Diagnosing seasonal to multi-decadal phytoplankton group dynamics in a highly productive coastal ecosystem

Catlett, Dylan; Siegel, David A.; Simons, Rachel D.; Guillocheau, Nathalie; Henderikx-Freitas, Fernanda; Thomas, Crystal

doi:10.1016/j.pocean.2021.102637

Cited by 14 publications

(14 citation statements)

References 81 publications

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“…Previous studies have highlighted the strong cross-shore gradients in community structure in the SCC, primarily through the use of general indices 18 , 59 (such as the ratio of autotrophic carbon to chlorophyll a ) or select groups of bacteria 18 , phytoplankton 15 , 16 , 18 , 19 , 60 and zooplankton 61 . The results generated from this study support and expand upon many of the findings from these previous studies.…”

Section: Discussionmentioning

confidence: 99%

“…Previous studies have shown that phytoplankton and zooplankton communities vary along these gradients 16 – 18 . Furthermore, changes in seasonal nearshore upwelling are thought to drive distinct differences in phytoplankton and zooplankton assemblages across the region with variation occurring on seasonal, interannual (El Niño/La Niña), and multidecadal (Pacific Decadal Oscillation) time frames 19 , 20 . Within the microbial community however, the bulk of knowledge exists at a broad level across taxonomic and or functional groups, masking the effects of environmental perturbation within these broad groups and completely missing “cryptic” groups that cannot be identified with more traditional methods (such as bacterial and archaeal groups).…”

Section: Introductionmentioning

confidence: 99%

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Influence of nutrient supply on plankton microbiome biodiversity and distribution in a coastal upwelling region

et al. 2022

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The ecological and oceanographic processes that drive the response of pelagic ocean microbiomes to environmental changes remain poorly understood, particularly in coastal upwelling ecosystems. Here we show that seasonal and interannual variability in coastal upwelling predicts pelagic ocean microbiome diversity and community structure in the Southern California Current region. Ribosomal RNA gene sequencing, targeting prokaryotic and eukaryotic microbes, from samples collected seasonally during 2014-2020 indicate that nitracline depth is the most robust predictor of spatial microbial community structure and biodiversity in this region. Striking ecological changes occurred due to the transition from a warm anomaly during 2014-2016, characterized by intense stratification, to cooler conditions in 2017-2018, representative of more typical upwelling conditions, with photosynthetic eukaryotes, especially diatoms, changing most strongly. The regional slope of nitracline depth exerts strong control on the relative proportion of highly diverse offshore communities and low biodiversity, but highly productive nearshore communities.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Influence of nutrient supply on plankton microbiome biodiversity and distribution in a coastal upwelling region

et al. 2022

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“…" position focus on improving the skill of global algorithms in regional oceans, creating more quantitative test records. These efforts include repeated surveys to compare absorption spectra of blooms dominated by different taxa, including diatoms, dinoflagellates, and others, in artificial controls and natural assemblages in the Santa Barbara Chanel and the Florida Keys (Figure 3a,b; see also Catlett and Siegel, 2018;Montes et al, 2020;Catlett et al, 2021). Plankton types can be distinguished in natural assemblages by investigating spectral derivatives (Figure 3b) that reveal signals from accessory pigments of different groups.…”

Section: Methods 1 Phytoplankton Community Composition Derived From Remote-sensing Reflectancementioning

confidence: 99%

“…Surface flow fields from altimetry and high-frequency (HF) radar can supply advective information to delineate features or assess underlying processes (Messié and Chavez, 2017;Matson et al, 2019;Catlett et al, 2021). For example, LCSs, areas of attraction and repulsion around frontal features, define fluid dynamical niches and contribute to regional biodiversity (d 'Ovidio et al, 2004;Cotté et al, 2011;Scales et al, 2018;Watson et al, 2018).…”

