Modeling approach to regime shifts of primary production in shallow coastal ecosystems

Pristine coastal shallow systems are usually dominated by extensive meadows of seagrass species, which are assumed to take advantage of nutrient supply from sediment. An increasing nutrient input is thought to favour phytoplankton, epiphytic microalgae, as well as opportunistic ephemeral macroalgae that coexist with seagrasses. The primary cause of shifts and succession in the macrophyte community is the increase of nutrient load to water; however, temperature plays also an important role. A competition model between rooted seagrass (Zostera marina), macroalgae (Ulva sp.), and phytoplankton has been developed to analyse the succession of primary producer communities in these systems. Successions of dominance states, with different resilience characteristics, are found when modifying the input of nutrients and the seasonal temperature and light intensity forcing.     

Use of individual-based models for population parameters estimation

A method for parameters estimation of stage-specific mortality and fecundity rate functions in poikilotherm organisms, and in particular for arthropod structured population, is proposed. The application of this method requires three types of information: stage-frequency data of a sampled population, development rate function and time evolution of forcing variables affecting the rate functions. By means of an individual-based model (a microscopic model) the number of eggs produced by the adults is generated starting from the number of individuals collected at each sampling time. Using a compartmental model (a macroscopic model) a stage-structured population dynamics is described and compared with observations. Non-linear regression methods based on least square principle are used to estimate the optimal parameters of the mortality and fecundity rate functions combining microscopic and macroscopic models. As a case study, the parameter estimation of the temperature-dependent mortality function of olives fruit fly Bactrocera oleae is presented.     

中国东南部太阳辐射变化特征、影响因素及其对区域气候的影响

基于8个站点1961年以来的长期太阳辐射及其它气象观测数据,通过线性回归、相关分析等方法,探讨近半个世纪以来中国东南部太阳辐射的变化特征,并分析了太阳辐射变化的影响因素以及对区域气候的影响。结果表明：该地区地表总太阳辐射自1961年以来呈下降趋势,变化率为-10.17MJ·m-2·a-1。太阳辐射下降主要集中在1961到1990年间,该时间段的下降趋势达到-39.43MJ·m-2·a-1,主要表现为直接辐射显著下降,散射辐射则变化不大;1990年代以后,地表总太阳辐射开始呈现上升趋势,变化率为13.21MJ·m-2·a-1。该地区太阳辐射变化与全球范围内太阳辐射＂变暗＂及＂变亮＂的变化是一致的。从云量对太阳辐射的作用来看,该地区太阳辐射的变化很有可能是受到低云量变化的影响;而太阳辐射的这种变化直接导致气温发生变化,使得最高气温和最低气温的变化出现不一致,日较差随之发生改变。     

Spatial and temporal hydrodynamic and water quality modeling analysis of a large reservoir on the South Carolina (USA) coastal plain

Two-dimensional, 31-segment, 61-channel hydrodynamic and water quality models of Lake Marion (surface area 330.7 km²; volume 1548.3×10⁶ m³) were developed using the WASP5 modeling system. Field data from 1985 to 1990 were used to parameterize the models. Phytoplankton kinetic rates and constants were obtained from a related in situ study; others from modeling literature. The hydrodynamic model was calibrated to estimates of daily lake volume; the water quality model was calibrated for ammonia, nitrate, ortho-phosphate, dissolved oxygen, chlorophyll-a, biochemical oxygen demand, organic nitrogen, and organic phosphorus. Water quality calibration suggested the model characterized phytoplankton and nutrient dynamics quite well. The model was validated (Kolmogorov–Smirnov two-sample goodness-of-fit test at P<0.05) by reparameterizing the nutrient loading functions using an independent set of field data. The models identified several factors that may contribute to the spatial variability previously reported from other research in the reservoir, despite the superficial absence of complex structure. Sensitivity analysis of the phytoplankton kinetic rates suggest that study site-specific estimates were important for obtaining model fit to field data. Sediment sources of ammonia (10–60 mg m⁻² day⁻¹) and phosphate (1–6 mg m⁻² day⁻¹) were important to achieve model calibration, especially during periods of high temperatures and low dissolved oxygen. This sediment flux accounted for 78% (nitrogen) and 50% (phosphorus) of the annual load. Spatial and temporal variability in the lake, reflected in the calibrated and validated models, suggest that ecological factors that influence phytoplankton productivity and nutrient dynamics are different in various parts of the lake. The WASP5 model as implemented here does not fully accommodate the ecological variability in Lake Marion due to model constraints on the specification of rate constants. This level of spatial detail may not be appropriate for an operational reservoir model, but as a research tool the models are both versatile and useful.     

