Distribution of phytoplankton functional types in high-nitrate, low-chlorophyll waters in a new diagnostic ecological indicator model

Abstract

Abstract.Modeling and monitoring plankton functional types (PFTs) is challenged by the insufficient amount of field measurements of ground truths in both plankton models and biooptical algorithms. In this study, we combine remote sensing data and a dynamic plankton model to simulate an ecologically sound spatial and temporal distribution of phytoPFTs. We apply an innovative ecological indicator approach to modeling PFTs and focus on resolving the question of diatom-coccolithophore coexistence in the subpolar highnitrate and low-chlorophyll regions. We choose an artificial neural network as our modeling framework because it has the potential to interpret complex nonlinear interactions governing complex adaptive systems, of which marine ecosystems are a prime example. Using ecological indicators that fulfill the criteria of measurability, sensitivity and specificity, we demonstrate that our diagnostic model correctly interprets some basic ecological rules similar to ones emerging from dynamic models. Our time series highlight a dynamic phyto-PFT community composition in all high-latitude areas and indicate seasonal coexistence of diatoms and coccolithophores. This observation, though consistent with in situ and remote sensing measurements, has so far not been captured by state-of-the-art dynamic models, which struggle to resolve this "paradox of the plankton". We conclude that an ecological indicator approach is useful for ecological modeling of phytoplankton and potentially higher trophic levels. Finally, we speculate that it could serve as a powerful tool in advancing ecosystem-based management of marine resources.

Obtaining Phytoplankton Diversity from Ocean Color: A Scientific Roadmap for Future Development

“…Phytoplankton composition product References ABUNDANCE Size classes Uitz et al, 2006;Brewin et al, 2010Brewin et al, , 2015 Size classes and multiple taxa Hirata et al, 2011 SPECTRAL REFLECTANCE Multiple taxa Alvain et al, 2005Alvain et al, , 2008Li et al, 2013;Ben Mustapha et al, 2014 Single taxon Coccolithophores Brown and Yoder, 1994;Moore et al, 2012Trichodesmium Subramaniam et al, 2002Westberry et al, 2005 ABSORPTION Size index Ciotti and Bricaud, 2006;Mouw and Yoder, 2010;Bricaud et al, 2012 Size classes Devred et al, 2006Devred et al, , 2011Hirata et al, 2008;Fujiwara et al, 2011;Roy et al, 2013 Multiple taxa Bracher et al, 2009;Sadeghi et al, 2012a;Werdell et al, 2014 BACK-SCATTERING Size classes Kostadinov et al, 2009Kostadinov et al, , 2016Fujiwara et al, 2011 ECOLOGICAL Taxonomic groups Palacz et al, 2013 Frontiers in Marine Science | www.frontiersin.orgFIGURE 1 | Illustration of phytoplankton diversity as found in nature impacted by environmental conditions, and how it can be derived from observations and modeling. Through in situ measurements (which represent the most real conditions), phytoplankton are grouped according to cellular traits that influence their optical properties such as pigments, size, morphology, and fluorescence, all also responding to photophysiology, which are named optical features of phytoplankton groups (PG).…”

“…Differences exist on the different satellite inputs (e.g., radiance, absorption, backscattering) and the underlying principles (for a comprehensive overview Mouw et al, 2017). Another approach incorporates various environmental parameters to predict PT based on their ecological preferences (Raitsos et al, 2008;Palacz et al, 2013). This method uses artificial neural networks to link the different biological and physical data sets.…”

“…This method uses artificial neural networks to link the different biological and physical data sets. While the approach of Raitsos et al (2008) was regionally developed for the North-Atlantic, the approach by Palacz et al (2013) is not purely based on remote sensing data but also requires a coupling to a dynamic plankton model. Products obtained from the PG algorithms (Table 2) are typically dominance (Brown and Yoder, 1994;Alvain et al, 2005;Moore et al, 2012;Ben Mustapha et al, 2014), presence or absence of a certain PT (Westberry et al, 2005;Werdell et al, 2014), fraction or concentration of chl-a of the three PSC (Devred et al, 2006(Devred et al, , 2011Uitz et al, 2006;Hirata et al, 2008Hirata et al, , 2011Kostadinov et al, 2009Kostadinov et al, , 2016Brewin et al, 2010Brewin et al, , 2015Fujiwara et al, 2011;Li et al, 2013;Roy et al, 2013) or a size factor characterizing the contribution of pico-(or micro-) phytoplankton to the phytoplankton community (Ciotti and Bricaud, 2006;Mouw and Yoder, 2010;Bricaud et al, 2012).…”

A Consumer's Guide to Satellite Remote Sensing of Multiple Phytoplankton Groups in the Global Ocean

“…To help users determine what might be best suited for their purpose, in addition to the satellite inputs, outputs, and validation metrics described above, we compile a comparative list of assumptions, strengths, and limitations (Table 4). It is possible that merged products produce the best output beyond any individually selected algorithm (Palacz et al, 2013), yet an understanding of the underlying inputs into a merged product is always desirable.…”

“…Thus, we exclude ecologically based methods that require additional physical and spatio-temporal information (e.g., Raitsos et al, 2008;Palacz et al, 2013). We utilize all of the algorithms that Kostadinov et al (2017) directly compare plus three additional algorithms (Hirata et al, 2008;Devred et al, 2011;Li et al, 2013).…”