Evaluation and application of a three-dimensional water quality model in a shallow lake with complex morphometry

Fundamental hydrodynamic and ecological processes of a lake or reservoir could be adequately depicted by one-dimensional (1D) numerical simulation models. Whereas, lakes with significant horizontal water quality and hydrodynamic gradients due to their complex morphometry, inflow or water level fluctuations require a three-dimensional (3D) hydrodynamics and ecological analyses to accurately simulate their temporal and spatial dynamics. In this study, we applied a 3D hydrodynamic model (ELCOM) coupled with an ecological model (CAEDYM) to simulate water quality parameters in three bays of the morphologically complex Lake Minnetonka. A considerable effort was made in setting up the model and a systematic parameterization approach was adopted to estimate the value of parameters based on their published values. Model calibration covered the entire length of the simulation periods from March 29 to October 20, 2000. Sensitivity analysis identified the top parameters with the largest contributions to the sensitivity of model results. The model was next verified with the same setup and parameter values for the period of April 25 to October 10, 2005 against field data. Spatial and temporal dynamics were well simulated and model output results of water temperature (T), dissolved oxygen (DO), total phosphorus (TP) and one group of algae (Cyanobacteria) represented as chlorophyll a (Chla) compared well with an extensive field data in the bays. The results show that the use of the model along with an accurate bathymetry, a systematic calibration and corroboration (verification) process will help to analyze the hydrodynamics and geochemical processes of the morphologically complex Lake Minnetonka. An example of an ecological application of the model for Lake Minnetonka is presented by examining the effect of spatial heterogeneity on coolwater fish habitat analysis in 3D and under a scenario where horizontal spatial heterogeneity was eliminated (1D). Both analyses captured seasonal fish habitat changes and the total seasonal averages differed moderately. However, the 1D analysis did not capture local and short duration variabilities and missed suitable fish habitat variations of as much as 20%. The experiment highlighted the need for a 3D analysis in depicting ecological hot spots such as unsuitable fish habitats in Lake Minnetonka.     

Directing technical change from fossil-fuel to renewable energy innovation: An application using firm-level patent data

In this paper we provide an analysis of directed technical change in the sector of electricity generation. We rely on patent data in fossil-fuel (FF) and renewable energy (REN) technologies for 5471 European firms over the 1978–2006 period. The novelty of our approach is in the focus on firm׳s heterogeneity in driving technological change. We make a distinction between small specialized firms, which innovate in only one type of technology, and large mixed firms, which innovate in both technologies, to analyse how REN patents can replace FF ones at the sector level both through a shift in innovation activities within existing firms and through firms׳ entry and exit. We use zero-inflated count data estimation techniques to identify the factors that affect specialized versus mixed firms׳ patenting behaviour both at the intensive (i.e., levels of innovation) and extensive (i.e., technological entry) margins. We further investigate the implications of our firm-level estimations for reducing the gap between REN and FF innovation at the aggregate level. We establish two key findings: (1) a decrease in the FF-REN technology gap mainly comes about through technological entry of specialized REN firms following an increase in REN market size; (2) increases in FF prices, FF market size, and FF knowledge stocks all increase the technology gap by increasing mixed firms FF innovation rates. An important implication of our results is that policies aimed at increasing REN innovation should focus on helping small firms to start and sustain innovation in the long-run.     

Flocking behaviour of passerines: a dynamic model for the non-reproductive season

Summary The behaviour of a small male passerine bird over a typical winter day is studied by a dynamic programming model. The bird can be either unpaired or paired; an unpaired bird can forage in a flock, forage alone or sing to attract a mate. Foraging increases his reserves, while singing reduces them. The optimal policy and the expected behaviour of birds depend both on time and reserves. The model predicts that birds will flock, especially in the morning, if flocking birds find more food (foraging efficiency), and also more flocking can be expected when the predation risk is lower in a flock (antipredator benefit). Where flocking gives lower variance in food intake, with the same mean (reduced variance benefit), birds with low reserves at the end of the day choose to forage alone (high variance option), while otherwise they are risk-averse and forage in a flock. The cost of flocking increases with time in a day and with the probability of mate attraction through singing. Decisions inevitably involve trade-offs. Where flocking results in antipredator benefit, but also lower foraging gain, birds with low reserves forage alone, but birds with high reserves flock. Offprint requests to: A.I. Houston     

A numerical model of periodically forced circulation near the shore of a lake

Numerical calculations for a model of the near-shore circulation in a lake subject to two diurnal forcing mechanisms are presented. The first mechanism is a heating/cooling term in the heat equation representing the daytime heating and nighttime cooling of the diurnal cycle. The second is a periodic surface stress modelling a sea-breeze/gully wind system typical of some coastal regions. The two forcing mechanisms can either act together or against each other depending on their relative phase. The numerical solutions are compared with previously published analytical solutions and used to explore the extra dynamics associated with non-linear effects (specifically advection). The latter dynamics include the formation of gravity currents and unstable density profiles leading to secondary circulation.     

