PANGAEA_995255
n-Alkane Distributions of Modern Plants from Kenya and Niger
Tweedy, Ruth; Shi, Sarah; Uno, Kevin
n-Alkane Distributions of Modern Plants from Kenya and Niger
2026-06-19
PANGAEA
Dataset
https://doi.pangaea.de/10.1594/PANGAEA.995255
Investigator
Kevin
Uno
kevinuno@g.harvard.edu
Earth Science
Event label
Latitude of event
Longitude of event
Date/Time of event
Reference/source
Species
Family
Subfamily
Domain, plants
Monocotyledons/dicotyledons
Growth habit
Photosynthetic pathway
Location
n-Alkane C10, biomass, dry mass
n-Alkane C11, biomass, dry mass
n-Alkane C12, biomass, dry mass
n-Alkane C13, biomass, dry mass
n-Alkane C14, biomass, dry mass
n-Alkane C15, biomass, dry mass
n-Alkane C16, biomass, dry mass
n-Alkane C17, biomass, dry mass
n-Alkane C18, biomass, dry mass
n-Alkane C19, biomass, dry mass
n-Alkane C20, biomass, dry mass
n-Alkane C21, biomass, dry mass
n-Alkane C22, biomass, dry mass
n-Alkane C23, biomass, dry mass
n-Alkane C24, biomass, dry mass
n-Alkane C25, biomass, dry mass
n-Alkane C26, biomass, dry mass
n-Alkane C27, biomass, dry mass
n-Alkane C28, biomass, dry mass
n-Alkane C29, biomass, dry mass
n-Alkane C30, biomass, dry mass
n-Alkane C31, biomass, dry mass
n-Alkane C32, biomass, dry mass
n-Alkane C33, biomass, dry mass
n-Alkane C34, biomass, dry mass
n-Alkane C35, biomass, dry mass
n-Alkane C36, biomass, dry mass
n-Alkane C37, biomass, dry mass
n-Alkane C38, biomass, dry mass
n-Alkane C39, biomass, dry mass
n-Alkane C40, biomass, dry mass
Sum n-alkane (C25-C35), biomass, dry mass
n-Alkane C25, relative proportion
n-Alkane C27, relative proportion
n-Alkane C29, relative proportion
n-Alkane C31, relative proportion
n-Alkane C33, relative proportion
n-Alkane C35, relative proportion
n-Alkane, average odd chain length (C25-C35)
Carbon Preference Index, n-alkanes (C25-C35)
geoscientificInformation
Africa
Field sampling
KU587
KU588
KU589
KU590
KU591
KU592
KU593
KU594
KU595
KU596
KU597
KU598
KU599
KU600
KU601
KU602
KU603
KU604
KU605
KU606
KU607
KU608
KU609
KU610
KU611
KU612
KU613
KU614
KU615
KU619
KU620
KU621
KU622
KU623
KU624
KU625
KU626
KU627
KU628
KU629
KU637
KU638
KU639
KU640
KU641
KU642
KU643
KU644
KU645
KU646
KU647
KU655
KU656
KU657
KU658
KU659
KU660
KU661
KU662
KU663
KU664
KU665
KU673
KU674
KU675
KU676
KU677
KU678
KU679
KU680
KU681
KU689
KU690
KU691
KU692
KU693
KU694
KU695
KU696
KU697
KU698
KU699
KU700
KU701
KU702
KU703
KU704
KU705
KU706
KU707
KU708
KU709
KU710
KU711
KU712
KU713
KU714
KU715
KU716
KU803
KU804
KU805
KU807
KU808
KU809
KU810
KU811
KU812
KU816
KU817
KU818
KU819
KU820
KU821
KU823
KU824
KU825
KU826
KU827
KU828
KU830
KU831
KU832
KU833
KU834
KU835
KU836
KU837
KU838
KU839
KU840
KU842
KU843
KU844
KU845
KU846
KU847
KU848
KU849
KU850
KU851
KU852
KU853
KU855
KU859
KU860
KU862
KU865
LT_2019_Plant
LT_2019_Plant_KU865
modern samples
n-alkanes
NGR_2019_Plant
NGR_2019_Plant_KU803
NGR_2019_Plant_KU804
NGR_2019_Plant_KU805
NGR_2019_Plant_KU807
NGR_2019_Plant_KU808
NGR_2019_Plant_KU809
NGR_2019_Plant_KU810
NGR_2019_Plant_KU811
NGR_2019_Plant_KU812
NGR_2019_Plant_KU816
NGR_2019_Plant_KU817
NGR_2019_Plant_KU818
