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  • 2020-2023  (4)
  • 1995-1999
  • 1935-1939
  • 1930-1934
  • 2022  (4)
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  • 2020-2023  (4)
  • 1995-1999
  • 1935-1939
  • 1930-1934
  • 2020-2024  (7)
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  • 1
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    Mixed Layer Depth Promotes Trophic Amplification on a Seasonal Scale (2022)
    Details
    Publication Date: 2022-10-06
    Description: The Humboldt Upwelling System is of global interest due to its importance to fisheries, though the origin of its high productivity remains elusive. In regional physical‐biogeochemical model simulations, the seasonal amplitude of mesozooplankton net production exceeds that of phytoplankton, indicating “seasonal trophic amplification.” An analytical approach identifies amplification to be driven by a seasonally varying trophic transfer efficiency due to mixed layer variations. The latter alters the vertical distribution of phytoplankton and thus the zooplankton and phytoplankton encounters, with lower encounters occurring in a deeper mixed layer where phytoplankton are diluted. In global model simulations, mixed layer depth appears to affect trophic transfer similarly in other productive regions. Our results highlight the importance of mixed layer depth for trophodynamics on a seasonal scale with potential significant implications, given mixed layer depth changes projected under climate change.
    Description: Plain Language Summary: The Humboldt Upwelling System is a fishery‐important region. A common assumption is that a certain amount of phytoplankton supports a proportional amount of fish. However, we find that a small seasonal change in phytoplankton can trigger a larger variation in zooplankton. This implies that one may underestimate changes in fish solely based on phytoplankton. Using ecosystem model simulations, we investigate why changes of phytoplankton are not proportionally reflected in zooplankton. The portion of phytoplankton that ends up in zooplankton is controlled by the changing depth of the surface ocean “mixed layer.” The “mixed layer” traps both the phytoplankton and zooplankton in a limited amount of space. When the “mixed layer” is shallow, zooplankton can feed more efficiently on phytoplankton as both are compressed in a comparatively smaller space. We conclude that in the Humboldt System, and other “food‐rich” regions, feeding efficiently, determined by the “mixed layer,” is more important than how much food is available.
    Description: Key Points: Environmental factors strongly affect plankton trophodynamics on a seasonal scale. Seasonal trophic amplification in the Humboldt system is driven by mixed layer dynamics. Mixed layer depth and food chain efficiency correlate also in other productive regions.
    Description: China Sponsorship Council
    Description: Bundesministerium für Bildung und Forschung http://dx.doi.org/10.13039/501100002347
    Keywords: ddc:577.7
    Language: English
    Type: doc-type:article
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    CITATION GEO-LEO
  • 2
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    Machine learning techniques to characterize functional traits of plankton from image data (2022)
    Association for the Sciences of Limnology and Oceanography
    Details
    Publication Date: 2022-10-18
    Description: © The Author(s), 2022. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Orenstein, E., Ayata, S., Maps, F., Becker, É., Benedetti, F., Biard, T., Garidel‐Thoron, T., Ellen, J., Ferrario, F., Giering, S., Guy‐Haim, T., Hoebeke, L., Iversen, M., Kiørboe, T., Lalonde, J., Lana, A., Laviale, M., Lombard, F., Lorimer, T., Martini, S., Meyer, A., Möller, K.O., Niehoff, B., Ohman, M.D., Pradalier, C., Romagnan, J.-B., Schröder, S.-M., Sonnet, V., Sosik, H.M., Stemmann, L.S., Stock, M., Terbiyik-Kurt, T., Valcárcel-Pérez, N., Vilgrain, L., Wacquet, G., Waite, A.M., & Irisson, J. Machine learning techniques to characterize functional traits of plankton from image data. Limnology and Oceanography, 67(8), (2022): 1647-1669, https://doi.org/10.1002/lno.12101.
    Description: Plankton imaging systems supported by automated classification and analysis have improved ecologists' ability to observe aquatic ecosystems. Today, we are on the cusp of reliably tracking plankton populations with a suite of lab-based and in situ tools, collecting imaging data at unprecedentedly fine spatial and temporal scales. But these data have potential well beyond examining the abundances of different taxa; the individual images themselves contain a wealth of information on functional traits. Here, we outline traits that could be measured from image data, suggest machine learning and computer vision approaches to extract functional trait information from the images, and discuss promising avenues for novel studies. The approaches we discuss are data agnostic and are broadly applicable to imagery of other aquatic or terrestrial organisms.
