File(s) | Type | Description | Action |
---|---|---|---|
IFCB_manual_carbon.csv (14.91 KB) | Comma Separated Values (.csv) | Primary data file for dataset ID 840147 | Download |
Biomass data for taxonomic groups from manually corrected live Imaging FlowCytobot (IFCB) analysis of water samples collected from surface and chlorophyll maximum depths during R/V Pt. Sur PS 18-09, western Gulf of Mexico, Sept-Oct 2017. These data were inspected visually and manually corrected.
On each of 2 cruise legs 01 and 03, samples were collected at 7 stations (S01, S06, S11, S16, S21, SS and GI) from 2 depths [surface and chlorophyll maximum depth when possible; see HRR-bottle data]) by CTD-rosette. At each station, triplicate 5-ml samples pre-filtered through 150 µm Nitex were analyzed immediately with an onboard Imaging FlowCytobot. All image data can be viewed on the TOAST dashboard: https://toast.tamu.edu/timeline?dataset=HRR_cruise.
Image analysis and feature extraction were performed using software developed by Sosik and colleagues which is available on github (https://github.com/hsosik/ifcb-analysis/). The automated classification approach of Sosik & Olson (2007), as modified and described by Anglès et al. (2019), was employed and the automated classification results were then inspected visually and manually corrected into a total of 102 categories that included 35 categories of diatoms, 30 categories of dinoflagellates, 10 categories of ciliates, 10 categories of flagellates, and 17 ‘others’, which included filamentous cyanobacteria, freshwater chlorophytes, coccolithophorids, and small cells that could not be identified taxonomically from images (refer to Fiorendino et al. 2021. for more details). See related dataset.
For comparison with the Texas Observatory for Algal Succession Time series (TOAST), IFCB images were also classified automatically into one of 112 classes utilizing a custom convolutional neural network (CNN) trained on a curated set of images (Henrichs et al. 2021.). See related dataset.
This dataset: Biomass for each image was estimated using the algorithm developed by Moberg & Sosik (2012) to calculate cellular volume from the extracted image features and then convert to total carbon per image (Menden-Deuer & Lessard 2000) and summed for each class..
Sampling locations:
Sample ID |
Station |
Leg |
Location Lat oN/Long oW |
L1_S01 |
S01 |
1 |
27.2286 -97.2686 |
L3_S01 |
S01 |
3 |
|
L1_S06 |
S06 |
1 |
27.8358 -96.9874 |
L3_S06 |
S06 |
3 |
|
L1_S11 |
S11 |
1 |
28.2614 -96.4129 |
L3_S11 |
S11 |
3 |
|
L1_S16 |
S16 |
1 |
28.5366 -95.8656 |
L3_S16 |
S16 |
3 |
|
L1_S21 |
S21 |
1 |
28.7644 -95.2978 |
L3_S21 |
S21 |
3 |
|
L1_SS |
SS |
1 |
28.9600 -95.0946 |
L3_SS |
SS |
3 |
|
L1_GI |
GI |
1 |
29.0649 -94.9000
|
L3_GI |
GI |
3 |
Campbell, L., Henrichs, D. W. (2021) Biomass of taxonomic groups from manually corrected live Imaging FlowCytobot (IFCB) analysis of water samples collected from surface and chlorophyll maximum depths during R/V Pt. Sur cruise PS 18-09 in the western Gulf of Mexico, Sept-Oct 2017. Biological and Chemical Oceanography Data Management Office (BCO-DMO). (Version 1) Version Date 2021-02-08 [if applicable, indicate subset used]. doi:10.26008/1912/bco-dmo.840147.1 [access date]
Terms of Use
This dataset is licensed under Creative Commons Attribution 4.0.
If you wish to use this dataset, it is highly recommended that you contact the original principal investigators (PI). Should the relevant PI be unavailable, please contact BCO-DMO (info@bco-dmo.org) for additional guidance. For general guidance please see the BCO-DMO Terms of Use document.