Evaluating tropical phytoplankton phenology metrics using contemporary tools

www.nature.com/scientificreports OPEN Received: 16 July 2018 Accepted: 4 December 2018 Published: xx xx xxxx Evaluating tropical phytoplankton phenology metrics using contemporary tools John A. Gittings1, Dionysios E. Raitsos2,3,4, Malika Kheireddine5, Marie-Fanny Racault Hervé Claustre6 & Ibrahim Hoteit1 2,3 , 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. In tropical oceans, phytoplankton constitute a direct food source for coral reef fauna and pelagic larvae1–4, whose survival ultimately contributes to healthy, diverse marine ecosystems. This translates to economic support, services and well-being for maritime nations via fisheries and tourism5. Phenology characterises the timing of phytoplankton growth periods and is an integral component controlling the structure of marine food webs and marine ecosystem functioning6,7. Alterations to phytoplankton phenology may influence the survival of higher trophic levels due to variations in the timing of food availability8–10. Thus, monitoring phenology at seasonal and interannual timescales is necessary for the establishment of management strategies in tropical oceans and associated coral reef ecosystems. Phenology metrics, including the timing of phytoplankton growth initiation, maximum amplitude, termination and duration, are referred to as ‘ecological indicators’, representing objective and quantitative measurements that can be utilised to evaluate the condition of marine ecosystems and their response to environmental change11–14. Ocean-colour remote sensing is currently the only method providing continuous, long-term (~20 years), synoptic time series of phytoplankton abundance (indexed by chlorophyll-a [Chl-a] concentration), from which phytoplankton phenology metrics can be computed15. However, remotely-sensed Chl-a observations are representative of the surface oceanic layer (~first optical depth), rather than being indicative of the complete vertical phytoplankton distribution within the water column. In particular, stratified tropical ecosystems are characterised 1 Department of Earth Science and Engineering, King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Saudi Arabia. 2Remote Sensing Group, Plymouth Marine Laboratory (PML), The Hoe, Plymouth, PL1 3DH, United Kingdom. 3National Centre for Earth Observation (NCEO), Plymouth Marine Laboratory (PML), The Hoe, Plymouth, PL1 3DH, United Kingdom. 4Department of Biology, National and Kapodistrian University of Athens, Athens, Greece. 5Red Sea Research Centre, Biological and Environmental Science and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Saudi Arabia. 6Marine Optics and Remote Sensing Laboratory, Laboratoire d’Océanographie de Villefranche, Villefranche-sur-Mer, France. Correspondence and requests for materials should be addressed to I.H. (email: ) SCiENTifiC REPOrTS | (2019) 9:674 | DOI:10.1038/s41598-018-37370-4 1 www.nature.com/scientificreports/ Figure 1. (a) Map displaying the track of the PROVOR BGC-Argo float (red circles) and corresponding satellite (OC-CCI) matchups (grey-shaded squares) in the northern Red Sea. A total of 139 vertical profiles were analysed between September 30th 2015 and September 27th 2016). (b) Time series displaying the derivative of the cumulative sums of Chl-a anomalies used to identify the timing of phenology metrics (initiation and termination) for the satellite and BGC-Argo datasets. The horizontal grey line located at zero highlights the transition between increasing/decreasing trends in the cumulative sums of Chl-a anomalies (e.g. when Chl-a concentrations rise above/below the phenology threshold criterion, see Materials and Methods). by the presence of Subsurface Chl-a Maxima (SCM) that cannot be detected by satellites. To date, attempts to validate satellite-based estimates of phytoplankton phenology with in situ measurements remain sparse, primarily due to the lack of continuous, spatially extensive observations13,16. The aforesaid limitations of satellite-derived datasets may discourage researchers from utilising remotely-sensed information in ecosystem management schemes. Oceanographic multi-platforms could bridge this gap and provide the necessary information needed to assess the potential of satellite remote sensing in retrieving phenology indices, and also, enable a more holistic quantification of phenology over the whole water column. Adopting an innovative approach, we synergistically utilise satellite-derived Chl-a observations with data from an autonomous Biogeochemical-Argo float (BGC-Argo float) to evaluate (1) the capability of remote-sensing data to estimate phytoplankton phenology metrics in a typical tropical marine ecosystem – the northern Red Sea; and (2) extend the phenological analysis to the part of the upper water column that is not seen by satellites. We corroborate surface signatures detected by satellites by investigating the physical mechanisms that control vertical phytoplankton dynamics. Results Comparing phenology metrics from satellite and BGC-Argo datasets. To evaluate the capability of satellite-derived Chl-a observations for the computation of phytoplankton phenology, we directly compare phenology metrics computed using satellite (OC-CCI) and BGC-Argo Chl-a datasets in the Red Sea (Fig. 1a). We refer to the time series of surface Chl-a conc (...truncated)