_0280922 crossref_primary_10_1111_1749_4877_13020 crossref_primary_10_1016_j_ocecoaman_2020_105328 crossref_primary_10_1016_j_heliyon_2023_e17241 crossref_primary_10_7717_peerj_19136 crossref_primary_10_1016_j_actatropica_2021_105950 crossref_primary_10_3390_insects12040299 crossref_primary_10_1016_j_actatropica_2021_105952 crossref_primary_10_1002_ece3_9302 crossref_primary_10_3390_ani15060896 crossref_primary_10_1002_ece3_9305 crossref_primary_10_3390_insects13060550 crossref_primary_10_3389_fpls_2024_1407867 crossref_primary_10_1016_j_jaridenv_2020_104153 crossref_primary_10_3356_jrr2451 crossref_primary_10_1007_s11295_023_01592_z crossref_primary_10_1007_s40808_022_01661_7 crossref_primary_10_1038_s41598_023_37897_1 crossref_primary_10_1371_journal_pone_0265316 crossref_primary_10_1038_s41598_023_33856_y crossref_primary_10_1093_ornithapp_duab006 crossref_primary_10_3390_f13101595 crossref_primary_10_1016_j_flora_2025_152827 crossref_primary_10_3390_insects14040316 crossref_primary_10_3390_insects14050476 crossref_primary_10_1002_ece3_8460 crossref_primary_10_1016_j_scitotenv_2024_172523 crossref_primary_10_1016_j_scitotenv_2021_148850 crossref_primary_10_1016_j_rse_2025_114804 crossref_primary_10_3390_genes11101114 crossref_primary_10_3390_f14102048 crossref_primary_10_1002_ece3_71530 crossref_primary_10_1016_j_aspen_2020_01_009 crossref_primary_10_3390_insects16050450 crossref_primary_10_1111_2041_210X_13902 crossref_primary_10_1676_21_00058 crossref_primary_10_3390_plants12010222 crossref_primary_10_3897_zookeys_1158_94152 crossref_primary_10_1007_s10584_020_02722_5 crossref_primary_10_1007_s13592_020_00753_6 crossref_primary_10_1002_ecs2_3870 crossref_primary_10_3390_f13091428 crossref_primary_10_3389_fmars_2020_542648 crossref_primary_10_1111_ddi_13297 crossref_primary_10_1016_j_avrs_2022_100009 crossref_primary_10_3390_agronomy15051165 crossref_primary_10_1016_j_lana_2021_100080 crossref_primary_10_1016_j_prevetmed_2021_105311 crossref_primary_10_1016_j_cell_2022_06_042 crossref_primary_10_1093_mollus_eyab003 crossref_primary_10_1111_acv_12696 crossref_primary_10_1007_s42991_021_00118_1 crossref_primary_10_3390_life15040589 crossref_primary_10_1111_1748_5967_12756 crossref_primary_10_1186_s12936_020_03187_8 crossref_primary_10_2478_foecol_2024_0020 crossref_primary_10_3389_fpls_2023_1184556 crossref_primary_10_1038_s41598_024_71782_9 crossref_primary_10_1080_03736687_2022_2126097 crossref_primary_10_1016_j_landurbplan_2020_103871 crossref_primary_10_1016_j_ocecoaman_2021_105555 crossref_primary_10_3390_d13020083 crossref_primary_10_3390_f13010126 crossref_primary_10_3390_su16093653 crossref_primary_10_1111_tbed_14669 crossref_primary_10_3390_d15070877 crossref_primary_10_1007_s11356_023_26351_4 crossref_primary_10_3390_d13060266 crossref_primary_10_1002_ece3_9210 crossref_primary_10_1016_j_jnc_2023_126505 crossref_primary_10_1111_geb_13108 crossref_primary_10_3390_d15101087 crossref_primary_10_1002_ecs2_3969 crossref_primary_10_1007_s40808_024_02005_3 crossref_primary_10_1016_j_indcrop_2022_115838 crossref_primary_10_1093_aobpla_plab009 crossref_primary_10_1016_j_biocon_2022_109711 crossref_primary_10_1016_j_heliyon_2023_e14927 crossref_primary_10_3390_f11101088 crossref_primary_10_3390_insects14100810 crossref_primary_10_3390_d13020072 crossref_primary_10_3390_plants12030473 crossref_primary_10_1002_ps_6932 