Using a 1-D mixing model to simulate the vertical flux of heat and oxygen in a lake subject to episodic mixing

A one-dimensional, two-layer lake model is used to simulate the daily temperature and oxygen profile of an English Lake in response to changes in wind, air temperature and radiation. The thermal model component derives from the TEMIX lake model originally developed by the Institute of Limnology in St. Petersburg [Mironov, D.V., Golosov, S.D., Zilitinkevitch, S.S., Kreiman, K.D., Terzhevik, A.Y., 1991. Seasonal changes of temperature and mixing conditions in a lake. In: Zilitinkevitch, S.S. (Ed.), Modelling Air–Lake Interactions. Springer-Verlag, pp. 74–90]. This paper describes the model's adaptation and its extension to incorporate dissolved oxygen to simulate periods of anoxia of short duration during summer stratification. The new oxygen model component, which is based on mass-balance principles, divides the lake into two layers in a similar way to the thermal model. Its primary purpose is to model periods of anoxia for use in studies of fish survival. The model has been tested over a 10-year period from 1991 to 1999 using daily weather data and fortnightly observations of chlorophyll a and secchi depth. Ten years of fortnightly oxygen measurements, together with 2 years of more detailed (hourly) oxygen data, indicate that simulated and observed oxygen levels are in reasonable agreement considering the sparseness of the chlorophyll observations. The balance between the relative effects of temperature and BOD on oxygen depletion is of particular importance to model accuracy.     

Simulation of herbicide impacts on a plant community: comparing model predictions of the plant community model IBC-grass to empirical data

Background

Semi-natural plant communities such as field boundaries play an important ecological role in agricultural landscapes, e.g., provision of refuge for plant and other species, food web support or habitat connectivity. To prevent undesired effects of herbicide applications on these communities and their structure, the registration and application are regulated by risk assessment schemes in many industrialized countries. Standardized individual-level greenhouse experiments are conducted on a selection of crop and wild plant species to characterize the effects of herbicide loads potentially reaching off-field areas on non-target plants. Uncertainties regarding the protectiveness of such approaches to risk assessment might be addressed by assessment factors that are often under discussion. As an alternative approach, plant community models can be used to predict potential effects on plant communities of interest based on extrapolation of the individual-level effects measured in the standardized greenhouse experiments. In this study, we analyzed the reliability and adequacy of the plant community model IBC-grass (individual-based plant community model for grasslands) by comparing model predictions with empirically measured effects at the plant community level.

Results

We showed that the effects predicted by the model IBC-grass were in accordance with the empirical data. Based on the species-specific dose responses (calculated from empirical effects in monocultures measured 4 weeks after application), the model was able to realistically predict short-term herbicide impacts on communities when compared to empirical data.

Conclusion

The results presented in this study demonstrate an approach how the current standard greenhouse experiments—measuring herbicide impacts on individual-level—can be coupled with the model IBC-grass to estimate effects on plant community level. In this way, it can be used as a tool in ecological risk assessment.

     

Sediment resuspension by wind in a shallow lake of Esteros del Iberá (Argentina): a model based on turbidimetry

A model based on empirical relationships is used to study frequency and magnitude of the sediment resuspension by wind-induced waves. The model has been developed for Laguna Galarza, a mesotrophic round-shaped shallow lake located in Esteros del Iberá wetland. Given the logistic and accessibility difficulties of this macrosystem, the installation of automated field stations facilitated continuous data acquisition. Using the wave theory, a daily spatial model of resuspension was built from simultaneous time series of hourly measurements of infrared nephelometric turbidity, wind speed and wind direction. The model was used to predict total suspended solids in another lake of the wetland (Laguna Iberá) showing a good agreement with observed field values, even although Laguna Iberá has a more irregular contour and a eutrophic state. Finally, we apply the model to discuss the ecological impacts of resuspension on the distribution of the littoral communities and to characterize the composition of the particulate suspended matter of the limnetic ecosystem. The model was useful to simulate the possible implications of the recent alterations of the wetland water level on the resuspension regime of the open water bodies.     

