Mesoscale variability related to iron speciation in a coastal Ross Sea area (Antarctica) during summer 2014

Dissolved iron (Fe) distribution and speciation was determined in water samples (0–200?m) collected in a coastal area near Terra Nova Bay during the austral summer of 2014. Nutrients, dissolved oxygen, chlorophyll-a, phytoplankton composition and prokaryotic biomass distribution were investigated in combination with measurements of the physical properties of the water columns and its dynamics. The dFe value was above the limiting growth concentration, ranging from 0.52 to 4.51?nM, and it showed a spatial variability with a horizontal length scale of about 10?km, according to the variability of the water column physical properties and to iron sources. The organic ligands (L) maintained the concentrations of dFe at levels much higher than the inorganic solubility of Fe, keeping it available for phytoplankton and the log K’_FeL values found (from 22.1 to 23.6) highlighted the presence of complexes of differing stabilities.     

An adjoint data assimilation approach for estimating parameters in a three-dimensional ecosystem model

In this paper an ecosystem model, including phytoplankton, zooplankton, nitrate, ammonium, phosphate and detritus, is described. The model is driven by physical fields derived from a three-dimensional physical transport model. Simulation includes nitrate input from a river. Simulated results are then sampled and the sampled data are used in sequential numerical experiments to assess the ability of using an adjoint data assimilation approach for estimating the poorly known parameters of the ecosystem model, such as growth and death rate, half-saturation constant of nutrients, etc. Data with different spatial and temporal resolution over 1 week are assimilated into the ecosystem model. Assimilation of data at 30 grid stations with a sampling interval of 6 h is proved to be adequate for recovering all the parameters of the ecosystem model. Both the spatial and temporal resolution of the data are mutually complementary in the assimilative model. Thus, improvement of either of them can result in improvement of model parameter recoveries. The assimilation of phytoplankton data is essential to recover the model parameters. Phytoplankton is the core of the food web and without the information on phytoplankton, the structure of the ecosystem cannot be constructed correctly. The adjoint method can work well with the noisy data. In the twin experiments with noisy data, the parameters can be recovered but the error is increased. The results of the model and parameter recovery are sensitive to the initial conditions of state variables, so the determination of the initial condition is as important as that of the model parameter. The spatial and temporal resolution and the data type of the observations in Analysis and Modelling Research of the Ecosystem in the Bohai Sea (AMREB) are suitable for the recovery of the model parameters used in this study.     

An individual-based analysis of the dynamics of two coexisting phytoplankton species in the mixed layer

In marine ecosystems ecological and environmental conditions continuously change, possibly supporting the wide range of phytoplankton species coexisting in aquatic environments. Phytoplankton communities are not homogeneously distributed in the water column due to the spatial and temporal variability of turbulent mixing and the concurrent biological response. In this paper an individual-based model (Lagrangian method) simulating the basic physiology of two coexisting phytoplankton species has been developed. The species, sharing the same availability of light and nutrient resource, are characterized by different photo-physiological parameters. The spatial and temporal evolution of turbulent mixing is simulated introducing vertical profiles of measured eddy diffusivity. Three case studies have been examined to analyze the role of environment–individual interactions in determining bloom conditions for both the selected species. The organisms experience recurrent fluctuations of light, temperature, and nutrient concentration gradients, due to the turbulent mixing in the water column, which have significant effects on the growth of the phytoplankton species. In all the numerical experiments, the temporal and spatial variability of different forcings do not support the prevalence of one species over the other over the time scale typical of a phytoplankton bloom.A well mixed water column favours the growth of both the populations while a variable mixing regime limits their growth reducing the photophysiological differences between the species.     

Variabilité à court terme du phytoplancton de l'estuaire du Saint-Laurent

Within the framework of a general study on time variability in the St. Lawrence Estuary, phytoplankton was sampled at 10-min intervals for 13 h. A symmetric, cosine, tapered filter was applied to the time series of cell counts, the cyclical trend being interpreted in relation to physical variables. Autocorrelation coefficients were computed for residual phytoplankton data, in order to investigate fine-scale effects in the series. The main pattern (trend) of variation in the phytoplankton data had a frequency of about 1 cycle/190 min and was related to physical variations, following therefore the tidal movement of water masses in the estuary. On the other hand, the correlogram suggests that phytoplankton is aggregated in patches of an average diameter of about 0.75 km.