crossref_primary_10_1111_geb_13103 crossref_primary_10_3390_biology12010084 crossref_primary_10_7717_peerj_12308 crossref_primary_10_1038_s41598_025_15546_z crossref_primary_10_1016_j_fcr_2021_108069 crossref_primary_10_1016_j_jnc_2020_125918 crossref_primary_10_1007_s10340_022_01479_3 crossref_primary_10_1002_inc3_70019 crossref_primary_10_3390_biology14091221 crossref_primary_10_1007_s13364_025_00815_z crossref_primary_10_1016_j_jaa_2019_101140 crossref_primary_10_1002_ece3_9228 crossref_primary_10_1093_jee_toae262 crossref_primary_10_1038_s41598_024_71816_2 crossref_primary_10_1016_j_jaridenv_2022_104725 crossref_primary_10_1007_s11756_023_01523_2 crossref_primary_10_1016_j_asr_2025_01_050 crossref_primary_10_1016_j_scitotenv_2020_139933 crossref_primary_10_3389_fpls_2019_01721 crossref_primary_10_1007_s11258_023_01312_6 crossref_primary_10_1088_2515_7620_adce5b crossref_primary_10_1111_tbed_14404 crossref_primary_10_7717_peerj_6514 crossref_primary_10_1038_s41598_018_34854_1 crossref_primary_10_1186_s13717_025_00622_z crossref_primary_10_1093_ornithapp_duab063 crossref_primary_10_1371_journal_pone_0242432 crossref_primary_10_1007_s10531_024_02802_3 crossref_primary_10_1038_s41437_024_00700_6 crossref_primary_10_1016_j_ecoinf_2021_101309 crossref_primary_10_1093_jee_toae255 crossref_primary_10_1007_s10661_018_7052_1 crossref_primary_10_1111_jse_12521 crossref_primary_10_1186_s12870_025_06590_y crossref_primary_10_1007_s10750_024_05554_x crossref_primary_10_1007_s11427_023_2448_x crossref_primary_10_2989_00306525_2022_2061063 crossref_primary_10_3389_fpls_2022_921310 crossref_primary_10_1002_ps_7804 crossref_primary_10_3354_meps13880 crossref_primary_10_3389_fpls_2021_774232 crossref_primary_10_3390_f13091504 crossref_primary_10_1038_s41598_025_09800_7 crossref_primary_10_1111_1365_2664_70153 crossref_primary_10_1007_s10661_025_14308_8 crossref_primary_10_1093_zoolinnean_zlaa030 crossref_primary_10_3390_f12111449 crossref_primary_10_3390_su142114621 crossref_primary_10_3390_biology14030242 crossref_primary_10_1016_j_ecolind_2025_113181 crossref_primary_10_1016_j_marenvres_2025_107253 crossref_primary_10_1016_j_ecoinf_2023_102402 crossref_primary_10_1016_j_ecolind_2025_114150 crossref_primary_10_3390_f13101611 crossref_primary_10_1007_s00704_023_04627_6 crossref_primary_10_1007_s11356_024_32935_5 crossref_primary_10_3389_fcosc_2025_1470223 crossref_primary_10_1038_s41598_024_52668_2 crossref_primary_10_1007_s10344_024_01780_9 crossref_primary_10_1186_s12870_024_05355_3 crossref_primary_10_1007_s12517_022_10442_6 crossref_primary_10_3390_agriculture14060850 crossref_primary_10_1002_ece3_70517 crossref_primary_10_1093_biolinnean_blaa147 crossref_primary_10_3390_ecologies4040043 crossref_primary_10_1002_ece3_70636 crossref_primary_10_1038_s41598_024_66260_1 crossref_primary_10_1002_ece3_70633 crossref_primary_10_1016_j_ecoinf_2021_101324 crossref_primary_10_1093_aesa_saz049 crossref_primary_10_1525_elementa_2023_00018 crossref_primary_10_1038_s41598_025_90564_5 crossref_primary_10_1088_2515_7620_ac3906 crossref_primary_10_3389_ffgc_2023_1250651 crossref_primary_10_1016_j_ecolmodel_2022_110039 crossref_primary_10_1016_j_flora_2020_151607 crossref_primary_10_1007_s10661_023_12122_8 crossref_primary_10_1371_journal_pone_0317368 crossref_primary_10_3390_d17060403 crossref_primary_10_1016_j_gecco_2025_e03813 