Development and application of “U.S.E.D.”: A hydroclimate lake stratification model

Two model types are currently in use to model the thermal stratification cycle in lakes and reservoirs: the eddy diffusion and the mixed layer (or integral energy) approaches. Here the former is analysed and developments are proposed to remove the empiricisms previously implicit in these models. These discussions permit the reformulation of K_H0 independently of current shear, together with an expression for R_i. The deduced formulae are in good agreement with observations. The newly formulated model (the University of Salford eddy diffusion model, U.S.E.D.) is subsequently used in simulations of lakes and reservoirs at different latitudes which are found to be in good agreement with observations without requiring inter-site calibration.     

An indirect method for the rapid estimation of carotenoid contents in Phaeodactylum tricornutum: Possible application to other marine algae

The equations of Parsons and Strickland (1963) have been applied to calculate the concentration of total carotenoids in Phaeodactylum tricornutum, and the same pigments have simultaneously been determined on the individual fractions after TLC (thinlayer chromatography) separation. Comparison of both sets of data shows that for diverse carotenoids: chlorophyll a ratios, the direct spectrophotometric values range from 80 to 130% of the chromatographic values; wide variations in the proportions of different carotenoids account for such difference. On the basis of correlations between the proportion of each carotenoid and the absorbance ratio 480:663 nm, an indirect method for the rapid estimation of individual carotenoids of P. tricornutum is proposed. Its eventual application to other Chrysophyta species and to sea water is discussed.Contribución del Instituto de Biología Marina No. 320.     

A method for building spatial model of annual weed seed dispersal from experimental data and its application to simulating Bromus sterilis population dispersal

Spatial model of annual weed seed dispersal, in this article, was theoretically derived. According to the requirements of building the spatial model, we designed and done an indoor experiment of weed seed dispersal by wind. In the experiment, the seeds of Bromus sterilis were released at 100 cm height under different wind velocity conditions. Based on the experimental data, the spatial models of seed dispersal of the weed species were built, which were divided into three types according to the coefficient β < 0, β = 0, β > 0. The results showed that dispersal of annual weed seed in any direction obeyed an approximate Gaussian distribution; under the experimental conditions, spatial distribution type of weed seed dispersal changed with variation of wind velocity. Well-known Howard et al.'s model (Howard et al., 1991) of Bromus sterilis seed dispersal is an especial example of the model built in this article. The result of model analysis indicated that the distribution type described by Howard's model was similar to that of seed dispersal of the weed species at the height of 100 cm under the condition of lower wind velocity (about 2.18 m/s). Using CA simulation analysis we found that mean control agent applying to a cell with weed should have a decrease with an increase of wind velocity to prevent weed with the initial configuration from spreading, which implicated less herbicide needs spraying in every cell with weed on average when wind velocity increases.     

An integrated model of regional and local residential sorting with application to air quality

We examine the interconnectedness of demand for regionally and locally varying public goods using a residential sorting model. We propose a version of the model that describes household choices at the city (MSA) level and, conditional on city, the neighborhood (census tract) level. We use a two-stage budgeting argument to develop an empirically feasible sorting model that allows us to estimate preferences for regionally varying air quality while accounting for sorting at the local level. Our conceptual and empirical approach nests previous sorting models as special cases, allowing us to assess the importance of accounting for multiple spatial scales in our predictions for the cost of air pollution. Furthermore our preferred specification connects the city and neighborhood sorting margins to the upper and lower elements of a nested logit model, thereby establishing a useful correspondence between two stage budgeting and nested logit estimation. Empirically we find that estimates from a conventional model of sorting across MSAs imply a smaller marginal willingness to pay for air quality than estimates from our proposed model. We discuss how the difference is attributable in part to the omitted variable problems arising when tract level sorting is ignored.     

An application of Markov Decision Programming to ecologico-cybernetics—A decision method applied to the antifrost of winter wheat

Modern ecologico-cybernetic principle is of importance to decreasing damages in relation to agricultural productions. As an illustration of this, the authors studied some of the questions about the optimal policy of choosing actions for the antifrost measures of winter wheat by means of the finite-stage model of Markov Decision Programming (MDP). The related data came from the investigation results of the investigated region in the northeastern part of Henan, China. First, the authors give the states, ecologico-cybernetic action measures, transition probabilities and reward values in relation to these antifrost action measures of winter wheat crop. Second, the authors describe the principles and computational procedures of the ecologico-cybernetic decision-makings based on the finite-stage model of MDP. Third, a simple table applicable to the decision-making practice is given. Finally, we evaluate the results of this study, point out their shortcomings and suggest that this method is applicable to the other fields in relation to decreasing damage ecologico-cybernetics.     