A Simple Model to Determine In Situ Mixing Height Growth from Surface Measurements

A generic In Situ Mixing Height Growth (IMG) model, capable of predicting the real-time growth of the mixed layer and its diurnal evolution from routinely observed simple surface meteorological is developed. The algorithm for the determination of temporally growing daytime mixing height includes both convective and mechanical turbulence contributions. It accounts for neutral as well as height varying potential temperature gradients above the mixed layer. For thermally stable and mechanically dominated unstable night time Atmospheric Boundary Layer (ABL) the module uses similarity formulae based on the wind velocity [1]), the Monin—Obukhov length [2], and the Coriolis parameter. In the convective case simple slab model is integrated, based on initial lapse rate and the surface heat flux. The lapse rate is evaluated on the basis of vertical atmospheric stability, surface type and surface temperature. This differentiates the IMG model from other existing mixing height models that need initial measured lapse rate for calculation. IMG model is site specific as it calculates the radiative incoming heat flux depending on the solar declination estimates based on-site latitude and longitude. The IMG model is applied to calculate mixing height for India by using surface data (viz. wind speed, surface temperature, surface type) from 152 surface meteorological stations. Results have been evaluated with radiosonde mixing height data procured from 18 upper air stations. Sensitivity analysis of the model with respect to various parameters is performed. The model is formulated after reviewing presently available radiosonde mixing height data in India and can satisfactorily provide an alternative means of estimating mixing height for air pollution dispersion models.     

Role of the natural and anthropogenic radiative forcings on global warming: evidence from cointegration–VECM analysis

Over the last few years there has been much debate about the hypothesis that anthropogenic emissions of CO₂ and other greenhouse gases increase global temperature permanently. By using recent advances in time series econometrics, this paper tries to answer the question on how human activity affects Earth’s surface temperatures. Bearing in mind this goal, we estimated the long-run cointegration relations between global temperatures and changes in radiative forcings by a set of perturbing factors. We found that the temperature response to a doubling in radiative forcing of anthropogenic greenhouse gases is + 2.94 °C [95 % CI: + 1.91, + 3.97], in perfect accordance with prior research, and that the orthogonalized cumulated effect over a 100 year time period, in response to a unit increase of size of one standard deviation in greenhouse gas radiative forcing, is + 3.86 °C [95 % CI: + 0.03, + 6.54]. Conversely, the amplitude of solar irradiance variability is hardly sufficient to explain observed variations in the Earth’s climate. Our results show that the combined effect of stochastic trends attributable to anthropogenic radiative forcing variations are driving the Earth’s climate system toward an ongoing phase of global warming, and that such long-run movement is unlikely to be transient.     