crossref_primary_10_3390_f12111464 crossref_primary_10_1016_j_pld_2023_05_001 crossref_primary_10_3390_plants9080957 crossref_primary_10_1007_s11676_022_01459_4 crossref_primary_10_1007_s11692_023_09613_4 crossref_primary_10_1111_jse_12558 crossref_primary_10_1002_ece3_6317 crossref_primary_10_3390_f15101693 crossref_primary_10_1016_j_scitotenv_2024_174095 crossref_primary_10_1016_j_mambio_2019_03_014 crossref_primary_10_3390_insects15060417 crossref_primary_10_3390_su17136078 crossref_primary_10_1093_jee_toz259 crossref_primary_10_1292_jvms_23_0146 crossref_primary_10_1093_jme_tjz244 crossref_primary_10_1016_j_ecolind_2023_110219 crossref_primary_10_1007_s11629_020_6560_y crossref_primary_10_1007_s43388_023_00130_8 crossref_primary_10_3390_land13081156 crossref_primary_10_1093_jmammal_gyab133 crossref_primary_10_3389_fevo_2023_1277058 crossref_primary_10_3389_fvets_2021_678478 crossref_primary_10_1038_s41598_023_47535_5 crossref_primary_10_3390_ijms26020574 crossref_primary_10_1038_s41598_024_66490_3 crossref_primary_10_3390_insects16060642 crossref_primary_10_1002_ece3_6200 crossref_primary_10_1007_s10661_021_09547_4 crossref_primary_10_1016_j_cj_2023_11_011 crossref_primary_10_1111_jzs_12372 crossref_primary_10_1002_ece3_8629 crossref_primary_10_3390_biology12070998 crossref_primary_10_1007_s10668_020_00819_6 crossref_primary_10_1016_j_ecolind_2025_113077 crossref_primary_10_1016_j_pecon_2021_01_001 crossref_primary_10_7717_peerj_4647 crossref_primary_10_3390_land12101907 crossref_primary_10_3390_plants14182827 crossref_primary_10_1002_ps_7297 crossref_primary_10_1051_alr_2024002 crossref_primary_10_1007_s40808_022_01378_7 crossref_primary_10_1186_s13717_020_00259_0 crossref_primary_10_1007_s10661_023_12003_0 crossref_primary_10_3390_biology13030198 crossref_primary_10_1371_journal_pone_0238126 crossref_primary_10_1007_s10336_024_02214_8 crossref_primary_10_1002_ece3_8632 crossref_primary_10_1007_s10530_020_02372_9 crossref_primary_10_1515_mammalia_2021_0130 crossref_primary_10_1016_j_jenvman_2019_02_031 crossref_primary_10_3390_plants13081082 crossref_primary_10_1016_j_actatropica_2024_107367 crossref_primary_10_1016_j_ecoinf_2021_101478 crossref_primary_10_1002_ece3_71406 crossref_primary_10_1016_j_scitotenv_2023_162893 crossref_primary_10_3389_fgene_2024_1322285 crossref_primary_10_3390_ani14071124 crossref_primary_10_1093_ee_nvab001 crossref_primary_10_1007_s11356_021_17171_5 crossref_primary_10_3390_plants10030460 crossref_primary_10_7717_peerj_18932 crossref_primary_10_7717_peerj_17968 crossref_primary_10_1186_s12870_023_04284_x crossref_primary_10_1038_s41598_022_09953_9 crossref_primary_10_1002_ece3_10252 crossref_primary_10_1002_ece3_71754 crossref_primary_10_1111_ecog_06852 crossref_primary_10_3390_d14100840 crossref_primary_10_3389_fpls_2024_1430576 crossref_primary_10_3390_f13122108 crossref_primary_10_1111_jbi_14224 crossref_primary_10_1111_jbi_14345 crossref_primary_10_1111_jbi_14587 crossref_primary_10_1051_parasite_2021030 crossref_primary_10_1134_S1062359025601041 crossref_primary_10_1038_s41598_025_02231_4 crossref_primary_10_7717_peerj_12387 crossref_primary_10_1016_j_ympev_2024_108216 crossref_primary_10_1002_ece3_70528 crossref_primary_10_1111_mec_16223 crossref_primary_10_1002_ece3_70403 crossref_primary_10_1002_ece3_11594 crossref_primary_10_1002_ece3_6117 