The pattern of species turnover resulting from stochastic population dynamics: The model and field data

The model of random population dynamics in a sampling site returns geometric distribution of longevities of continuous presence (=persistence) and Poisson distribution of the presence–absence transitions. This discrete-time stochastic process describes the presence–absence pattern observed in the beetles surveyed 6 years on Mount Carmel, Israel. Homogeneous pools of species mostly on the Families rank, exhibit the predicted by the model patterns. Conformity to an ergodic hypothesis is the criterion of ecological homogeneity. This criterion assumes the equivalence of short-term behavior of entire pool and long-term behavior of any species from this pool. The pool of all 801 species of Order Coleoptera does not match the model. Thus a taxon of an arbitrary rank may not be considered a priory as a unit of ecological study. Determined from field data parameters of the model are biased and magnitude of the bias depends on longevity of the survey. Parameter of distribution depends also on species tolerance, which is the level adaptation of given species to given environment in given time interval. Random process of species turnover may be considered as a game of species to gain their presence against deteriorative fluctuations of environmental conditions.     

几种事故预测方法的探讨

本文根据生产单位事故发生的随机性特点,论证了未来时段中事故次数及其时间分布等各项指标,是能够用多种随机数学模型和灰色系统理论来预测的。用非随机的函数模型预測事故是不合适的。给出了几种事故预測方法,并提出了几种值得研究和试用的方法。     

Role of competition in phytoplankton population for the occurrence and control of plankton bloom in the presence of environmental fluctuations

Termination of harmful algal blooms (HABs) and coexistence of phytoplankton–zooplankton populations are of great importance to human health, ecosystem, environment, tourism and fisheries. In this paper, we propose a three component model consisting of non-toxic phytoplankton (NTP), toxin producing phytoplankton (TPP) and zooplankton (Z). The growth of zooplankton species is assume to reduce due to toxic chemicals released by TPP population. We have extended the model proposed by Chattopadhyay et al. [Chattopadhyay, J., Sarkar, R.R., Pal, S., 2004. Mathematical modelling of harmful algal blooms supported by experimental findings. Ecol. Comp. 1, 225–235] by including competition terms between TPP and NTP. We observe the effect of competition factors both in the presence and absence of the environmental fluctuation. From our field as well as model analysis we observe that competition helps in the coexistence of the species, but if the effect of competition is very high on the TPP population, it results in planktonic bloom. It is shown that the coexistence equilibrium loses its stability when the competition coefficient crosses a critical value and resulting Hopf-bifurcation around the positive equilibrium depicting oscillations phenomena of the populations.     

A Markov model for assessing ecological stability properties

Ecological systems are frequently modeled as dynamic systems. It is natural then to use the techniques of dynamic systems theory to analyze such models. However, the methods and results that are produced by dynamic systems theory do not always capture the aspects of ecological systems that are of greatest interest to managers and decision-makers. We identify some of the challenges of using dynamic systems theory to explore ecological systems and propose an alternative approach that emphasizes the understanding of transient effects in light of uncertainty and variability. We illustrate this method by an examination of a model for phosphorus levels in fresh water lakes.     

Selecting parameters for calibration via sensitivity analysis: An individual-based model of mosquitofish population dynamics

A stochastic individual-based model (IBM) of mosquitofish population dynamics in experimental ponds was constructed in order to increase, virtually, the number of replicates of control populations in an ecotoxicology trial, and thus to increase the statistical power of the experiments. In this context, great importance had to be paid to model calibration as this conditions the use of the model as a reference for statistical comparisons. Accordingly, model calibration required that both mean behaviour and variability behaviour of the model were in accordance with real data. Currently, identifying parameter values from observed data is still an open issue for IBMs, especially when the parameter space is large. Our model included 41 parameters: 30 driving the model expectancy and 11 driving the model variability. Under these conditions, the use of “Latin hypercube” sampling would most probably have “missed” some important combinations of parameter values. Therefore, complete factorial design was preferred. Unfortunately, due to the constraints of the computational capacity, cost-acceptable “complete designs” were limited to no more than nine parameters, the calibration question becoming a parameter selection question. In this study, successive “complete designs” were conducted with different sets of parameters and different parameter values, in order to progressively narrow the parameter space. For each “complete design”, the selection of a maximum of nine parameters and their respective n values was carefully guided by sensitivity analysis. Sensitivity analysis was decisive in selecting parameters that were both influential and likely to have strong interactions. According to this strategy, the model of mosquitofish population dynamics was calibrated on real data from two different years of experiments, and validated on real data from another independent year. This model includes two categories of agents; fish and their living environment. Fish agents have four main processes: growth, survival, puberty and reproduction. The outputs of the model are the length frequency distribution of the population and the 16 scalar variables describing the fish populations. In this study, the length frequency distribution was parameterized by 10 scalars in order to be able to perform calibration. The recently suggested notion of “probabilistic distribution of the distributions” was also applied to our case study, and was shown to be very promising for comparing length frequency distributions (as such).     

