Evaluating tropical phytoplankton phenology metrics using contemporary tools View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2019-12

AUTHORS

John A. Gittings, Dionysios E. Raitsos, Malika Kheireddine, Marie-Fanny Racault, Hervé Claustre, Ibrahim Hoteit

ABSTRACT

The timing of phytoplankton growth (phenology) in tropical oceans is a crucial factor influencing the survival rates of higher trophic levels, food web structure and the functioning of coral reef ecosystems. Phytoplankton phenology is thus categorised as an 'ecosystem indicator', which can be utilised to assess ecosystem health in response to environmental and climatic perturbations. Ocean-colour remote sensing is currently the only technique providing global, long-term, synoptic estimates of phenology. However, due to limited available in situ datasets, studies dedicated to the validation of satellite-derived phenology metrics are sparse. The recent development of autonomous oceanographic observation platforms provides an opportunity to bridge this gap. Here, we use satellite-derived surface chlorophyll-a (Chl-a) observations, in conjunction with a Biogeochemical-Argo dataset, to assess the capability of remote sensing to estimate phytoplankton phenology metrics in the northern Red Sea - a typical tropical marine ecosystem. We find that phenology metrics derived from both contemporary platforms match with a high degree of precision (within the same 5-day period). The remotely-sensed surface signatures reflect the overall water column dynamics and successfully capture Chl-a variability related to convective mixing. Our findings offer important insights into the capability of remote sensing for monitoring food availability in tropical marine ecosystems, and support the use of satellite-derived phenology as an ecosystem indicator for marine management strategies in regions with limited data availability. More... »

PAGES

674

Identifiers

URI

http://scigraph.springernature.com/pub.10.1038/s41598-018-37370-4

DOI

http://dx.doi.org/10.1038/s41598-018-37370-4

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1111643099

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/30679755


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Download the RDF metadata as:  json-ld nt turtle xml License info

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RDF/XML is a standard XML format for linked data.

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283 https://www.grid.ac/institutes/grid.5216.0 schema:alternateName National and Kapodistrian University of Athens
284 schema:name Department of Biology, National and Kapodistrian University of Athens, Athens, Greece
285 National Centre for Earth Observation (NCEO), Plymouth Marine Laboratory (PML), The Hoe, PL1 3DH, Plymouth, United Kingdom
286 Remote Sensing Group, Plymouth Marine Laboratory (PML), PL1 3DH, The Hoe, Plymouth, United Kingdom
287 rdf:type schema:Organization
 




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