crossref_primary_10_1016_j_landurbplan_2024_105039 crossref_primary_10_1016_j_funbio_2022_10_004 crossref_primary_10_1016_j_ecoinf_2022_101693 crossref_primary_10_3389_ffgc_2023_1143208 crossref_primary_10_1007_s10661_023_12232_3 crossref_primary_10_1111_rec_14232 crossref_primary_10_1007_s10336_024_02239_z crossref_primary_10_1007_s42991_020_00056_4 crossref_primary_10_1111_plb_12925 crossref_primary_10_1016_j_heliyon_2023_e19867 crossref_primary_10_1016_j_ecolind_2023_110001 crossref_primary_10_3390_biology11010110 crossref_primary_10_1007_s10344_024_01806_2 crossref_primary_10_1007_s40415_024_00993_1 crossref_primary_10_1016_j_ecolind_2019_106009 crossref_primary_10_1002_ece3_9516 crossref_primary_10_1016_j_ecolind_2021_107950 crossref_primary_10_1371_journal_pntd_0013464 crossref_primary_10_1371_journal_pone_0320598 crossref_primary_10_3390_f14061150 crossref_primary_10_3389_fmars_2021_608867 |
| Cites_doi | 10.1111/j.1600-0587.2010.06237.x 10.1016/j.ecolmodel.2011.02.011 10.1111/j.0906-7590.2008.5203.x 10.1111/j.1471-8286.2004.00843.x 10.1111/j.2041-210X.2011.00172.x 10.1890/10-1171.1 10.1111/2041-210X.12200 10.1073/pnas.0706568104 10.1111/j.1600-0587.2013.07872.x 10.1016/j.ecolmodel.2011.04.011 10.1016/j.ecolmodel.2005.03.026 10.1111/j.1466-8238.2007.00358.x 10.23943/princeton/9780691136868.001.0001 10.1038/ncomms5473 10.1890/11-0826.1 10.1016/j.ecolmodel.2013.08.011 10.1111/j.1365-2699.2009.02174.x 10.1111/jbi.12227 10.1111/j.1365-2699.2010.02290.x 10.1016/j.ecolmodel.2013.12.012 10.1046/j.1365-2699.2003.00867.x |
| ContentType | Journal Article |
| Copyright | COPYRIGHT 2017 PeerJ. Ltd. 2017 Brown et al. 2017 Brown et al. |
| Copyright_xml | – notice: COPYRIGHT 2017 PeerJ. Ltd. – notice: 2017 Brown et al. 2017 Brown et al. |
| DBID | AAYXX CITATION NPM 7X8 5PM DOA |
| DOI | 10.7717/peerj.4095 |
| DatabaseName | CrossRef PubMed MEDLINE - Academic PubMed Central (Full Participant titles) Open Access: DOAJ - Directory of Open Access Journals |
| DatabaseTitle | CrossRef PubMed MEDLINE - Academic |
| DatabaseTitleList | CrossRef PubMed MEDLINE - Academic |
| Database_xml | – sequence: 1 dbid: DOA name: DOAJ (selected full-text) url: https://www.doaj.org/ sourceTypes: Open Website – sequence: 2 dbid: NPM name: PubMed url: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed sourceTypes: Index Database – sequence: 3 dbid: 7X8 name: MEDLINE - Academic url: https://search.proquest.com/medline sourceTypes: Aggregation Database |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Medicine |
| EISSN | 2167-8359 |
| ExternalDocumentID | oai_doaj_org_article_0a9265d4854944f9977d9c2786a0e738 PMC5721907 A517457428 29230356 10_7717_peerj_4095 |
| Genre | Journal Article |
| GroupedDBID | 53G 5VS 88I 8FE 8FH AAFWJ AAYXX ABUWG ADBBV ADRAZ AENEX AFFHD AFKRA AFPKN ALMA_UNASSIGNED_HOLDINGS AOIJS AZQEC BAWUL BBNVY BCNDV BENPR BHPHI BPHCQ CCPQU CITATION DIK DWQXO ECGQY GNUQQ GROUPED_DOAJ GX1 H13 HCIFZ HYE IAO IEA IHR IHW ITC KQ8 LK8 M2P M48 M7P M~E OK1 PHGZM PHGZT PIMPY PQGLB PQQKQ PROAC RPM W2D YAO NPM 7X8 PUEGO 5PM |
| ID | FETCH-LOGICAL-c542t-ff95dcbae75cc03e4daddcc1a398788be5b92f02d59869838fb7e3acf45cd3123 |
| IEDL.DBID | DOA |
| ISICitedReferencesCount | 640 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000417100100002&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 2167-8359 |