Optimal control of lake pH for mercury bioaccumulation control

Mercury is recognized internationally as an important pollutant since mercury and its compounds are persistent, bioaccumulative and toxic, and pose human and ecosystem risks. A critical aspect of mercury cycling is its bioaccumulation, mainly as methylmercury, along the aquatic food web resulting in high risk of human exposure through contaminated fish consumption. Since lake acidity (pH) and mercury methylation are correlated, control of lake pH through lake liming is a possible option to mitigate mercury bioaccumulation. This work proposes to use optimal control theory to derive time-dependent lake liming strategies for a tighter control of lake pH. Since the behavior of the freshwater ecosystems such as lakes is often associated with considerable uncertainties, a robust and realistic analysis should incorporate such uncertainties. This work models the time-dependent uncertain variations in the basic lake pH value and derives the liming profiles in the presence of such seasonal pH fluctuations. Established techniques from real options theory are employed for modeling the uncertainty as a stochastic process, and stochastic optimal control is used for deriving liming profiles. The approach is critically evaluated through applications to various case study lakes. Considering the substantial costs associated with liming operations, the work formulates a multi-objective problem highlighting the tradeoff between accurate pH control and liming cost. The results of the control problem solution are also compared with heuristics based liming. The results, while highlighting the success of using time-dependent liming, put forth certain interesting aspects that might be helpful to a decision maker. The analysis is expected to make liming operation more reliable, thereby presenting one more tool to manage the harmful effects of mercury pollution.     

Implications of network particle tracking (NPT) for ecological model interpretation

Network particle tracking (NPT), building on the foundation of network environ analysis (NEA), is a new development in the definition of coherence relations within and between connected systems. This paper evaluates three ecosystem models in a comparison of throughflow- and storage-based NEA and NPT. Compartments in models with high indirect effects and Finn cycling showed low correlation of NEA storage and throughflow with particle repeat visits and numbers of particles in compartments at steady state. Conversely, the correlation between NEA and NPT results was high with two models having lower indirect effects and Finn cycling. Analysis of ecological orientors associated with NEA showed NPT to fully support conventional NEA results when the common conditions of donor control and steady state are satisfied. Particle trajectories are recorded in the new concept of a particle “passport”. Ability to track and record particle in-system histories enables views of multiple scales and opens the possibility of making pathway-dependent modeling decisions. NPT may also enable modeling of time, allowing integration of Newtonian, organismal and stochastic modeling perspectives in a single comprehensive analysis.     

SLFP: a stochastic linear fractional programming approach for sustainable waste management

A stochastic linear fractional programming (SLFP) approach is developed for supporting sustainable municipal solid waste management under uncertainty. The SLFP method can solve ratio optimization problems associated with random information, where chance-constrained programming is integrated into a linear fractional programming framework. It has advantages in: (1) comparing objectives of two aspects, (2) reflecting system efficiency, (3) dealing with uncertainty expressed as probability distributions, and (4) providing optimal-ratio solutions under different system-reliability conditions. The method is applied to a case study of waste flow allocation within a municipal solid waste (MSW) management system. The obtained solutions are useful for identifying sustainable MSW management schemes with maximized system efficiency under various constraint-violation risks. The results indicate that SLFP can support in-depth analysis of the interrelationships among system efficiency, system cost and system-failure risk.     

The theoretical value of encounters with parasitized hosts for parasitoids

A female parasitoid searching for hosts in a patch experiences a diminishing encounter rate with unparasitized and thus suitable hosts. To use the available time most efficiently, it constantly has to decide whether to stay in the patch and continue to search for hosts or to search for and travel to another patch in the habitat. Several informational cues can be used to optimize the searching success. Theoretically, encounters with unparasitized hosts should lead to a prolonged search in a given patch if hosts are distributed contagiously. The results of empirical studies strongly support this hypothesis. However, it has, to date, not been investigated theoretically whether encounters with already parasitized hosts (which usually entail time costs) provide a parasitoid with valuable information for the optimization of its search in depletable patches, although the empirical studies concerning this question so far have produced ambiguous results. Building on recent advances in Bayesian foraging strategies, we approached this problem by modeling a priori searching strategies (which differ in the amount of information considered) and then testing them in computer simulations. By comparing the strategies, we were able to determine whether and how encounters with already parasitized hosts can yield information that can be used to enhance a parasitoid’s searching success.

Global energy and emissions scenarios for effective climate change mitigation—Deterministic and stochastic scenarios with the TIAM model

The achievement possibilities of the EU 2 °C climate target have been assessed with the ETSAP TIAM global energy systems model. Cost-effective global and regional mitigation scenarios of carbon dioxide, methane, nitrous oxide and F-gases were calculated with alternative assumptions on emissions trading. In the mitigation scenarios, an 85% reduction in CO₂ emissions is needed from the baseline, and very significant changes in the energy system towards emission-free sources take place during this century. The largest new technology groups are carbon-capture and storage (CCS), nuclear power, wind power, advanced bioenergy technologies and energy efficiency measures. CCS technologies contributed a 5.5-Pg CO₂ annual emission reduction by 2050 and 12 Pg CO₂ reduction by 2100. Also large-scale forestation measures were found cost-efficient. Forestation measures reached their maximum impact of 7.7 Pg CO₂ annual emission reduction in 2080. The effects of uncertainties in the climate sensitivity have been analysed with stochastic scenarios.