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Contribution au programme du Groupe interuniversitaire de recherches océanographiques du Québec (GIROQ).     

Modelling the horizontal spatial structure of planktonic community in Lake Trasimeno (Umbria,Italy) using multivariate geostatistical methods

Spatial heterogeneity of ecological systems has been recognised in recent years as an important ecological feature of an ecosystem, rather than a mere statistical nuisance. However, although considerable interest has been paid to the development of statistical methods for the analysis of spatial environmental data, when in presence of more species or environmental variables common analyses still fail to recognise the necessity of a joint modelling of the whole correlation structure. In this paper, we propose to study the multivariate spatial autocorrelation of a plankton community by making explicit reference to a spatial linear factor model entailing a set of constraints for the spatial structure of the planktonic species. The data set examined come from an intensive 2-day sampling survey performed in July 1991 on Lake Trasimeno (Italy) to investigate the horizontal spatial heterogeneity and distribution of the planktonic community, from small (50 m) to large (1000–10,000 m) scale. The analysis revealed that zooplankton and phytoplankton essentially have different degrees of heterogeneity and different spatial structures which required separate modelling. On the other hand, the similarity of the spatial autocorrelation found within zooplankton and phytoplankton communities, indicates that at the investigated scales of observation the horizontal organisation of both components is not appreciably affected by species-specific behaviours. The analysis of the multivariate spatial patterns emerging from the mapping of the extracted factors suggested an interpretation of the distribution of macrozooplankton and phytoplankton assemblages in terms of planktonic responses to environmental factors of a lake-size scale.     

Modelling the temperature and the phytoplankton distributions at the Aveiro near coastal zone,Portugal

The main objective of this paper is to implement a coupled three-dimensional physical and ecological model for the Aveiro coast, and to apply it to study the temperature and the phytoplankton biomass spatial distributions along the coastal ecosystem. The Aveiro coast is located at Portugal within the upwelling system of the Atlantic Iberian coast, characterized by nutrients availability and phytoplankton biomass accumulation, from April to October. In order to implement the ecological model, its validation was assessed by comparing simulations to data relative to the horizontal and vertical distributions of the temperature, nutrients and phytoplankton biomass, obtained during the CICLOS I survey off the Portuguese coast [Moita, M.T., 2001. Estrutura, Variabilidade e Dinâmica do Fitoplâncton na Costa de Portugal Continental. PhD Thesis. Faculdade de Ciências da Universidade de Lisboa, 272 pp.]. A sensitivity analysis of the model has been performed in order to assess the influence of the main ecological model variables. The simulation results show that the model is capable of predicting realistic the temperature, the nutrients and the chlorophyll-a distributions for the study area. The scenarios evidence the setup of a thermal stratification pattern resulting from the upwelling of deep and rich in nutrients water to the surface layer and a chlorophyll-a maxima extending offshore, along the picnocline and the nutricline. The results confirm the crucial role played by the physical processes in the phytoplankton bloom along the Aveiro coast. They also evidence the close link between the surface phytoplankton distribution and the surface temperature distribution.     

Fine-scale habitat modeling of a top marine predator: do prey data improve predictive capacity?

Predators and prey assort themselves relative to each other, the availability of resources and refuges, and the temporal and spatial scale of their interaction. Predictive models of predator distributions often rely on these relationships by incorporating data on environmental variability and prey availability to determine predator habitat selection patterns. This approach to predictive modeling holds true in marine systems where observations of predators are logistically difficult, emphasizing the need for accurate models. In this paper, we ask whether including prey distribution data in fine-scale predictive models of bottlenose dolphin (Tursiops truncatus) habitat selection in Florida Bay, Florida, U.S.A., improves predictive capacity. Environmental characteristics are often used as predictor variables in habitat models of top marine predators with the assumption that they act as proxies of prey distribution. We examine the validity of this assumption by comparing the response of dolphin distribution and fish catch rates to the same environmental variables. Next, the predictive capacities of four models, with and without prey distribution data, are tested to determine whether dolphin habitat selection can be predicted without recourse to describing the distribution of their prey. The final analysis determines the accuracy of predictive maps of dolphin distribution produced by modeling areas of high fish catch based on significant environmental characteristics. We use spatial analysis and independent data sets to train and test the models. Our results indicate that, due to high habitat heterogeneity and the spatial variability of prey patches, fine-scale models of dolphin habitat selection in coastal habitats will be more successful if environmental variables are used as predictor variables of predator distributions rather than relying on prey data as explanatory variables. However, predictive modeling of prey distribution as the response variable based on environmental variability did produce high predictive performance of dolphin habitat selection, particularly foraging habitat.     

