Dataset: Cell abundances of taxonomic groups determined using a custom convolutional neural network from live Imaging FlowCytobot (IFCB) at seven stations sampled during R/V Pt. Sur cruise PS 18-09 in the western Gulf of Mexico, Sept-Oct 2017.

Final no updates expectedDOI: 10.26008/1912/bco-dmo.840201.1Version 1 (2021-02-08)Dataset Type:Cruise Results

Principal Investigator: Lisa Campbell (Texas A&M University)

Co-Principal Investigator: Darren W. Henrichs (Texas A&M University)

Contact: James Fiorendino (Texas A&M University)

BCO-DMO Data Manager: Nancy Copley (Woods Hole Oceanographic Institution)


Project: RAPID: Hurricane Impact on Phytoplankton Community Dynamics and Metabolic Response (HRR)


Abstract

Cell abundance data for taxonomic groups from a custom convolutional neural network from live Imaging FlowCytobot (IFCB) at seven stations from surface and chlorophyll maximum depths during R/V Pt. Sur PS 18-09, western Gulf of Mexico, Sept-Oct 2017. This dataset was uncorrected and is for comparison with the Texas Observatory for Algal Succession Time-series (TOAST).

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).

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.

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. See related dataset.

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


Related Datasets

IsRelatedTo

Dataset: HRR-IFCB manual counts
Campbell, L., Henrichs, D. W. (2021) Cell abundances 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 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 doi:10.26008/1912/bco-dmo.840060.1

Related Publications

Results

Anglès, S., Jordi, A., Henrichs, D. W., & Campbell, L. (2019). Influence of coastal upwelling and river discharge on the phytoplankton community composition in the northwestern Gulf of Mexico. Progress in Oceanography, 173, 26–36. doi:10.1016/j.pocean.2019.02.001
Results

Fiorendino, J. M., Gaonkar, C. C., Henrichs, D. W., & Campbell, L. (2021). Drivers of microplankton community assemblage following tropical cyclones. Journal of Plankton Research, 45(1), 205–220. https://doi.org/10.1093/plankt/fbab073
Results

Henrichs, D. W., Anglès, S., Gaonkar, C. C., & Campbell, L. (2021). Application of a convolutional neural network to improve automated early warning of harmful algal blooms. Environmental science and pollution research international, 28(22), 28544–28555. https://doi.org/10.1007/s11356-021-12471-2
Methods

Menden-Deuer, S., & Lessard, E. J. (2000). Carbon to volume relationships for dinoflagellates, diatoms, and other protist plankton. Limnology and Oceanography, 45(3), 569–579. doi:10.4319/lo.2000.45.3.0569
Methods

Moberg, E. A., & Sosik, H. M. (2012). Distance maps to estimate cell volume from two-dimensional plankton images. Limnology and Oceanography: Methods, 10(4), 278–288. doi:10.4319/lom.2012.10.278