Tidal stirring and phytoplankton bloom dynamics in an estuary

A decade of observation in South San Francisco Bay demonstrates that estuarine phytoplankton biomass fluctuates at the time scale of days to weeks, and that much of this variability is associated with fluctuations in tidal energy. During the spring seasons of every year from 1980–1990, episodic blooms occurred in which phytoplankton biomass rose from a baseline of 2–4 mg chlorophyll a m–3, peaked at 20–40 mg chlorophyll a m–3, and then returned to baseline values, all within several weeks. Each episode of biomass increase occurred during neap tides, and each bloom decline coincided with spring tides. This suggests that daily variations in the rate of vertical mixing by tidal stirring might control phytoplankton bloom dynamics in some estuaries. Simulation experiments with a numerical model of phytoplankton population dynamics support this hypothesis. The model incorporates biological processes (light-dependent growth, zooplankton grazing, benthic grazing) and physical processes (sinking, vertical mixing) as controls on the biomass distribution of phytoplankton in a 10-m water column. Numerical simulations indicate that phytoplankton dynamics are highly sensitive to the rate of vertical mixing (parameterized as an eddy diffusivity Kz), such that biomass increases rapidly at small Kz (5 m2 d–1), but not at large Kz (50 m2 d–1). Cyclic variation of Kz between 5 and 50 over a 14-d period (simulated neap-spring cycle) yields simulation results that are similar to bloom events observed in this estuary

Eutrophication

Eutrophication is a syndrome of [../152248/index.html ecosystem]] responses to human activities that fertilize water bodies with nitrogen (N) and phosphorus (P), often leading to changes in animal and plant populations and degradation of water and habitat quality. Nitrogen and phosphorus are essential components of structural proteins, enzymes, cell membranes, nucleic acids, and molecules that capture and utilize light and chemical energy to support life. The biologically available forms of N and P are present at low concentrations in pristine lakes, rivers, estuaries, and in vast regions of the upper ocean.https://inspire.redlands.edu/oh_chapters/1160/thumbnail.jp

Primary Production in the Delta: Then and Now

To evaluate the role of restoration in the recovery of the Delta ecosystem, we need to have clear targets and performance measures that directly assess ecosystem function. Primary production is a crucial ecosystem process, which directly limits the quality and quantity of food available for secondary consumers such as invertebrates and fish. The Delta has a low rate of primary production, but it is unclear whether this was always the case. Recent analyses from the Historical Ecology Team and Delta Landscapes Project provide quantitative comparisons of the areal extent of 14 habitat types in the modern Delta versus the historical Delta (pre-1850). Here we describe an approach for using these metrics of land use change to: (1) produce the first quantitative estimates of how Delta primary production and the relative contributions from five different producer groups have been altered by large-scale drainage and conversion to agriculture; (2) convert these production estimates into a common currency so the contributions of each producer group reflect their food quality and efficiency of transfer to consumers; and (3) use simple models to discover how tidal exchange between marshes and open water influences primary production and its consumption. Application of this approach could inform Delta management in two ways. First, it would provide a quantitative estimate of how large-scale conversion to agriculture has altered the Delta\u27s capacity to produce food for native biota. Second, it would provide restoration practitioners with a new approach—based on ecosystem function—to evaluate the success of restoration projects and gauge the trajectory of ecological recovery in the Delta region

Coupled effects of vertical mixing and benthic grazing on phytoplankton populations in shallow, turbid estuaries

Coastal ocean waters tend to have very different patterns of phytoplankton biomass variability from the open ocean, and the connections between physical variability and phytoplankton bloom dynamics are less well established for these shallow systems. Predictions of biological responses to physical variability in these environments is inherently difficult because the recurrent seasonal patterns of mixing are complicated by aperiodic fluctuations in river discharge and the high-frequency components of tidal variability. We might expect, then, less predictable and more complex bloom dynamics in these shallow coastal systems compared with the open ocean. Given this complex and dynamic physical environment, can we develop a quantitative framework to define the physical regimes necessary for bloom inception, and can we identify the important mechanisms of physical-biological coupling that lead to the initiation and termination of blooms in estuaries and shallow coastal waters? Numerical modeling provides one approach to address these questions. Here we present results of simulation experiments with a refined version of Cloern\u27s (1991) model in which mixing processes are treated more realistically to reflect the dynamic nature of turbulence generation in estuaries. We investigated several simple models for the turbulent mixing coefficient. We found that the addition of diurnal tidal variation to Cloern\u27s model greatly reduces biomass growth indicating that variations of mixing on the time scale of hours are crucial. Furthermore, we found that for conditions representative of South San Francisco Bay, numerical simulations only allowed for bloom development when the water column was stratified and when minimal mixing was prescribed in the upper layer. Stratification, however, itself is not sufficient to ensure that a bloom will develop: minimal wind stirring is a further prerequisite to bloom development in shallow turbid estuaries with abundant populations of benthic suspension feeders