Development of categorical mapping for quantitative assessment of eutrophication

Spatial distribution of nutrient and phytoplankton variables is often illustrated using categorical mapping for each variable. However, the assessment of eutrophication cannot be derived from a single parameter since a synthesis of the environmental variables related to eutrophication is required. These shortcomings are further complicated since it is difficult to discriminate between distinct trophic states along natural environmental gradients. In the present work, a methodological procedure for quantitative assessment of eutrophication at a spatial scale was examined in the Gulf of Saronicos, Greece, based on a thematic map generated from the synthesis of four variables characterising eutrophication. The categorical map of each variable was developed using the Kriging interpolation method and four trophic levels were indicated (eutrophic, upper-mesotrophic, lower-mesotrophic and oligotrophic) based on nutrient and phytoplankton concentration scaling. Multi-criteria choice methods were applied to generate a final categorical map showing the four trophic levels in the area. This synthesis of categorical maps for assessing eutrophication at a spatial scale is proposed as a methodological procedure appropriate for coastal management studies.     

Exploration of spatial morphometry and socioeconomic variables in modelling urban land use change

The incorporation of land use (LU) data with socioeconomic data is a main issue in modelling. This is as a result of difference in data model and scale. This study proposed and tested the change–pattern approach, which allows the incorporation of these data sets in modelling LU change. Focusing on LU dynamics for a selected part of the Thames Gateway within the City of London, the approach tested two different methods of input selection for the modelling operations. Variables selected from these two methods serve as inputs into several neural networks tested in order to identify the direction of change for each of the LU types within the study area. The result shows that direction of LU change across the study area could be identified when spatial morphology of the area and socioeconomic variables are considered. Some classes of change could be identified fairly accurately using landscape metrics indicating level of fragmentation, extent of LU patches, shape complexity of LU patches in combination with some socioeconomic variables.     

A simple plankton model for the Oregon upwelling ecosystem: Sensitivity and validation against time-series ocean data

Simple plankton models serve as useful platforms for testing our understanding of the mechanisms underlying ecosystem dynamics. A simple, one-dimensional plankton model was developed to describe the dynamics of nitrate, ammonium, two phytoplankton size-classes, meso-zooplankton, and detritus in the Oregon upwelling ecosystem. Computational simplicity was maintained by linking the biological model to a one-dimensional, cross-shelf physical model driven by the daily coastal upwelling index. The model sacrificed resolution of regional-scale and along-shore (north to south) processes and assumed that seasonal productivity is primarily driven by local cross-shelf Ekman transport of surface waters and upwelling of nutrient-rich water from depth.Our goals were to see how well a simple plankton model could capture the general temporal and spatial dynamics of the system, test system sensitivity to alternate parameter set values, and observe system response to the effective scale of potential retention mechanisms. Model performance across the central Oregon shelf was evaluated against two years (2000-2001) of chlorophyll and copepod time-series observations. While the modeled meso-zooplankton biomass was close in scale to the observed copepod biomass, phytoplankton was overestimated relative to that inferred from the observed surface chlorophyll concentration. Inshore, the system was most sensitive to the nutrient uptake kinetics of diatom-size phytoplankton and to the functional grazing response of meso-zooplankton. Meso-zooplankton was more sensitive to alternate parameter values than was phytoplankton. Reduction of meso-zooplankton cross-shelf advection rates (crudely representing behavioral retention mechanisms) reduced the scale of model error relative to the observed seasonal mean inshore copepod biomass but had little effect of the modeled meso-zooplankton biomass offshore nor upon phytoplankton biomass across the entire shelf.     