A model for measuring leaf growth rate of vegetative shoot in the seagrass, Zostera marina

A new model for determining leaf growth in vegetative shoots of the seagrass Zostera marina (eelgrass) is described. This model requires the weights of individual mature and immature whole leaves and leaf plastochrone interval (P_L) as parameters, differing from the conventional leaf marking technique (CLM) that requires cutting and separation between new and old tissue of leaves. The techniques required for the model are the same as for the plastochrone method, but the parameters differ between both methods in use of the weight of individual immature leaves. In a mesocosm study, eelgrass growth was examined, and parameters for the new model and plastochrone method (the weights of individual mature and immature leaves and P_L) were measured. Leaf growth rate was measured using the CLM and determined by the new method and the plastochrone method. The results were then compared between the CLM, the new model, and the plastochrone method. The results obtained with the new model were similar to those obtained with the CLM. However, the results of the plastochrone method differed from those of the CLM, while the weight of immature leaves varied seasonally. The new model was also used to determine leaf growth in a natural eelgrass bed in Mikawa Bay, Japan, and revealed the growth rates in all shoots and those of different ages. This method would be advantageous as an accurate means of direct measurement in fieldwork, and should therefore be a useful tool for monitoring seagrass growth.     

Long-run changes in radiative forcing and surface temperature: The effect of human activity over the last five centuries

We test two hypotheses that are derived from the anthropogenic theory of climate change. The first postulates that a growing population and increasing economic activity increase anthropogenic emissions of radiatively active gases relative to natural sources and sinks, and this alters global biogeochemical cycles in a way that increases the persistence of radiative forcing and temperature. The second postulates that the increase in the persistence of radiative forcing transmits a stochastic trend to the time series for temperature. Results indicate that the persistence of radiative forcing and temperature changes from I(0) to I(1) during the last 500 years and that the I(1) fingerprint in radiative forcing can be detected in a statistically measureable fashion in surface temperature. As such, our results are consistent with the physical mechanisms that underlie the theory of anthropogenic climate change.     

Space-time modeling of environmental monitoring data

This study describes and applies statistical methods for space-time modeling of data from environmental monitoring programs, e.g., within areas such as climate change, air pollution and aquatic environment. Such data are often characterized by sparse sampling in both the temporal and spatial dimensions. In order to improve the amount of information on the physical system in question we suggest using statistical modeling methods for monitoring data. Model predictions combined with observations could be analyzed directly to assess the environmental state or as forcing functions for time series models and deterministic, hydrodynamic models. To illustrate the approach we applied the proposed modeling methods to data from the Danish and Swedish marine monitoring programs. Time series with a weekly resolution were predicted from observations of dissolved inorganic nitrogen (DIN) from the Kattegat basin (1993–1997). DIN observations were sparse, irregularly distributed and comprised approximately 10% of the generated time series.     

Analysis of annual fluctuations of C. nodosa in the Venice lagoon: Modeling approach

A simple simulation model was developed to describe the growth trends of Cymodocea nodosa (Ucria) Ascherson based on data sets from the Venice lagoon. The model reproduces the seasonal fluctuations in the above and belowground biomass and in shoot density. The modeling results are in good agreement with data on net production, growth rates and chemical–physical parameters of water. It was assumed that light and temperature are the most important factors controlling C. nodosa development, and that the growth was not limited by nutrient availability. The aim was to simulate biomass production as a function of external forcing variables (light, water temperature) and internal control (plant density). A series of simulation experiments were performed with the basic model showing that among the most important phenomena affecting C. nodosa growth are: (1) inhibition of production and recruitment of new shoots by high temperature and (2) light attenuation due to seasonal fluctuation.     

Hierarchical Bayesian space-time models

Space-time data are ubiquitous in the environmental sciences. Often, as is the case with atmo- spheric and oceanographic processes, these data contain many different scales of spatial and temporal variability. Such data are often non-stationary in space and time and may involve many observation/prediction locations. These factors can limit the effectiveness of traditional space- time statistical models and methods. In this article, we propose the use of hierarchical space-time models to achieve more flexible models and methods for the analysis of environmental data distributed in space and time. The first stage of the hierarchical model specifies a measurement- error process for the observational data in terms of some 'state' process. The second stage allows for site-specific time series models for this state variable. This stage includes large-scale (e.g. seasonal) variability plus a space-time dynamic process for the anomalies'. Much of our interest is with this anomaly proc ess. In the third stage, the parameters of these time series models, which are distributed in space, are themselves given a joint distribution with spatial dependence (Markov random fields). The Bayesian formulation is completed in the last two stages by speci- fying priors on parameters. We implement the model in a Markov chain Monte Carlo framework and apply it to an atmospheric data set of monthly maximum temperature.     