On the human appropriation of wetland primary production

Humans are changing the Earth\u27s surface at an accelerating pace, with significant consequences for ecosystems and their biodiversity. Landscape transformation has far-reaching implications including reduced net primary production (NPP) available to support ecosystems, reduced energy supplies to consumers, and disruption of ecosystem services such as carbon storage. Anthropogenic activities have reduced global NPP available to terrestrial ecosystems by nearly 25%, but the loss of NPP from wetland ecosystems is unknown. We used a simple approach to estimate aquatic NPP from measured habitat areas and habitat-specific areal productivity in the largest wetland complex on the USA west coast, comparing historical and modern landscapes and a scenario of wetland restoration. Results show that a 77% loss of wetland habitats (primarily marshes) has reduced ecosystem NPP by 94%, C (energy) flow to herbivores by 89%, and detritus production by 94%. Our results also show that attainment of habitat restoration goals could recover 12% of lost NPP and measurably increase carbon flow to consumers, including at-risk species and their food resources. This case study illustrates how a simple approach for quantifying the loss of NPP from measured habitat losses can guide wetland conservation plans by establishing historical baselines, projecting functional outcomes of different restoration scenarios, and establishing performance metrics to gauge success

Does the Sverdrup critical depth model explain bloom dynamics in estuaries?

In this paper we use numerical models of coupled biological-hydrodynamic processes to search for general principles of bloom regulation in estuarine waters. We address three questions: What are the dynamics of stratification in coastal systems as influenced by variable freshwater input and tidal stirring? How does phytoplankton growth respond to these dynamics? Can the classical Sverdrup Critical Depth Model (SCDM) be used to predict the timing of bloom events in shallow coastal domains such as estuaries? We present results of simulation experiments which assume that vertical transport and net phytoplankton growth rates are horizontally homogeneous. In the present approach the temporally and spatially varying turbulent diffusivities for various stratification scenarios are calculated using a hydrodynamic code that includes the Mellor-Yamada 2.5 turbulence closure model. These diffusivities are then used in a time- and depth-dependent advection-diffusion equation, incorporating sources and sinks, for the phytoplankton biomass. Our modeling results show that, whereas persistent stratification greatly increases the probability of a bloom, semidiurnal periodic stratification does not increase the likelihood of a phytoplankton bloom over that of a constantly unstratified water column. Thus, for phytoplankton blooms, the physical regime of periodic stratification is closer to complete mixing than to persistent stratification. Furthermore, the details of persistent stratification are important: surface layer depth, thickness of the pycnocline, vertical density difference, and tidal current speed all weigh heavily in producing conditions which promote the onset of phytoplankton blooms. Our model results for shallow tidal systems do not conform to the classical concepts of stratification and blooms in deep pelagic systems. First, earlier studies (Riley, 1942, for example) suggest a monotonic increase in surface layer production as the surface layer shallows. Our model results suggest, however, a nonmonotonic relationship between phytoplankton population growth and surface layer depth, which results from a balance between several \u27\u27competing\u27\u27 processes, including the interaction of sinking with turbulent mixing and average net growth occurring within the surface layer. Second, we show that the traditional SCDM must be refined for application to energetic shallow systems or for systems in which surface layer mixing is not strong enough to counteract the sinking loss of phytoplankton. This need for refinement arises because of the leakage of phytoplankton from the surface layer by turbulent diffusion and sinking, processes not considered in the classical SCDM. Our model shows that, even for low sinking rates and small turbulent diffusivities, a significant percentage of the phytoplankton biomass produced in the surface layer can be lost by these processes