The use of effluent fees to regulate public sector sources of pollution An application of the niskanen model

The economics literature on environmental policy makes a compelling case for the use of effluent fees to control polluting activities. This analysis, however, proceeds from the assumption of profit maximizing behavior by polluters. Since (non-profit-maximizing) public agencies are a major source of environmental damage, this paper investigates the likely response of bureaucrats to effluent fees in terms of some extended versions of the Niskanen model of bureaucratic behavior. We find that, at least for a range of plausible cases, such fees can induce significant reduction in polluting activities. The results are sufficiently encouraging to make the extension of fees to public agencies worthy of serious consideration.     

Public expenditure and the environmental management of Brazilian municipalities: a panel data model

Environmental expenditure can be classified as an indicator to measure the efforts of public agents with the environment. Cities and countries seek to understand the determinants of environmental spending over time. This research aims to analyze the variables that influence the environmental spending of Brazilian municipalities between the years 2012 and 2016. Thus, this research helps fill the gap on lack of longitudinal research that involves local contexts in relation to public municipal expenditures on the environment. The research method consists of the panel data model. Data were collected on the environmental expenditures of the municipalities and other variables of 4269 Brazilian municipalities, the collection generated 21,329 observations. The results showed that the municipalities of the state of Amapá had better relative expenditure averages, it was also observed that the municipalities in the Northeast region show the worst results. The municipality of Itamaracá, located in the state of Pernambuco, presented the best percentage of the country, 9.47% of the environmental expenditure in relation to total expenditure. Regarding the results of panel data, the variables revenue, population, density, GDP, and HDI presented significance in all models; the models estimated by fixed effects ensure the consistency of the parameters.     

Model selection criteria for overdispersed data and their application to the characterization of a host-parasite relationship

In the statistical modeling of a biological or ecological phenomenon, selecting an optimal model among a collection of candidates is a critical issue. To identify an optimal candidate model, a number of model selection criteria have been developed and investigated based on estimating Kullback’s (Information theory and statistics. Dover, Mineola, 1968) directed or symmetric divergence. Criteria that target the directed divergence include the Akaike (2nd international symposium on information theory. Akadémia Kiadó, Budapest, Hungary, pp 267–281, 1973, IEEE Trans Autom Control AC 19:716–723, 1974) information criterion, AIC, and the “corrected” Akaike information criterion (Hurvich and Tsai in Biometrika 76:297–307, 1989), AICc; criteria that target the symmetric divergence include the Kullback information criterion, KIC, and the “corrected” Kullback information criterion, KICc (Cavanaugh in Stat Probab Lett 42:333–343, 1999; Aust N Z J Stat 46:257–274, 2004). For overdispersed count data, simple modifications of AIC and AICc have been increasingly utilized: specifically, the quasi Akaike information criterion, QAIC, and its corrected version, QAICc (Lebreton et al. in Ecol Monogr 62(1):67–118 1992). In this paper, we propose analogues of QAIC and QAICc based on estimating the symmetric as opposed to the directed divergence: QKIC and QKICc. We evaluate the selection performance of AIC, AICc, QAIC, QAICc, KIC, KICc, QKIC, and QKICc in a simulation study, and illustrate their practical utility in an ecological application. In our application, we use the criteria to formulate statistical models of the tick (Dermacentor variabilis) load on a white-footed mouse (Peromyscus leucopus) in northern Missouri.     

Identification of the largest individual sample value using composite sample data and certain modifications of the sweep-out method

Cost-effective hotspot identification is an important issue in hazardous waste site characterization and evaluation. Composite sampling techniques are known to be cost effective when the cost of measurement is substantially higher than the cost of sampling. Although compositing incurs no loss of information on the means, information on individual sample values is lost due to compositing. In particular, if the interest is in identifying the largest individual sample value, the composite sampling techniques are not able to do so. Under certain assumptions, it may be possible to satisfactorily predict individual sample values using the composite sample data, but it is not generally possible to identify the largest individual sample value. In this paper, we propose two methods of identifying the largest individual sample value with some additional measurement effort. Both methods are modifications of the simple sweep-out method proposed earlier. Since analytical results do not seem to be feasible, performance of the proposed methods is assessed via simulation. The simulation results show that both the proposed methods, namely the locally sequential sweep-out and the globally sequential sweep-out, are better than the simple sweep-out method.Prepared with partial support from the Statistical Analysis and Computing Branch, Environmental Statistics and Information Division, Office of Policy, Planning, and Evaluation, United States Environmental Protection Agency, Washington, DC under a Cooperative Agreement Number CR-821531. The contents have not been subjected to Agency review and therefore do not necessarily reflect the views of the Agency and no official endorsement should be inferred.     

