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Coupling ecological concepts with an ocean-colour model: Phytoplankton size structure
Get PDFPhytoplankton play a central role in the planetary cycling of important elements and compounds. Understanding how phytoplankton are responding to climate change is consequently a major question in Earth Sciences. Monitoring phytoplankton is key to answering this question. Satellite remote sensing of ocean colour is our only means of monitoring phytoplankton in the entire surface ocean at high temporal and large spatial scales, and the continuous ocean-colour data record is now approaching a length suitable for addressing questions around climate change, at least in some regions. Yet, developing ocean-colour algorithms for climate change studies requires addressing issues of ambiguity in the ocean-colour signal. For example, for the same chlorophyll-a concentration (Chl-a) of phytoplankton, the colour of the ocean can be different depending on the type of phytoplankton present. One route to tackle the issue of ambiguity is by enriching the ocean-colour data with information on sea surface temperature (SST), a good proxy of changes in three phytoplankton size classes (PSCs) independent of changes in total Chl-a, a measure of phytoplankton biomass. Using a global surface in-situ dataset of HPLC (high performance liquid chromatography) pigments, size-fractionated filtration data, and concurrent satellite SST spanning from 1991 to 2021, we re-tuned, validated and advanced an SST-dependent three-component model that quantifies the relationship between total Chl-a and Chl-a associated with the three PSCs (pico-, nano- and microplankton). Similar to previous studies, striking dependencies between model parameters and SST were captured, which were found to improve model performance significantly. These relationships were applied to 40 years of monthly composites of satellite SST, and significant trends in model parameters were observed globally, in response to climate warming. Changes in these parameters highlight issues in estimating long-term trends in phytoplankton biomass (Chl-a) from ocean colour using standard empirical algorithms, which implicitly assume a fixed relationship between total Chl-a and Chl-a of the three size classes. The proposed ecological model will be at the centre of a new ocean-colour modelling framework, designed for investigating the response of phytoplankton to climate change, described in subsequent parts of this series of papers
Validating and improving the uncertainty assumptions for the assimilation of ocean‐colour‐derived chlorophyll a into a marine biogeochemistry model of the Northwest European Shelf Seas
Get PDFThe correct specification of all sources of uncertainty is critical to the success of data assimilation (DA) in improving the realism and accuracy of forecasts and reanalyses. This work focuses on improving the uncertainty assumptions made during the assimilation of ocean-colour-derived chlorophyll a into an operational marine coupled physical–biogeochemical DA system, which produces daily biogeochemistry forecasts on the Northwest European Shelf Seas. Analysis of the observation–model misfits shows significant biases in chlorophyll a, which vary strongly with season. The behaviour of these misfits agrees well with previous studies and can be attributed to systematic errors within the coupled model. Diagnostic metrics, used frequently within numerical weather prediction, are applied to separate out the random component of the observation and model errors, allowing for the derivation of new error covariance matrices. These new error covariance matrices are then modified to account for the biases in the model that cannot be treated explicitly within the operational DA system. This has the effect of inflating both the error variances and the correlation length-scales. Experiments show that the new error covariances can result in significant improvements in the accuracy of the analysis and forecast. In particular, the new error covariance matrices reduce the bias in the spring phytoplankton bloom present when using the previous error covariances. Validation against independent glider observations in the North Sea also shows reductions in bias in chlorophyll a and oxygen that extend below the surface to the depth of the mixed layer. Accounting for the biases in the model in the error correlations can lead to much larger improvements than not accounting for them; however, there are also regions where large degradations are seen that may indicate model instabilities. This may be improved by estimating the bias separately for the different regions on the shelf
Evidence of a range expansion in sunfish from 47 years of coastal sightings
No full textAlmost nothing is known about the historical abundance of the ocean sunfish. Yet as an ecologically and functionally important taxa, understanding changes in abundance may be a useful indicator of how our seas are responding to anthropogenic changes including overfishing and climate change. Within this context, sightings from a coastal bird observatory (51.26∘ N, 9.30∘ W) over a 47 year period (from April to October 1971–2017) provided the first long-term index of sunfish abundance. Using a general linear mixed effect model with a hurdle to deal with imperfect detectability and to model trends, a higher probability of detecting sunfish was found in the 1990s and 2000s. Continuous Plankton Recorder (CPR) phytoplankton color indices and the annual mean position of the 13 ∘C sea surface isotherm were significantly correlated with the probability of detecting sunfish. An increase in siphonophore abundance (as measured by the CPR) was also documented. However, this increase occurred 10–15 years after the sunfish increase and was not significantly correlated with sunfish abundance. Our results suggest that the observed increase in sunfish sightings is evidence of a range expansion because it was significantly correlated with the mean position of the 13 ∘C isotherm which moved northwards by over 200 km. Furthermore, the observed increase in sunfish occured 10 years before sunfish sightings are documented in Icelandic and Norwegian waters, and was concurrent with well-known range expansions for other fish species during the 1990s. This study demonstrates how sustained citizen science projects can provide unique insights on the historical abundance of this enigmatic species
Bayesian two-part modeling of phytoplankton biomass and occurrence