Bivalve Grazing Can Shape Phytoplankton Communities

The ability of bivalve filter feeders to limit phytoplankton biomass in shallow waters is well-documented, but the role of bivalves in shaping phytoplankton communities is not. The coupled effect of bivalve grazing at the sediment-water interface and sinking of phytoplankton cells to that bottom filtration zone could influence the relative biomass of sinking (diatoms) and non-sinking phytoplankton. Simulations with a pseudo-2D numerical model showed that benthic filter feeding can interact with sinking to alter diatom:non-diatom ratios. Cases with the smallest proportion of diatom biomass were those with the fastest sinking speeds and strongest bivalve grazing rates. Hydrodynamics modulated the coupled sinking-grazing influence on phytoplankton communities. For example, in simulations with persistent stratification, the non-sinking forms accumulated in the surface layer away from bottom grazers while the sinking forms dropped out of the surface layer toward bottom grazers. Tidal-scale stratification also influenced vertical gradients of the two groups in opposite ways. The model was applied to Suisun Bay, a low-salinity habitat of the San Francisco Bay system that was transformed by the introduction of the exotic clam Potamocorbula amurensis. Simulation results for this Bay were similar to (but more muted than) those for generic habitats, indicating that P. amurensis grazing could have caused a disproportionate loss of diatoms after its introduction. Our model simulations suggest bivalve grazing affects both phytoplankton biomass and community composition in shallow waters. We view these results as hypotheses to be tested with experiments and more complex modeling approaches

Food Quality and Phytoplankton Community Composition in San Francisco Bay using Imaging Spectroscopy Data from the California HyspIRI Airborne Campaign

The San Francisco Bay (SFB) is the largest estuary on the west coast of the United States. It is an important transition zone between marine, freshwater, and inland terrestrial watersheds. The SFB is an important region for the cycling of nutrients and pollutants and it supports nurseries of ecologically and commercially important fisheries, including some threatened species. Phytoplankton community structure influences food web dynamics, and the taxonomy of the phytoplankton may be more important in determining primary food quality than environmental factors. As such, estimating food quality from phytoplankton community composition can be a robust tool to understand trophic transfer of energy. Recent work explores phytoplankton food quality in SFB through the use of microscopy and phytoplankton chemotaxonomy to evaluate how changes in phytoplankton composition may have influenced the recent trophic collapse of pelagic fishes in the northern part of the SFB. The objective of this study is to determine if the approach can also be applied to imaging spectroscopy data in order to quantify phytoplankton food quality from space. Imaging spectroscopy data of SFB from the Airborne VisibleInfrared Imaging Spectrometer (AVIRIS) was collected during the Hyperspectral Infrared (HyspIRI) Airborne Campaign in California (2013 2015) and used in this study. Estimates of ocean chlorophyll and phytoplankton community structure were determined using standard ocean chlorophyll algorithms and the PHYtoplankton Detection with Optics (PHYDOTax) algorithms. These were validated using in situ observations of phytoplankton composition using microscopic cell counts and phytoplankton chemotaxonomy from the US Geological Surveys ship surveys of the SFB. The findings from this study may inform the use of future high spectral resolution satellite sensors with the spatial resolution appropriate for coastal systems (e.g., HyspIRI) to assess food quality from space

The annual cycles of phytoplankton biomass

Terrestrial plants are powerful climate sentinels because their annual cycles of growth, reproduction and senescence are finely tuned to the annual climate cycle having a period of one year. Consistency in the seasonal phasing of terrestrial plant activity provides a relatively low-noise background from which phenological shifts can be detected and attributed to climate change. Here, we ask whether phytoplankton biomass also fluctuates over a consistent annual cycle in lake, estuarine–coastal and ocean ecosystems and whether there is a characteristic phenology of phytoplankton as a consistent phase and amplitude of variability. We compiled 125 time series of phytoplankton biomass (chlorophyll a concentration) from temperate and subtropical zones and used wavelet analysis to extract their dominant periods of variability and the recurrence strength at those periods. Fewer than half (48%) of the series had a dominant 12-month period of variability, commonly expressed as the canonical spring-bloom pattern. About 20 per cent had a dominant six-month period of variability, commonly expressed as the spring and autumn or winter and summer blooms of temperate lakes and oceans. These annual patterns varied in recurrence strength across sites, and did not persist over the full series duration at some sites. About a third of the series had no component of variability at either the six- or 12-month period, reflecting a series of irregular pulses of biomass. These findings show that there is high variability of annual phytoplankton cycles across ecosystems, and that climate-driven annual cycles can be obscured by other drivers of population variability, including human disturbance, aperiodic weather events and strong trophic coupling between phytoplankton and their consumers. Regulation of phytoplankton biomass by multiple processes operating at multiple time scales adds complexity to the challenge of detecting climate-driven trends in aquatic ecosystems where the noise to signal ratio is high