NGR_2019_Plant_KU819
NGR_2019_Plant_KU820
NGR_2019_Plant_KU821
NGR_2019_Plant_KU823
NGR_2019_Plant_KU824
NGR_2019_Plant_KU825
NGR_2019_Plant_KU826
NGR_2019_Plant_KU827
NGR_2019_Plant_KU828
NGR_2019_Plant_KU830
NGR_2019_Plant_KU831
NGR_2019_Plant_KU832
NGR_2019_Plant_KU833
NGR_2019_Plant_KU834
NGR_2019_Plant_KU835
NGR_2019_Plant_KU836
NGR_2019_Plant_KU837
NGR_2019_Plant_KU838
NGR_2019_Plant_KU839
NGR_2019_Plant_KU840
NGR_2019_Plant_KU842
NGR_2019_Plant_KU843
NGR_2019_Plant_KU844
NGR_2019_Plant_KU845
NGR_2019_Plant_KU846
NGR_2019_Plant_KU847
NGR_2019_Plant_KU848
NGR_2019_Plant_KU849
NGR_2019_Plant_KU850
NGR_2019_Plant_KU851
NGR_2019_Plant_KU852
NGR_2019_Plant_KU853
NGR_2019_Plant_KU855
NGR_2019_Plant_KU859
NGR_2019_Plant_KU860
NGR_2019_Plant_KU862
paleoecology
Plant wax
SNR_2001_2002_Plant
SNR_2001_2002_Plant_KU587
SNR_2001_2002_Plant_KU588
SNR_2001_2002_Plant_KU589
SNR_2001_2002_Plant_KU590
SNR_2001_2002_Plant_KU591
SNR_2001_2002_Plant_KU592
SNR_2001_2002_Plant_KU593
SNR_2001_2002_Plant_KU594
SNR_2001_2002_Plant_KU595
SNR_2001_2002_Plant_KU596
SNR_2001_2002_Plant_KU597
SNR_2001_2002_Plant_KU598
SNR_2001_2002_Plant_KU599
SNR_2001_2002_Plant_KU600
SNR_2001_2002_Plant_KU601
SNR_2001_2002_Plant_KU602
SNR_2001_2002_Plant_KU603
SNR_2001_2002_Plant_KU604
SNR_2001_2002_Plant_KU605
SNR_2001_2002_Plant_KU606
SNR_2001_2002_Plant_KU607
SNR_2001_2002_Plant_KU608
SNR_2001_2002_Plant_KU609
SNR_2001_2002_Plant_KU610
SNR_2001_2002_Plant_KU611
SNR_2001_2002_Plant_KU612
SNR_2001_2002_Plant_KU613
SNR_2001_2002_Plant_KU614
SNR_2001_2002_Plant_KU615
SNR_2001_2002_Plant_KU619
SNR_2001_2002_Plant_KU620
SNR_2001_2002_Plant_KU621
SNR_2001_2002_Plant_KU622
SNR_2001_2002_Plant_KU623
SNR_2001_2002_Plant_KU624
SNR_2001_2002_Plant_KU625
SNR_2001_2002_Plant_KU626
SNR_2001_2002_Plant_KU627
SNR_2001_2002_Plant_KU628
SNR_2001_2002_Plant_KU629
SNR_2001_2002_Plant_KU637
SNR_2001_2002_Plant_KU638
SNR_2001_2002_Plant_KU639
SNR_2001_2002_Plant_KU640
SNR_2001_2002_Plant_KU641
SNR_2001_2002_Plant_KU642
SNR_2001_2002_Plant_KU643
SNR_2001_2002_Plant_KU644
SNR_2001_2002_Plant_KU645
SNR_2001_2002_Plant_KU646
SNR_2001_2002_Plant_KU647
SNR_2001_2002_Plant_KU655
SNR_2001_2002_Plant_KU656
SNR_2001_2002_Plant_KU657
SNR_2001_2002_Plant_KU658
SNR_2001_2002_Plant_KU659
SNR_2001_2002_Plant_KU660
SNR_2001_2002_Plant_KU661
SNR_2001_2002_Plant_KU662
SNR_2001_2002_Plant_KU663
SNR_2001_2002_Plant_KU664
SNR_2001_2002_Plant_KU665
SNR_2001_2002_Plant_KU673
SNR_2001_2002_Plant_KU674
SNR_2001_2002_Plant_KU675
SNR_2001_2002_Plant_KU676
SNR_2001_2002_Plant_KU677
SNR_2001_2002_Plant_KU678
SNR_2001_2002_Plant_KU679
SNR_2001_2002_Plant_KU680
SNR_2001_2002_Plant_KU681
SNR_2001_2002_Plant_KU689
SNR_2001_2002_Plant_KU690
SNR_2001_2002_Plant_KU691
SNR_2001_2002_Plant_KU692
SNR_2001_2002_Plant_KU693
SNR_2001_2002_Plant_KU694
SNR_2001_2002_Plant_KU695
SNR_2001_2002_Plant_KU696
SNR_2001_2002_Plant_KU697
SNR_2001_2002_Plant_KU698
SNR_2001_2002_Plant_KU699
SNR_2001_2002_Plant_KU700
SNR_2001_2002_Plant_KU701
SNR_2001_2002_Plant_KU702
SNR_2001_2002_Plant_KU703
SNR_2001_2002_Plant_KU704
SNR_2001_2002_Plant_KU705