Section: Methods 3 Pelagic Seascape Ecology: Tracking Dynamic Features and Habitatsmentioning

confidence: 99%

“…For example, LCSs, areas of attraction and repulsion around frontal features, define fluid dynamical niches and contribute to regional biodiversity (d 'Ovidio et al, 2004;Cotté et al, 2011;Scales et al, 2018;Watson et al, 2018). MBON researchers used a combination of ocean color and HF radar to partition the spatial from the temporal part of unique coccolithophorid blooms in the Santa Barbara Channel (Matson et al, 2019) and to attribute changes in dinoflagellate dominance to anomalous advection associated with long-term shifts in the North Pacific Gyre (Catlett et al, 2021). Mesoscale features can increase the spatial heterogeneity of water masses but also enhance dispersal (Clayton et al, 2013), structuring habitat for zooplankton and larval fishes (e.g., Govoni et al, 2010), mesopelagic communities (Della Penna and Gaube, 2020), and phytoplankton (Schulien et al, 2020), and attracting foragers or predators with diverse foraging strategies (Oliver et al, 2019).…”

Section: Methods 3 Pelagic Seascape Ecology: Tracking Dynamic Features and Habitatsmentioning

confidence: 99%

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Satellite Remote Sensing and the Marine Biodiversity Observation Network: Current Science and Future Steps

Kavanaugh¹,

Bell²,

Catlett³

et al. 2021

Oceanog

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Coastal ecosystems are rapidly changing due to human-caused global warming, rising sea level, changing circulation patterns, sea ice loss, and acidification that in turn alter the productivity and composition of marine biological communities. In addition, regional pressures associated with growing human populations and economies result in changes in infrastructure, land use, and other development; greater extraction of fisheries and other natural resources; alteration of benthic seascapes; increased pollution; and eutrophication. Understanding biodiversity is fundamental to assessing and managing human activities that sustain ecosystem health and services and mitigate humankind’s indiscretions. Remote-sensing observations provide rapid and synoptic data for assessing biophysical interactions at multiple spatial and temporal scales and thus are useful for monitoring biodiversity in critical coastal zones. However, many challenges remain because of complex bio-optical signals, poor signal retrieval, and suboptimal algorithms. Here, we highlight four approaches in remote sensing that complement the Marine Biodiversity Observation Network (MBON). MBON observations help quantify plankton functional types, foundation species, and unique species habitat relationships, as well as inform species distribution models. In concert with in situ observations across multiple platforms, these efforts contribute to monitoring biodiversity changes in complex coastal regions by providing oceanographic context, contributing to algorithm and indicator development, and creating linkages between long-term ecological studies, the next generations of satellite sensors, and marine ecosystem management.

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Integrating phytoplankton pigment andDNAmeta‐barcoding observations to determine phytoplankton composition in the coastal ocean

Catlett

Siegel

Matson

et al. 2022

Limnology & Oceanography

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Quantifying phytoplankton composition is critical to predicting marine ecosystem structure and function. DNA meta‐barcoding and high‐performance liquid chromatography (HPLC) pigment analysis are two widely used methods for assessing phytoplankton composition; however, comparing their performance has been done only rarely. Here, we integrate DNA meta‐barcoding and HPLC pigment observations to determine eukaryotic phytoplankton composition in the Santa Barbara Channel, California. We find that both methods identify the same four dominant eukaryotic phytoplankton taxa (diatoms, dinoflagellates, chlorophytes, and prymnesiophytes), but inter‐ and intra‐lineage variability in biomarker pigmentation (associated with both a lack of taxonomic specificity of biomarker pigments and intrinsic differences in accessory pigmentation) drives substantial disagreement between the methods. Covariation network analysis circumvents this disagreement and reveals that diverse assemblages of phytoplankton and other protists covary with distinct suites of biomarker pigments. Our results highlight the strengths and weaknesses of each method in characterizing phytoplankton composition and reveal novel insights into phytoplankton physiology that could only be gained by integrating the two methods. Finally, we suggest a path to monitor eukaryotic plankton communities on unprecedented spatiotemporal scales based on the covariation of unique phytoplankton and protistan assemblages with remotely sensible phytoplankton pigment concentrations.

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Diagnosing seasonal to multi-decadal phytoplankton group dynamics in a highly productive coastal ecosystem

Cited by 14 publications

References 81 publications

Influence of nutrient supply on plankton microbiome biodiversity and distribution in a coastal upwelling region

Influence of nutrient supply on plankton microbiome biodiversity and distribution in a coastal upwelling region

Satellite Remote Sensing and the Marine Biodiversity Observation Network: Current Science and Future Steps

Integrating phytoplankton pigment andDNAmeta‐barcoding observations to determine phytoplankton composition in the coastal ocean

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