Sensitivity of species-distribution models to error,bias, and model design: An application to resource selection functions for woodland caribou

Models that predict distribution are now widely used to understand the patterns and processes of plant and animal occurrence as well as to guide conservation and management of rare or threatened species. Application of these methods has led to corresponding studies evaluating the sensitivity of model performance to requisite data and other factors that may lead to imprecise or false inferences. We expand upon these works by providing a relative measure of the sensitivity of model parameters and prediction to common sources of error, bias, and variability. We used a one-at-a-time sample design and GPS location data for woodland caribou (Rangifer tarandus caribou) to assess one common species-distribution model: a resource selection function. Our measures of sensitivity included change in coefficient values, prediction success, and the area of mapped habitats following the systematic introduction of geographic error and bias in occurrence data, thematic misclassification of resource maps, and variation in model design. Results suggested that error, bias and model variation have a large impact on the direct interpretation of coefficients. Prediction success and definition of important habitats were less responsive to the perturbations we introduced to the baseline model. Model coefficients, prediction success, and area of ranked habitats were most sensitive to positional error in species locations followed by sampling bias, misclassification of resources, and variation in model design. We recommend that researchers report, and practitioners consider, levels of error and bias introduced to predictive species-distribution models. Formal sensitivity and uncertainty analyses are the most effective means for evaluating and focusing improvements on input data and considering the range of values possible from imperfect models.     

Incorporating spatial autocorrelation into cellular automata model: An application to the dynamics of Chinese tamarisk (Tamarix chinensis Lour.)

Spatial autocorrelation (SAC) is frequently encountered in most spatial data in ecology. Cellular automata (CA) models have been widely used to simulate complex spatial phenomena. However, little has been done to examine the impact of incorporating SAC into CA models. Using image-derived maps of Chinese tamarisk (Tamarix chinensis Lour.), CA models based on ordinary logistic regression (OLCA model) and autologistic regression (ALCA model) were developed to simulate landscape dynamics of T. chinensis. In this study, significant positive SAC was detected in residuals of ordinary logistic models, whereas non-significant SAC was found in autologistic models. All autologistic models obtained lower Akaike's information criterion corrected for small sample size (AICc) values than the best ordinary logistic models. Although the performance of ALCA models only satisfied the minimum requirement, ALCA models showed considerable improvement upon OLCA models. Our results suggested that the incorporation of the autocovariate term not only accounted for SAC in model residuals but also provided more accurate estimates of regression coefficients. The study also found that the neglect of SAC might affect the statistical inference on underlying mechanisms driving landscape changes and obtain false ecological conclusions and management recommendations. The ALCA model is statistically sound when coping with spatially structured data, and the adoption of the ALCA model in future landscape transition simulations may provide more precise probability maps on landscape transition, better model performance and more reasonable mechanisms that are responsible for landscape changes.     

Unintended consequences of price controls: An application to allowance markets

Price controls established in a cap-and-trade allowance market are intended to reduce cost uncertainty by constraining allowance prices between a ceiling and floor; however, they could provide opportunities for strategic actions by firms that would lower government revenue and increase emissions. In particular, when the ceiling price is supported by introducing new allowances into the market, firms could choose to buy allowances at the ceiling price, regardless of the prevailing market price, in order to lower the equilibrium price of all allowances. Those purchases could either be transacted by firms intending to manipulate the market price or be induced through the introduction of inaccurate information about the cost of emissions abatement. Theory and simulations using allowance elasticity estimates for U.S. firms suggest that the manipulation could be profitable under the stylized setting and assumptions evaluated in the paper, although in practice many other conditions will determine its use.     

A holistic approach to the development of sustainable agriculture: application of the ecosystem health model

Serious resource depletion has made sustainable agriculture an important and pressing issue for scientists, policy makers, and stakeholders worldwide, particularly in developing countries. Researchers have focused on methods to assess agriculture correctly, and to introduce sound solutions for sustainability, but have reached no agreement. In this paper, we introduce the theoretical framework of the agro-ecosystem health model, a new holistic approach, and apply it at a regional scale using four aspects (4S): sound structure, stable function, safe service, and sustainable development. We examine how 12 indicators of an agro-ecosystem health assessment (AHA) were selected using three dimensions based on this theoretical framework. In an AHA, we used an amoeba approach to examine a high-yield agro-ecosystem in Huantai County, Shandong Province, China. The results indicate that this model of ecosystem health can reflect the complex ecological, economic, and human conditions of an agro-ecosystem and evaluate these conditions using perspectives pertinent to system structure, function, and responses (services).