| IngestDate | Mon Nov 10 04:29:33 EST 2025 Tue Nov 04 01:59:24 EST 2025 Thu Oct 02 11:58:29 EDT 2025 Tue Nov 11 10:19:28 EST 2025 Tue Nov 04 17:58:22 EST 2025 Thu May 22 21:21:59 EDT 2025 Thu Apr 03 07:01:43 EDT 2025 Sat Nov 29 05:38:27 EST 2025 Tue Nov 18 22:22:54 EST 2025 |
| IsDoiOpenAccess | true |
| IsOpenAccess | true |
| IsPeerReviewed | true |
| IsScholarly | true |
| Keywords | ArcGIS CANAPE categorization Ecological niche models Rarefy occurrences Spatial jackknifing MaxEnt bias files Geographic information systems |
| Language | English |
| License | http://creativecommons.org/licenses/by/4.0 This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ) and either DOI or URL of the article must be cited. |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c542t-ff95dcbae75cc03e4daddcc1a398788be5b92f02d59869838fb7e3acf45cd3123 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 |
| OpenAccessLink | https://doaj.org/article/0a9265d4854944f9977d9c2786a0e738 |
| PMID | 29230356 |
| PQID | 1976000083 |
| PQPubID | 23479 |
| ParticipantIDs | doaj_primary_oai_doaj_org_article_0a9265d4854944f9977d9c2786a0e738 pubmedcentral_primary_oai_pubmedcentral_nih_gov_5721907 proquest_miscellaneous_1976000083 gale_infotracmisc_A517457428 gale_infotracacademiconefile_A517457428 gale_healthsolutions_A517457428 pubmed_primary_29230356 crossref_primary_10_7717_peerj_4095 crossref_citationtrail_10_7717_peerj_4095 |
| PublicationCentury | 2000 |
| PublicationDate | 2017-12-05 |
| PublicationDateYYYYMMDD | 2017-12-05 |
| PublicationDate_xml | – month: 12 year: 2017 text: 2017-12-05 day: 05 |
| PublicationDecade | 2010 |
| PublicationPlace | United States |
| PublicationPlace_xml | – name: United States – name: San Francisco, USA |
| PublicationTitle | PeerJ (San Francisco, CA) |
| PublicationTitleAlternate | PeerJ |
| PublicationYear | 2017 |
| Publisher | PeerJ. Ltd PeerJ Inc |
| Publisher_xml | – name: PeerJ. Ltd – name: PeerJ Inc |
| References | Anderson (10.7717/peerj.4095/ref-2) 2011; 222 Anderson (10.7717/peerj.4095/ref-3) 2010; 37 Ray (10.7717/peerj.4095/ref-20) 2005; 5 Barve (10.7717/peerj.4095/ref-5) 2011; 222 McRae (10.7717/peerj.4095/ref-12) 2007; 104 ESRI (10.7717/peerj.4095/ref-8) 2017 Lobo (10.7717/peerj.4095/ref-11) 2008; 17 Veloz (10.7717/peerj.4095/ref-22) 2009; 36 Phillips (10.7717/peerj.4095/ref-17) 2008; 31 Brown (10.7717/peerj.4095/ref-7) 2014; 5 Warren (10.7717/peerj.4095/ref-23) 2011; 21 Boria (10.7717/peerj.4095/ref-6) 2014; 275 Radosavljevic (10.7717/peerj.4095/ref-19) 2014; 41 Laffan (10.7717/peerj.4095/ref-10) 2010; 33 Anderson (10.7717/peerj.4095/ref-1) 2003; 30 Barbet-Massin (10.7717/peerj.4095/ref-4) 2012; 3 Phillips (10.7717/peerj.4095/ref-18) 2017 Mishler (10.7717/peerj.4095/ref-14) 2014; 5 Merow (10.7717/peerj.4095/ref-13) 2013; 36 Peterson (10.7717/peerj.4095/ref-15) 2011; 49 Phillips (10.7717/peerj.4095/ref-16) 2006; 190 Shcheglovitova (10.7717/peerj.4095/ref-21) 2013; 269 Hijmans (10.7717/peerj.4095/ref-9) 2012; 93 |