Linking richness, community variability, and invasion resistance with patch size

The influence of community dynamics on the success or failure of an invasion is of considerable interest. What has not been explored is the influence of patch size on the outcomes of invasions for communities with the same species pool. Here we use an empirically validated spatial model of a marine epibenthic community to examine the effects of patch size on community variability, species richness, invasion, and the relationships between these variables. We found that the qualitative form of the relationship between community variability and species richness is determined by the size of the model patch. In small patches, variability decreases with species richness, but beyond a critical patch size, variability increases with increasing richness. This occurs because in large patches large, long-lived colonies attain sufficient size to minimize mortality and dominate the community, leading to decreased species richness and community variability. This mechanism cannot operate on smaller patches where the size of colonies is limited by the patch size and mortality is high irrespective of species identity. Further, invasion resistance is strongly correlated with community variability. Thus, the relationship between species richness and invasion resistance is also determined by patch size. These patterns are generated largely by an inverse relationship between colony size and mortality, and they depend on the spatial nature and patch size of the community. Our results suggest that a continuum of possible relationships can exist between species richness, community variability, invasion resistance, and area. These relationships are emergent behaviors generated by the individual properties of the particular component species of a community.     

Does biodiversity of estuarine phytoplankton depend on hydrology?

Phytoplankton growth in estuaries is controlled by factors such as flushing, salinity tolerance, light, nutrients and grazing. Here, we show that biodiversity of estuarine phytoplankton is related to flushing, and illustrate this for some European estuaries.The implications for the definition of reference conditions for quality elements in estuaries of different types are examined, leading to the conclusion that constraints on the number of estuarine and coastal types that may be defined for management purposes require that quality classes take into account natural variability within types, in order to be ecologically meaningful. We develop a screening model to predict the growth rate required for a phytoplankton species to be present under different flushing conditions and apply it to estuaries in the EU and US to show how changes in physical forcing may alter biodiversity. Additional results are presented on the consequences for eutrophication, showing that changes in residence time may interact with species-specific nutrient uptake rates to cause shifts in species composition, potentially leading to effects such as harmful algal blooms.We discuss applications for integrated coastal zone management, and propose an approach to normalization of estuarine phytoplankton composition as regards species numbers.     

Modelling seasonal dynamics of biomasses and nitrogen contents in a seagrass meadow (Zostera noltii Hornem.): application to the Thau lagoon (French Mediterranean coast)

Anumerical deterministic model for a seagrass ecosystem (Zostera noltii meadows) has been developed for the Thau lagoon. It involves both above- and belowground seagrass biomasses, nitrogen quotas and epiphytes. Driving variables are light intensity, wind speed, rain data and water temperature. This seagrass model has been coupled to another biological model in order to simulate the relative contributions of each primary producer to: (i) the total ecosystem production, (ii) the impact on inorganic nitrogen and (iii) the fluxes towards the detritus compartment. As a first step in the modelling of seagrass beds in the Thau lagoon, the model has a vertical structure based on four boxes (a water box on top of three sediment boxes) and the horizontal variability is neglected until now. This simple box structure is nevertheless representative for the shallow depth Z. noltii meadows, spread over large areas at the lagoon periphery.After calibration, simulation results have been compared with in situ measurements and have shown that the model is able to reproduce the general pattern of biomasses and nitrogen contents seasonal dynamics. Moreover, results show that, in such shallow ecosystems, seagrasses remain the most productive compartment when compared with epiphytes or phytoplankton productions, and that seagrasses, probably due to their ability in taking nutrients in the sediment, have a lower impact on nutrient concentration in the water column than the phytoplankton. Furthermore, in spite of active mechanisms of internal nitrogen redistribution and reclamation, the occurrence of a nitrogen limitation of the seagrass growth during summer, already mentioned in the literature, have also been pointed out by the model. Finally, simulations seems to point out that epiphytes and phytoplankton could compete for nitrogen in the water column, while a competition for light resources seems to be more likely between epiphytes and seagrasses.     