Get PDFPhytoplankton biomass data often involve zero outcomes preventing a description by continuous distributions with positive support such as the lognormal distribution commonly used to describe ecological data. Two usual solutions: ignoring the zeroes and adding a small positive number to all outcomes, induce bias and reduce predictive power. To address these shortcomings, we design a Bayesian two-part
model with a binary component for presence or absence and a continuous component involving a lognormal model for non-zero biomass. We specify two equations relating species-specific occurrence probabilities and expected log-biomasses when present to potential covariates, with spike-and-slab priors imposed on linear effects to selectively discard the
irrelevant predictors. We analyze the biomass data of
74 phytoplankton (57 diatoms and 17 dinoflagellates)
recorded weekly at Station L4 (Western English Channel, UK) between April 2003 and December 2009, along with measurements of abiotic covariates. Our results disclose different combinations of environmental predictors for the occurrence and the biomass of individual species. Overall, the occurrence of dinoflagellates is associated with higher temperature and irradiance levels compared to diatoms, with
virtually no dependence on nutrient concentrations. Irradiance emerges as the key predictor of biomass when species are present. Optimum temperatures for biomass accumulation and temperature sensitivities vary widely among and within functional types. Compared to one-stage models based on usual zero handling approaches, our two-part model stands out with higher prediction accuracy. The two-part modeling approach provides a valuable framework for
decoupling the predictors of species occurrence and
abundance from observational data
Simultaneous retrieval of selected optical water quality indicators from Landsat-8, Sentinel-2, and Sentinel-3
Get PDFConstructing multi-source satellite-derived water quality (WQ) products in inland and nearshore coastal waters from the past, present, and future missions is a long-standing challenge. Despite inherent differences in sensors’ spectral capability, spatial sampling, and radiometric performance, research efforts focused on formulating, implementing, and validating universal WQ algorithms continue to evolve. This research extends a recently developed machine-learning (ML) model, i.e., Mixture Density Networks (MDNs) (Pahlevan et al., 2020; Smith et al., 2021), to the inverse problem of simultaneously retrieving WQ indicators, including chlorophyll-a (Chla), Total Suspended Solids (TSS), and the absorption by Colored Dissolved Organic Matter at 440 nm (acdom(440)), across a wide array of aquatic ecosystems. We use a database of in situ measurements to train and optimize MDN models developed for the relevant spectral measurements (400–800 nm) of the Operational Land Imager (OLI), MultiSpectral Instrument (MSI), and Ocean and Land Color Instrument (OLCI) aboard the Landsat-8, Sentinel-2, and Sentinel-3 missions, respectively. Our two performance assessment approaches, namely hold-out and leave-one-out, suggest significant, albeit varying degrees of improvements with respect to second-best algorithms, depending on the sensor and WQ indicator (e.g., 68%, 75%, 117% improvements based on the hold-out method for Chla, TSS, and acdom(440), respectively from MSI-like spectra). Using these two assessment methods, we provide theoretical upper and lower bounds on model performance when evaluating similar and/or out-of-sample datasets. To evaluate multi-mission product consistency across broad spatial scales, map products are demonstrated for three near-concurrent OLI, MSI, and OLCI acquisitions. Overall, estimated TSS and acdom(440) from these three missions are consistent within the uncertainty of the model, but Chla maps from MSI and OLCI achieve greater accuracy than those from OLI. By applying two different atmospheric correction processors to OLI and MSI images, we also conduct matchup analyses to quantify the sensitivity of the MDN model and best-practice algorithms to uncertainties in reflectance products. Our model is less or equally sensitive to these uncertainties compared to other algorithms. Recognizing their uncertainties, MDN models can be applied as a global algorithm to enable harmonized retrievals of Chla, TSS, and acdom(440) in various aquatic ecosystems from multi-source satellite imagery. Local and/or regional ML models tuned with an apt data distribution (e.g., a subset of our dataset) should nevertheless be expected to outperform our global model
Characterization of Sea Surface Microlayer and Marine Aerosol Organic Composition Using STXM-NEXAFS Microscopy and FTIR Spectroscopy
Get PDFAtmospheric submicron particles, generated primary marine aerosol (gPMA), seawater (depth 5 m), and sea surface microlayer (depth ∼0.001 m) samples were collected
during the North Atlantic Aerosol and Marine Ecosystem Study (NAAMES) on the R/V Atlantis in September 2017 and March 2018 and analyzed by scanning transmission X-ray
microscopy with near-edge X-ray absorption fine structure (STXM-NEXAFS) and Fourier transform infrared (FTIR) spectroscopy. Three organic functional groups (hydroxyl, alkane, and amine) were present in all sample types, with the hydroxyl group typically being 50−90% of the quantified organic mass concentration. Microlayer and atmospheric particle samples both had a larger range of hydroxyl group to alkane group mass ratios than either seawater or
gPMA. These measurements suggest that the sea surface microlayer organic composition contributes to the range of submicron atmospheric aerosol organic functional group
composition. Atmospheric and microlayer sample alkane/hydroxyl group ratio variations were
also associated with tracers of seawater biological activity, including chlorophyll and net primary production. Seawater and gPMA samples had relatively constant organic functional group composition for all of the sampled
locations and varying phytoplankton activity conditions, suggesting that they are associated with the more consistent nature of dissolved organic carbon fraction that is ubiquitous in seawater. Eight k-means clusters of STXM-NEXAFS particles were identified from the spectra for all four sample types (atmospheric aerosol particles, gPMA, seawater, and microlayer), which showed that all four sample types had particles in four of the clusters and seven of the eight clusters included a mixture of sample types. These
STXM-NEXAFS results support the FTIR measurements by showing consistent organic particle clusters across the four sample types as well as heterogeneity within each type
How do plankton species coexist in an apparently unstructured environment?