| References_xml | – year: 2017 ident: 10.7717/peerj.4095/ref-18 article-title: MaxEnt software for modeling species niches and distributions – volume: 33 start-page: 643 year: 2010 ident: 10.7717/peerj.4095/ref-10 article-title: Biodiverse, a tool for the spatial analysis of biological and related diversity publication-title: Ecography doi: 10.1111/j.1600-0587.2010.06237.x – volume: 222 start-page: 1810 issue: 11 year: 2011 ident: 10.7717/peerj.4095/ref-5 article-title: The crucial role of the accessible area in ecological niche modeling and species distribution modeling publication-title: Ecological Modelling doi: 10.1016/j.ecolmodel.2011.02.011 – volume: 31 start-page: 161 year: 2008 ident: 10.7717/peerj.4095/ref-17 article-title: Modeling of species distributions with MaxEnt: new extensions and a comprehensive evaluation publication-title: Ecography doi: 10.1111/j.0906-7590.2008.5203.x – volume: 5 start-page: 177 issue: 1 year: 2005 ident: 10.7717/peerj.4095/ref-20 article-title: PATHMATRIX: a geographical information system tool to compute effective distances among samples publication-title: Molecular Ecology Notes doi: 10.1111/j.1471-8286.2004.00843.x – volume: 3 start-page: 327 year: 2012 ident: 10.7717/peerj.4095/ref-4 article-title: Selecting pseudo-absences for species distribution models: how, where and how many? publication-title: Methods in Ecology and Evolution doi: 10.1111/j.2041-210X.2011.00172.x – volume: 21 start-page: 335 year: 2011 ident: 10.7717/peerj.4095/ref-23 article-title: Ecological niche modeling in MaxEnt: the importance of model complexity and the performance of model selection criteria publication-title: Ecological Applications doi: 10.1890/10-1171.1 – volume: 5 start-page: 694 issue: 7 year: 2014 ident: 10.7717/peerj.4095/ref-7 article-title: SDMtoolbox: a python-based GIS toolkit for landscape genetic, biogeographic, and species distribution model analyses publication-title: Methods in Ecology and Evolution doi: 10.1111/2041-210X.12200 – volume: 104 start-page: 19885 issue: 50 year: 2007 ident: 10.7717/peerj.4095/ref-12 article-title: Circuit theory predicts gene flow in plant and animal populations publication-title: Proceedings of the National Academy of Sciences doi: 10.1073/pnas.0706568104 – volume: 36 start-page: 1058 year: 2013 ident: 10.7717/peerj.4095/ref-13 article-title: A practical guide to MaxEnt for modeling species’ distributions: what it does, and why inputs and settings matter publication-title: Ecography doi: 10.1111/j.1600-0587.2013.07872.x – volume: 222 start-page: 2796 year: 2011 ident: 10.7717/peerj.4095/ref-2 article-title: Species-specific tuning increases robustness to sampling bias in models of species distributions: an implementation with MaxEnt publication-title: Ecological Modelling doi: 10.1016/j.ecolmodel.2011.04.011 – volume: 190 start-page: 231 year: 2006 ident: 10.7717/peerj.4095/ref-16 article-title: Maximum entropy modeling of species geographic distributions publication-title: Ecological Modelling doi: 10.1016/j.ecolmodel.2005.03.026 – volume-title: ArcGIS desktop and spatial analyst extension: release 10.5 year: 2017 ident: 10.7717/peerj.4095/ref-8 – volume: 17 start-page: 145 year: 2008 ident: 10.7717/peerj.4095/ref-11 article-title: AUC: a misleading measure of the performance of predictive distribution models publication-title: Global Ecology & Biogeography doi: 10.1111/j.1466-8238.2007.00358.x – volume: 49 volume-title: Ecological niches and geographic distributions year: 2011 ident: 10.7717/peerj.4095/ref-15 doi: 10.23943/princeton/9780691136868.001.0001 – volume: 5 start-page: 4473 year: 2014 ident: 10.7717/peerj.4095/ref-14 article-title: Phylogenetic measures of biodiversity and neo- and paleo-endemism in Australian