The effects of littoral zone vegetation on turbulent mixing in lakes

Micro-scale thermal profile data were acquired in four lakes in northwest England and southeast Australia that ranged from a small, sheltered pond with a surface area of about 1 ha to more open lakes with surface areas of several square kilometres. These lakes provided a range of topographic and climatic contexts, basin morphologies and dominant macrophyte species. The data were acquired using two SCAMP profilers, one deployed in the open water and the other mounted on a field traverse deployed within the vegetated littoral zone. From these profile data, turbulence parameters were calculated. The results show the variation in the influence of vegetation on turbulence in the four lakes, which depends on the combination of wind stress, solar radiative forcing and macrophyte mechanical properties. In the sheltered pond, the vegetation alters the light climate within the water, thus reducing stratification and allowing weak, thermally-driven mixing. In the larger lakes, however, the primary action of the vegetation is to prevent surface-generated TKE from penetrating the water column, although this effect becomes less important as the plant separation increases. A simple mechanistic model, calibrated against the field data, suggests that the macrophyte mechanical properties are most important in determining the turbulent kinetic energy (TKE) profile. Increasing the number of turbulence-generating plants reduces the transport of surface-generated TKE into the deeper water, consistent with the field observations. The model suggests that solar forcing, as measured by the temperature gradient between the surface and bottom waters, is of less importance since the TKE profile is similar in runs with different gradients. Perhaps most surprisingly, the value of the surface-wind stress used in the model is not important, within the limitations of the model, as it does not change the TKE profile, except in a thin surface layer.     

Spatial patterns and environmental correlates in leaf-associated epifaunal assemblages of temperate seagrass (<Emphasis Type="Italic">Zostera marina</Emphasis>) meadows

We estimated and tested variability of seagrass leaf-associated epifaunal assemblages at a range of scales. Sampling was performed in 36 seagrass (Zostera marina) meadows within three regions along the Swedish west coast following a hierarchical design (samples separated by 10 s m, km or 100 km). Results showed strongest variability (43–81%) at the intermediate amongst-meadow (km) scale using biomass of functional categories, while considering taxa composition the within-meadow (10 s m) scale contributed most to variability (60%). Using functional categories, we found that embayment exposure and seagrass shoot density were the most important predictor variables explaining part of the variability in biomass of suspension feeders (bivalves and barnacles) and grazers. In contrast, variability in epifaunal taxa composition was predicted mainly by sediment chemistry, substratum coverage and geographical positioning. Our findings suggest that models to develop predictive power and mechanistic understanding should focus on variables and processes varying at small and intermediate scales rather than those varying at larger scales.     

Production dynamics of the eelgrass, <Emphasis Type="Italic">Zostera marina</Emphasis> in two bay systems on the south coast of the Korean peninsula

Production dynamics of eelgrass, Zostera marina was examined in two bay systems (Koje Bay and Kosung Bay) on the south coast of the Korean peninsula, where few seagrass studies have been conducted. Dramatically reduced eelgrass biomass and growth have been observed during summer period on the coast of Korea, and we hypothesized that the summer growth reduction is due to increased water temperature and/or reduced light and nutrient availabilities. Shoot density, biomass, morphological characteristics, leaf productivities, and tissue nutrient content of eelgrass were measured monthly from June 2001 to April 2003. Water column and sediment nutrient concentrations were also measured monthly, and water temperature and underwater irradiance were monitored continuously at seagrass canopy level. Eelgrass shoot density, biomass, and leaf productivities exhibited clear seasonal variations, which were strongly correlated with water temperature. Optimal water temperature for eelgrass growth in the present study sites was about 15–20°C during spring period, and eelgrass growths were inhibited at the water temperature above 20°C during summer. Daily maximum underwater photon flux density in the study sites was usually much higher than the light saturation point of Z. marina previously reported. Densities of each terminal, lateral, and reproductive shoot showed their unique seasonal peak. Seasonal trends of shoot densities suggest that new eelgrass shoots were created through formation of lateral shoots during spring and a part of the vegetative shoots was transformed into flowering shoots from March. Senescent reproductive shoots were detached around June, and contributed to reductions of shoot density and biomass during summer period. Ambient nutrient level appeared to provide an adequate reserve of nutrient for eelgrass growth throughout the experimental period. The relationships between eelgrass growth and water temperature suggested that rapid reductions of eelgrass biomass and growth during summer period on the south coast of the Korean peninsula were caused by high temperature inhibition effects on eelgrass growth during this season.     