A spatial model of phytoplankton patchiness

The one-dimensional theory of critical-length scales of phytoplankton patchiness is developed to include phytoplankton growth and herbivore grazing as functions of time and space. The critical-length scale L _c for the pathch is then determined by the initial spatial distribution and concentration of the limiting nutrient and herbivores in addition to the daily averaged values of the growth and loss processes. The response of an initial phytoplankton patch to the stresses of turbulent diffusion, nutrient depletion, light periodicity, and nocturnal or continuous herbivore grazing is investigated numerically for several oceanic conditions. Nocturnal grazing, while less stressful on primary production than continous grazing, results in lower phytoplankton standing stocks. Increase in biomass of vertically migrating zooplankton results in a net loss of nutrient which might otherwise be egested, recycled, and utilized in the euphotic zone under continuous grazing conditions. The Ivlev constant is shown via sensitivity analysis to be a significant parameter ultimately influencing phytoplankton production. It is demonstrated numerically that diffusion of phytoplankton cells from areas of high concentration to low concentration prevents the local extinction of the standing stock, thereby rendering a positive herbivore grazing-threshold unnecessary for ecosystem stability.     

Spatial scaling of avian population dynamics: population abundance, growth rate, and variability

Synchrony in population fluctuations has been identified as an important component of population dynamics. In a previous study, we determined that local-scale (<15-km) spatial synchrony of bird populations in New England was correlated with synchronous fluctuations in lepidopteran larvae abundance and with the North Atlantic Oscillation. Here we address five questions that extend the scope of our earlier study using North American Breeding Bird Survey data. First, do bird populations in eastern North America exhibit spatial synchrony in abundances at scales beyond those we have documented previously? Second, does spatial synchrony depend on what population metric is analyzed (e.g., abundance, growth rate, or variability)? Third, is there geographic concordance in where species exhibit synchrony? Fourth, for those species that exhibit significant geographic concordance, are there landscape and habitat variables that contribute to the observed patterns? Fifth, is spatial synchrony affected by a species' life history traits? Significant spatial synchrony was common and its magnitude was dependent on the population metric analyzed. Twenty-four of 29 species examined exhibited significant synchrony in population abundance: mean local autocorrelation (rho)= 0.15; mean spatial extent (mean distance where rho=0) = 420.7 km. Five of the 29 species exhibited significant synchrony in annual population growth rate (mean local autocorrelation = 0.06, mean distance = 457.8 km). Ten of the 29 species exhibited significant synchrony in population abundance variability (mean local autocorrelation = 0.49, mean distance = 413.8 km). Analyses of landscape structure indicated that habitat variables were infrequent contributors to spatial synchrony. Likewise, we detected no effects of life history traits on synchrony in population abundance or growth rate. However, short-distance migrants exhibited more spatially extensive synchrony in population variability than either year-round residents or long-distance migrants. The dissimilarity of the spatial extent of synchrony across species suggests that most populations are not regulated at similar spatial scales. The spatial scale of the population synchrony patterns we describe is likely larger than the actual scale of population regulation, and in turn, the scale of population regulation is undoubtedly larger than the scale of individual ecological requirements.     

Responses of phytoplankton and periphytic diatoms to environmental factors in a tropical riverine reservoir北大核心CSCD

Riverine reservoirs have a short water retention time, which is ecologically more similar to that of rivers. Generally, phytoplankton-based approaches are used for lakes and periphytic diatom-based approach for rivers. To understand the differences in the responses of phytoplankton and periphytic diatoms to environmental variables for riverine reservoirs, we collected periphytic diatom samples on artificial substrata as well as phytoplankton samples from a tropical reservoir with a resident time less than 10 days. Our results showed that 131 phytoplankton species and 138 periphytic diatoms were detected; the variation of phytoplankton community was mainly reflected by the dominant species with a strong response to the environmental variables at a time scale, whereas the variation of periphytic diatom community was noted in both the species composition and the dominant species, with a strong response at spatial-temporal scales. The multivariate regression analysis and redundancy analysis showed that environmental factors have higher explanations for the variance of the periphytic diatom community (R2 = 0.27). Temperature was the key explanatory variable for phytoplankton, planktonic diatoms and periphytic diatoms (P < 0.01). However, dissolved oxygen and nitrate were also detected as significant explanatory factors associated with periphytic diatom community (P < 0.01). Thus, the periphytic diatoms were concluded to be more sensitive to environmental change and were associated with more environmental variables than phytoplankton. Periphytic diatoms appear to provide more ecological information than phytoplankton for riverine reservoirs. © 2018 Science Press. All rights reserved.