Acacia publication-title: Nature Communications doi: 10.1038/ncomms5473 – volume: 93 start-page: 679 year: 2012 ident: 10.7717/peerj.4095/ref-9 article-title: Cross-validation of species distribution models: removing spatial sorting bias and calibration with a null model publication-title: Ecology doi: 10.1890/11-0826.1 – volume: 269 start-page: 9 year: 2013 ident: 10.7717/peerj.4095/ref-21 article-title: Estimating optimal complexity for ecological niche models: a jackknife approach for species with small sample sizes publication-title: Ecological Modeling doi: 10.1016/j.ecolmodel.2013.08.011 – volume: 36 start-page: 2290 year: 2009 ident: 10.7717/peerj.4095/ref-22 article-title: Spatially autocorrelated sampling falsely inflates measures of accuracy for presence-only niche models publication-title: Journal of Biogeography doi: 10.1111/j.1365-2699.2009.02174.x – volume: 41 start-page: 629 year: 2014 ident: 10.7717/peerj.4095/ref-19 article-title: Making better MaxEnt models of species distributions: complexity, overfitting and evaluation publication-title: Journal of Biogeography doi: 10.1111/jbi.12227 – volume: 37 start-page: 1378 year: 2010 ident: 10.7717/peerj.4095/ref-3 article-title: The effect of the extent of the study region on GIS models of species geographic distributions and estimates of niche evolution: preliminary tests with montane rodents (genus Nephelomys) in Venezuela publication-title: Journal of Biogeography doi: 10.1111/j.1365-2699.2010.02290.x – volume: 275 start-page: 73 year: 2014 ident: 10.7717/peerj.4095/ref-6 article-title: Spatial filtering to reduce sampling bias can improve the performance of ecological niche models publication-title: Ecological Modeling doi: 10.1016/j.ecolmodel.2013.12.012 – volume: 30 start-page: 591 year: 2003 ident: 10.7717/peerj.4095/ref-1 article-title: Real vs. artefactual absences in species distributions: tests for Oryzomys albigularis (Rodentia: Muridae) in Venezuela publication-title: Journal of Biogeography doi: 10.1046/j.1365-2699.2003.00867.x |
| SSID | ssj0000826083 |
| Score | 2.6196663 |
| Snippet | SDMtoolbox 2.0 is a software package for spatial studies of ecology, evolution, and genetics. The release of SDMtoolbox 2.0 allows researchers to use the most... |
| SourceID | doaj pubmedcentral proquest gale pubmed crossref |
| SourceType | Open Website Open Access Repository Aggregation Database Index Database Enrichment Source |
| StartPage | e4095 |
| SubjectTerms | Analysis Animal behavior Animal populations ArcGIS Biodiversity Biogeography Bioinformatics Conservation Biology Ecological niche models Geographic information systems Graphical user interfaces MaxEnt bias files Python (Programming language) Rarefy occurrences Spatial jackknifing Technology application |
| Title | SDMtoolbox 2.0: the next generation Python-based GIS toolkit for landscape genetic, biogeographic and species distribution model analyses |
| URI | https://www.ncbi.nlm.nih.gov/pubmed/29230356 https://www.proquest.com/docview/1976000083 https://pubmed.ncbi.nlm.nih.gov/PMC5721907 https://doaj.org/article/0a9265d4854944f9977d9c2786a0e738 |
| Volume | 5 |