Comparisons in demographic rates of bay scallops in eelgrass and the introduced alga, Codium fragile, in New York

Bay scallops, Argopecten irradians, supported vibrant fisheries which subsequently collapsed, as such, they are a focus species for many restoration efforts along the Atlantic and Gulf coasts of the United States. The scallops’ preferred habitat, seagrass, has also dramatically declined, and some scallop populations have increased post-restoration despite reduced seagrass cover. This has led to the hypothesis that macroalgae may serve as suitable alternative habitats for bay scallops. This study is the first to compare demographic rates, such as long-term survival, growth, condition and reproductive potential of scallops between the native eelgrass, Zostera marina, and the introduced alga, Codium fragile. Although long-term survival was not different between habitats, results suggest site-specific and inter-annual variation in the impacts of Codium on scallop growth. While demographic rates did not differ in Shinnecock Bay, in Sag Harbor, growth and/or condition were significantly different between both vegetated habitats depending on the year. However, recruit density, size and condition did not vary significantly, adding to the complexity of this relationship. Despite potential site-specific and inter-annual differences, this study supports the hypothesis that habitats other than eelgrass can benefit bay scallops.     

Which forcing factors fit? Using ecosystem models to investigate the relative influence of fishing and changes in primary productivity on the dynamics of marine ecosystems

Fishing mortality and primary production (or proxy for) were used to drive the dynamics of fish assemblages in 9 trophodynamic models of contrasting marine ecosystems. Historical trends in abundance were reconstructed by fitting model predictions to observations from stock assessments and fisheries independent survey data. The model fitting exercise derives values for otherwise unknown parameters that specify the relative strength of trophic interactions and, in some instances, a time series anomaly for changes in primary production. We measured how much better or worse were model predictions when bottom-up forcing by primary production were added to top-down forcing by fishing. Searching for cross system patterns, the relative contribution of fishing and changes in primary production, mediated through trophic interactions, are evaluated for the ecosystems as a whole and for selected similar species in different ecosystems. The analysis provides a simple qualitative way to explain which forcing factors have most influence on modeled dynamics. Both fishing and primary production forcing were required to obtain the best model fits to data. Fishing effects more strongly influenced 6 of 9 of the ecosystems, but primary production was more often found to be the main factor influencing the selected pelagic and demersal fish stock trends. Examination of sensitivity to ecological and model parameters suggests that the results are the product of complex food-web interactions rather than simple deterministic responses of the models.     