No full textIn a paper entitled The paradox of the plankton, Hutchinson asked 'how it is possible for a number of species to coexist in a relatively isotropic or unstructured environment all competing for the same sorts of materials' (Hutchinson 1961 Am. Nat. 95, 137–145 (doi:10.1086/282171)). Particularly relevant for phytoplankton, this paradox was based on two implicit, and perhaps naive, postulates, i.e. (i) that all plankton species have similar requirements and (ii) that the marine environment is relatively homogeneous in space and time. A number of hypotheses, based on purely theoretical or experimental studies, have been proposed to solve this conundrum, ranging from spatio-temporal environmental heterogeneity to biotic chaotic variability. Here, we characterize the ecological niche of 117 plankton species belonging to three different taxonomic groups and show that all species have a niche sufficiently distinct to ensure coexistence in a structured marine environment. We also provide evidence that pelagic habitats are, unsurprisingly, more diverse in space and time than Hutchinson imagined, the marine environment being neither unstructured nor stable in space and time. We, therefore, conclude that the niche theory, and its corollary the principle of competitive exclusion, apply as much for the plankton as for other forms of life, be they terrestrial or marine
Chloroplasts play a central role in facilitating MAMP-triggered immunity, pathogen suppression of immunity and crosstalk with abiotic stress
Get PDFMicrobe-associated molecular pattern (MAMP)-triggered immunity (MTI) research has traditionally centred around signal transduction pathways originating from activated membrane-localized pattern recognition receptors (PRRs), culminating in nuclear transcription and posttranslational modifications. More recently, chloroplasts have emerged as key immune signalling hubs, playing a central role in integrating environmental signals. Notably, MAMP recognition induces chloroplastic reactive oxygen species (cROS) that is suppressed by pathogen effectors, which also modify the balance of chloroplast-synthesized precursors of the defence hormones, jasmonic acid, salicylic acid (SA) and abscisic acid. This study focuses on how well-characterized PRRs and coreceptors modulate chloroplast physiology, examining whether diverse signalling pathways converge to similarly modulate chloroplast function. Pretreatment of receptor mutant plants with MAMP and D(Damage)AMP peptides usually protect against effector modulation of chlorophyll fluorescence and prevent Pseudomonas syringae effector-mediated quenching of cROS and suppression of maximum dark-adapted quantum efficiency (the ratio of variable/maximum fluorescence [Fv/Fm]). The MTI coreceptor double mutant, bak1-5/bkk1-1, exhibits a remarkable decrease in Fv/Fm compared to control plants during infection, underlining the importance of MTI-mediated signalling in chloroplast immunity. Further probing the role of the chloroplast in immunity, we unexpectedly found that even moderate changes in light intensity can uncouple plant immune signalling
Red Pigmentation Can Be Used to Reliably Distinguish Between Live Calanus finmarchicus and Calanus glacialis Females in the Fram Strait
Get PDFCopepods from the genus Calanus provide an important lipid-rich food source in the Arctic marine foodweb. Despite extensive research on Calanus finmarchicus and Calanus
glacialis, accurately identifying adults to species level remains challenging due to similar morphologies. Although these species co-occur in many regions, the distribution of
C. finmarchicus and C. glacialis correspond to Atlantic and Arctic water masses respectively and are frequently used as climate indicators. Correct identification is therefore vital for understanding the phenotypic plasticity of these species and the impacts climate change will have on Calanus-dominated marine ecosystems. In this study, prosome length and percentage of red pigmentation (redness) of genital somites, the antennae, and throughout the whole body were determined for 139 females of C. finmarchicus and C. glacialis from the Fram Strait. Molecular analysis of a 16S rDNA barcode confirmed that the best morphological features for resolving the identity of these two species were the redness of the antennae and the redness of the genital somites. Overall accuracy of using antennae redness and genital somite redness to discriminate between the two species were the same, yet each of these explanatory variables had different specificity; C. finmarchicus were more accurately identified by the absence of redness in the genital somites, whereas C. glacialis were more accurately identified using antennae redness. Given the ecological importance of these congeners, these findings contribute to a better understanding of the reliability of using morphological characteristics to identify Calanus to species level, especially when sorting live specimens for climate related ecological experiments