| WOSCitedRecordID | wos000417100100002&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| journalDatabaseRights | – providerCode: PRVAON databaseName: DOAJ (selected full-text) customDbUrl: eissn: 2167-8359 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000826083 issn: 2167-8359 databaseCode: DOA dateStart: 20130101 isFulltext: true titleUrlDefault: https://www.doaj.org/ providerName: Directory of Open Access Journals – providerCode: PRVHPJ databaseName: ROAD: Directory of Open Access Scholarly Resources customDbUrl: eissn: 2167-8359 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000826083 issn: 2167-8359 databaseCode: M~E dateStart: 20130101 isFulltext: true titleUrlDefault: https://road.issn.org providerName: ISSN International Centre – providerCode: PRVPQU databaseName: Biological science database customDbUrl: eissn: 2167-8359 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000826083 issn: 2167-8359 databaseCode: M7P dateStart: 20130212 isFulltext: true titleUrlDefault: http://search.proquest.com/biologicalscijournals providerName: ProQuest – providerCode: PRVPQU databaseName: ProQuest Central customDbUrl: eissn: 2167-8359 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000826083 issn: 2167-8359 databaseCode: BENPR dateStart: 20130212 isFulltext: true titleUrlDefault: https://www.proquest.com/central providerName: ProQuest – providerCode: PRVPQU databaseName: Publicly Available Content Database customDbUrl: eissn: 2167-8359 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000826083 issn: 2167-8359 databaseCode: PIMPY dateStart: 20130212 isFulltext: true titleUrlDefault: http://search.proquest.com/publiccontent providerName: ProQuest – providerCode: PRVPQU databaseName: Science Database customDbUrl: eissn: 2167-8359 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000826083 issn: 2167-8359 databaseCode: M2P dateStart: 20130212 isFulltext: true titleUrlDefault: https://search.proquest.com/sciencejournals providerName: ProQuest |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV1Lb9QwELagRYgL4k2gLEYgISTSep04jrm10EIldhVRkLanyPGjXaiSajdF8BP418w46WojkLhwsZTMRIntGfsbZfwNIS8ym7HE-yrWmfVxmmgX52O4dBI8zzhrlQ9VSz7K6TSfzVSxVuoLc8I6euBu4HaYVjwTNs0hkElTrwCvWGW4zDPNnEzCMV8m1VowFdZgQM0ALjo-Ugkhy865c4uv2xDNiMEOFIj6_1yO1_ajYa7k2uZzcIvc7FEj3e2-9ja54uo75Pqk_y9-l_w6ejdpm-asan5Qvs3eUIB1tIZ1l54EWmkcfVr8RJ6AGPctS98fHlF84tu8pYBbaTjyi8lQ4Ql4zWtazZuTrkT66dxQkFM8lgmRNbXIttsXyqKhlg6IkdzELe-RLwf7n99-iPsiC7ERKW9j75WwptJOCmNY4lILK54xY52oHMLjyolKcc-4RSJ3lSe5r6RLtPGpMDaBfe8-2aib2j0kFFYHpwGxZCnTqXGgnTObcyMqxg3TIiKvLge-ND0DORbCOCshEsFJKsMklThJEXm-0j3veDf-qrWH87fSQK7scAMsqOwtqPyXBUXkKc5-2R08XXl8uYsk3kJCfBaRl0EDfR4-2Oj-6AJ0G9mzBppbA03wVTMQP7u0sBJFmOBWu-ZiWY4V_iFFQByRB53FrXrFAYSzRGQRkQNbHHR7KKnnp4EqXECAr5h89D_G6TG5wRHTYC6P2CIb7eLCPSHXzPd2vlyMyFU5y0dkc29_WnwaBW-EdsILbCW0m8XhpDj-DSetPlM |
| linkProvider | Directory of Open Access Journals |
| 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=SDMtoolbox+2.0%3A+the+next+generation+Python-based+GIS+toolkit+for+landscape+genetic%2C+biogeographic+and+species+distribution+model+analyses&rft.jtitle=PeerJ+%28San+Francisco%2C+CA%29&rft.au=Brown%2C+Jason+L.&rft.au=Bennett%2C+Joseph+R.&rft.au=French%2C+Connor+M.&rft.date=2017-12-05&rft.pub=PeerJ+Inc&rft.eissn=2167-8359&rft.volume=5&rft_id=info:doi/10.7717%2Fpeerj.4095&rft_id=info%3Apmid%2F29230356&rft.externalDocID=PMC5721907 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=2167-8359&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=2167-8359&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=2167-8359&client=summon |