SNR_2001_2002_Plant_KU706
SNR_2001_2002_Plant_KU707
SNR_2001_2002_Plant_KU708
SNR_2001_2002_Plant_KU709
SNR_2001_2002_Plant_KU710
SNR_2001_2002_Plant_KU711
SNR_2001_2002_Plant_KU712
SNR_2001_2002_Plant_KU713
SNR_2001_2002_Plant_KU714
SNR_2001_2002_Plant_KU715
SNR_2001_2002_Plant_KU716
Gas chromatography (GC), Agilent, 7890A equipped with a mass selective detector (MSD), Agilent, 5975C
Calculated
2001-10-06
2019-11-09
Complete
0.5683
16.990325
2.109298
37.528
unrestricted
CC-BY-4.0: Creative Commons Attribution 4.0 International
English
PANGAEA
Data Publisher for Earth & Environmental Science
https://www.pangaea.de/
Data Center Contact
Michael
Diepenbroek
info@pangaea.de
Leobener Str.
Bremen
Bremen
28359
Germany
online
7128 data points
text/tab-separated-values
Tweedy, Ruth; Shi, Sarah; Uno, Kevin (2026): Supervised Machine Learning Approaches to African Plant Chemotaxonomy from n-Alkane Chain Length Abundances. Organic Geochemistry, https://doi.org/10.1016/j.orggeochem.2026.105295
This dataset comprises plant wax n-alkane concentrations (C₂₅ - C₃₅) and corresponding relative abundances for 148 modern African angiosperm samples, including trees, shrubs, grasses, herbs, vines, and palms. Samples were collected from three regions: Samburu National Reserve (SNR) in Kenya (n = 99), Lothagam in Kenya (n = 1), and multiple locations across Niger (n = 48). The SNR samples were collected between October 2001 and August 2002 and include repeated sampling of the same individual plants, allowing assessment of temporal variability in n-alkane distributions. The Lothagam and Niger samples were collected in April and November 2019, respectively. All specimens were identified to at least the family level, with most resolved to genus or species, and are accompanied by metadata including plant functional type and photosynthetic pathway. Sample preparation and analysis were conducted at Lamont-Doherty Earth Observatory between 2018 and 2021. n-Alkanes were quantified using gas chromatography with a mass selective detector (GC-MSD) and flame ionization detector (FID). Response factor corrections were applied to peak areas to obtain concentration values. Odd-carbon-number n-alkanes (C₂₅ - C₃₅) were normalized to unit sum to generate relative abundance distributions. Trees, shrubs, and grasses from this dataset formed part of the training and validation data for Tweedy et al. (2026), where machine learning models differentiated African woody and grassy samples with up to 89% validation accuracy. The 19 herb, palm, and vine samples included here are unique to this dataset and not published elsewhere. This dataset provides a reference for evaluating n-alkane chemotaxonomic signals and supports applications in vegetation reconstruction. ** For all details see the full metadata description at "https://doi.pangaea.de/10.1594/PANGAEA.995255"!
https://doi.org/10.1016/j.orggeochem.2026.105295
Supervised Machine Learning Approaches to African Plant Chemotaxonomy from n-Alkane Chain Length Abundances
https://en.wikipedia.org/wiki/Species
Species
DIF
9.4
2026-06-19
2026-09-08