Predictive modelling of eelgrass (Zostera marina) depth limits

Empirical models relating secchi depths to maximum depth limits of eelgrass (Zostera marina L.) can describe basic differences in depth limits between areas or time periods exhibiting large differences in secchi depth. However, these models cannot predict the precise depth limit at a particular site at any specific time. In this study we aim to improve the ability of regression models to predict maximum depth limits by: (1) assuming that eelgrass depth limits respond to changes in secchi depth with a temporal delay of 1–2 years, (2) including other water-quality variables in addition to secchi depth, and (3) taking into account that factors regulating depth limits may vary between years and between sites. We were not able to improve the models by introducing a systematic delay in the response of depth limits to changes in secchi depths. The reason for this failure is likely to have been the systematic nature of our approach, since some sites responded with a delay, while others did not. The explanatory power of the models increased when additional water-quality variables were added in a multiple regression model. Where secchi depth alone explained 58% of the variations in depth limits, addition of winter [NH₄⁺] and maximum water depth as independent variables increased the explanatory power to 71%. These models applied to data from one specific year, but when data from several years (1989–1998) were included, only 35% of the variation in depth limits could be explained by the three factors. More detailed analyses showed that the regulation of eelgrass depth limits varied considerably between years and between sites, and the models were further improved by taking this information into account. Our results confirmed previous studies by showing light to be the most important parameter in the regulation of eelgrass depth limits, but also revealed a complexity in the regulation of depth limits not expressed in earlier studies. Limited colonisation potentials may delay the response to improved light conditions, and hypoxia/anoxia and indirect effects of nutrients may prevent eelgrass from attaining the depth limit that light levels would allow. The power to predict depth limits on the basis of secchi depths can therefore be improved by taking site-specific information on eelgrass growth conditions into account.Communicated by M. Kühl, Helsingør     

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.     

Nitrogen source apportionment—a comparison between a dynamic and a statistical model

A dynamic model, HBV-N, and a statistical model, MESAW, for nitrogen source apportionment were compared regarding model performance, model uncertainty and user applicability. The HBV-N model simulates continuous series of nitrogen concentrations with meteorological data and sub-basin characteristics as input. Diffuse nitrogen emissions are defined as regional model parameters which are calibrated by comparison of observed and simulated nitrogen data. The MESAW model uses nitrogen loads for a fixed time interval at each monitoring site as response variable and sub-basin characteristics as explanatory variables to estimate diffuse nitrogen emissions through non-linear regression analysis. The two models were applied in the Matsalu Bay watershed (3640 km²) in Estonia and the same land use and point sources data were used as input. Both models gave similar levels of diffuse total nitrogen emissions and retention rates, which also fit well with previous estimates made in Estonia and Scandinavia. A sensitivity analysis of the model parameters also showed similar uncertainty levels, which indicated that the model uncertainty was more dependent on the availability of nitrogen data and land cover distribution than the choice of model. Furthermore, the sensitivity analysis showed a parameter interdependency in both models, which implied the risk of compensation between estimated diffuse emissions and retention. In conclusion, however, the study showed that both models were capable of estimating nitrogen leakage from the dominating land classes and giving reliable source apportionment from the available input data. The study indicated that the HBV-N model has its advantage in assessments where detailed outputs are needed and when run-off data are limited, while the statistical MESAW model has its advantage in extensive studies since it is easily applied to large watersheds that have dense monitoring networks.     

Gridded meteorological data as a resource for mechanistic macroecology in coastal environments

Gridded weather data were evaluated as sources of forcing variables for biophysical models of intertidal animal body temperature with model results obtained using local weather station data serving as the baseline of comparison. The objective of the study was to determine which gridded data are sufficient to capture observed patterns of thermal stress. Three coastal sites in western North America were included in this analysis: Boiler Bay, Oregon; Bodega Bay, California; and Pacific Grove, California. The gridded data with the highest spatial resolution, the 32-km North American Regional Reanalysis (NARR) and the 38-km Climate Forecasting System Reanalysis (CFSR), predicted daily maximum intertidal animal temperature most similarly to the local weather Station data. Time step size was important for variables that change rapidly throughout the day, such as solar radiation. There were site-based differences in the ability of the model to predict daily maximum intertidal animal temperature, with the gridded data predictions being the closest to local weather station predictions in Boiler Bay, Oregon. In a review of gridded data used as part of ecological studies, there was broad use of the data across subject areas and ecosystems so the recent improvements in the spatial (from 2 degrees to 32 km) and temporal scales (from 6 hours to 1 hour) of gridded data will further add to the applicability within the ecological community particularly for mechanistic studies.