    Description: SDA acknowledges funding from CNRS for her sabbatical in 2018–2020. Additional support was provided by the Institut des Sciences du Calcul et des Données (ISCD) of Sorbonne Université (SU) through the support of the sponsored junior team FORMAL (From ObseRving to Modeling oceAn Life), especially through the post-doctoral contract of EO. JOI acknowledges funding from the Belmont Forum, grant ANR-18-BELM-0003-01. French co-authors also wish to thank public taxpayers who fund their salaries. This work is a contribution to the scientific program of Québec Océan and the Takuvik Joint International Laboratory (UMI3376; CNRS - Université Laval). FM was supported by an NSERC Discovery Grant (RGPIN-2014-05433). MS is supported by the Research Foundation - Flanders (FWO17/PDO/067). FB received support from ETH Zürich. MDO is supported by the Gordon and Betty Moore Foundation and the U.S. National Science Foundation. ECB is supported by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) under the grant agreement no. 88882.438735/2019-01. TB is supported by the French National Research Agency (ANR-19-CE01-0006). NVP is supported by the Spanish State Research Agency, Ministry of Science and Innovation (PTA2016-12822-I). FL is supported by the Institut Universitaire de France (IUF). HMS was supported by the Simons Foundation (561126) and the U.S. National Science Foundation (CCF-1539256, OCE-1655686). Emily Peacock is gratefully acknowledged for expert annotation of IFCB images. LS was supported by the Chair VISION from CNRS/Sorbonne Université.
    Repository Name: Woods Hole Open Access Server
    Type: Article
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    PAPER (OA)
  • 3
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    Machine learning techniques to characterise functional traits of plankton image data (2022)
    AMER SOC LIMNOLOGY OCEANOGRAPHY
    In:  EPIC3Limnology and Oceanography, AMER SOC LIMNOLOGY OCEANOGRAPHY, ISSN: 0024-3590
    Details
    Publication Date: 2022-08-15
    Description: Plankton imaging systems supported by automated classification and analysis have improved ecologists' ability to observe aquatic ecosystems. Today, we are on the cusp of reliably tracking plankton populations with a suite of lab-based and in situ tools, collecting imaging data at unprecedentedly fine spatial and temporal scales. But these data have potential well beyond examining the abundances of different taxa; the individual images themselves contain a wealth of information on functional traits. Here, we outline traits that could be measured from image data, suggest machine learning and computer vision approaches to extract functional trait information from the images, and discuss promising avenues for novel studies. The approaches we discuss are data agnostic and are broadly applicable to imagery of other aquatic or terrestrial organisms.
    Repository Name: EPIC Alfred Wegener Institut
    Type: Article , NonPeerReviewed
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  • 4
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    Projected effects of climate change on Pseudo-nitzschia bloom dynamics in the Gulf of Maine (2022)
    Elsevier
    Details
    Publication Date: 2022-07-20
    Description: © The Author(s), 2022. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Clark, S., Hubbard, K., Ralston, D., McGillicuddy, D., Stock, C., Alexander, M., & Curchitser, E. Projected effects of climate change on Pseudo-nitzschia bloom dynamics in the Gulf of Maine. Journal of Marine Systems, 230, (2022): 103737, https://doi.org/10.1016/j.jmarsys.2022.103737.
    Description: Worldwide, warming ocean temperatures have contributed to extreme harmful algal bloom events and shifts in phytoplankton species composition. In 2016 in the Gulf of Maine (GOM), an unprecedented Pseudo-nitzschia bloom led to the first domoic-acid induced shellfishery closures in the region. Potential links between climate change, warming temperatures, and the GOM Pseudo-nitzschia assemblage, however, remain unexplored. In this study, a global climate change projection previously downscaled to 7-km resolution for the Northwest Atlantic was further refined with a 1–3-km resolution simulation of the GOM to investigate the effects of climate change on HAB dynamics. A 25-year time slice of projected conditions at the end of the 21st century (2073–2097) was compared to a 25-year hindcast of contemporary ocean conditions (1994–2018) and analyzed for changes to GOM inflows, transport, and Pseudo-nitzschia australis growth potential. On average, climate change is predicted to lead to increased temperatures, decreased salinity, and increased stratification in the GOM, with the largest changes occurring in the late summer. Inflows from the Scotian Shelf are projected to increase, and alongshore transport in the Eastern Maine Coastal Current is projected to intensify. Increasing ocean temperatures will likely make P. australis growth conditions less favorable in the southern and western GOM but improve P. australis growth conditions in the eastern GOM, including a later growing season in the fall, and a longer growing season in the spring. Combined, these changes suggest that P. australis blooms in the eastern GOM could intensify in the 21st century, and that the overall Pseudo-nitzschia species assemblage might shift to warmer-adapted species such as P. plurisecta or other Pseudo-nitzschia species that may be introduced.
    Description: This research was funded by the National Science Foundation (Grant Number OCE-1840381), the National Institute of Environmental Health Sciences (Grant Number 1P01ES028938), the Woods Hole Center for Oceans and Human Health, and the Academic Programs Office of the Woods Hole Oceanographic Institution.
    Keywords: Gulf of Maine ; ROMS ; Pseudo-nitzschia ; Climate change ; Harmful algal blooms
    Repository Name: Woods Hole Open Access Server
    Type: Article
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