Algal Population Growth Model Integrated with Toxicokinetics for Ecological Risk Assessment under Time-Varying Pesticide Exposure

An algal population growth model integrated with toxicokinetics was developed for assessing the effect of pesticides on population dynamics. This model is a simple one-compartment, first-order kinetic model in which toxicity (growth inhibition and mortality) depends on the intracellular effective concentration of a pesticide at a target site. The model's parameters were derived using an experimental study that investigated the effects of pretilachlor, bensulfuron-methyl, pentoxazone, and quinoclamine on the growth, mortality, and subsequent population recovery of the green alga Pseudokirchneriella subcapitata. Modeled and measured trajectories of algal population dynamics agreed well. The effect on population recovery was underestimated by the model that ignored the toxicokinetics. The four tested herbicides had a variety of toxicity characteristics and physicochemical properties, indicating the wide range of the model's applicability. Moreover, the developed model and the obtained model's parameters were extrapolated to predict long-term algal population dynamics under time-varying herbicide exposure. The calculated integral biomass lost compared with the control was considered a quantitative index of the population-level ecological risk. The model's prediction showed that the same exposure level (peak concentration is equivalent to EC50) indicated much different population-level effect depending on the herbicide.     

Modelling Human Granulopoiesis under Poly-chemotherapy with G-CSF Support

Cytotoxic drugs administered in polychemotherapy cause a characteristic neutropenic period depending on the schedule of the drugs, which can partly be prevented by G-CSF growth factor support. To quantify these effects and to gain a deeper insight into the dynamics of bone marrow recovery after such suppressing and stimulating disturbances, we construct a biomathematical compartment model of human granulopoiesis under polychemotherapy with G-CSF support. The underlying assumptions and mathematical techniques used to obtain the model are explained in detail. A large variety of biological and clinical data as well as knowledge from a model of murine haematopoiesis are evaluated to construct a physiological model for humans.Particular emphasis is placed on estimating the influence of chemotherapeutic drugs on the granulopoietic system. As a result, we present an innovative method to estimate the bone marrow damage caused by cytotoxic drugs with respect to single identifiable cell stages only on the basis of measured peripheral blood leukocyte dynamics. Conversely, our model can be used in a planning phase of a clinical trial to estimate the haematotoxicity of regimens based on new combinations of drugs already considered and with or without growth factor support.Acknowledgement This paper was supported by the DFG (Deutsche Forschungsgemeinschaft) in the framework of the project Aufbau von Simulationsmodellen der h{\a}matopoetischen Dynamik nach konventioneller und hochdosierter Chemotherapie und Zytokingabe beim Menschen (Nr. LO 342/8-2). We would like to thank the German High Grade Non-Hodgkins-Lymphoma Study Group and the German Hodgkins Lymphoma Study Group for the kind provision of data.     

Comparison of life history traits between invasive and native populations of purple loosestrife (Lythrum salicaria) using nonlinear mixed effects model

We compared growth patterns of invasive and native populations of purple loosestrife (Lythrum salicaria) while varying water and nutrient levels. We examined three life-history traits (height, number of branches, and the size of largest leaf) during the growth period adopting a nonlinear mixed effects model. Invasive populations were found to be slower in shoot elongation but grew to be taller than native populations. Invasive populations produced more branches than natives only in the high water, high nutrient treatment. Invasive populations had a similar increase in the size of the largest leaf compared to natives, but ultimately produced a greater size of largest leaf than natives. Invasive populations were found to display a greater vegetative expansion, but this was not strongly affected by our treatments.     

Scale-dependent interactions between intrinsic and extrinsic processes reduce variability in protist populations

Interactions between intrinsic processes and extrinsic fluctuations can positively impact population persistence in ways often not predicted by classic ecological models. These interactions only arise when the intrinsic and extrinsic processes operate on the proper relative scales in time or space. Both metapopulation theory and resonance/attenuation theory suggest that interactions which lower population variability will occur when the intrinsic and extrinsic process occur on similar time scales. I performed an aquatic protist microcosm experiment to investigate how the relative frequencies of extrinsic density perturbations and intrinsic resource pulses impacted population variability. Population variability was lowest in the treatments of intermediate frequency, in which the extrinsic fluctuations and intrinsic processes were on the same time scale. This result is consistent with general theoretical predictions, and empirically documents the importance of considering scale in interactions between intrinsic and extrinsic processes that positively impact population persistence.     

A characterization model with spatial and temporal resolution for life cycle impact assessment of photochemical precursors in the United States

Background, aim, and scope  Traditional life cycle impact assessment methodologies have used aggregated characterization factors, neglecting spatial and temporal variations in regional impacts like photochemical oxidant formation. This increases the uncertainty of the LCA results and diminishes the ease of decision-making. This study compares four common impact assessment methods, CML2001, Eco-indicator 99, TRACI, and EDIP2003, on their underlying models, spatial and temporal resolution, and the level at which photochemical oxidant impacts are calculated. A new characterization model is proposed that incorporates spatial and temporal differentiation. Materials and methods  A photochemical air quality modeling system (CAMx-MM5-SMOKE) is used to simulate the process of formation, transformation, transport, and removal of photochemical pollutants. Monthly characterization factors for individual US states are calculated at three levels along the cause–effect chain, namely, fate level, human and ecosystem exposure level, and human effect level. Results and discussion  The results indicate that a spatial variability of one order of magnitude and a temporal variability of two orders of magnitude exist in both the fate level and human exposure and effect level characterization factors for NO_x. The summer time characterization factors for NO_x are higher than the winter time factors. However, for anthropogenic VOC, the summer time factors are lower than the winter time in almost half of the states. This is due to the higher emission rates of biogenic VOCs in the summer. The ecosystem exposure factors for NO_x and VOC do not follow a regular pattern and show a spatial variation of about three orders of magnitude. They do not show strong correlation with the human exposure factors. Sensitivity analysis has shown that the effect of meteorology and emission inputs is limited to a factor of three, which is several times smaller than the variation seen in the factors. Conclusions  Uncertainties are introduced in the characterization of photochemical precursors due to a failure to consider the spatial and temporal variations. Seasonal variations in photochemical activity influence the characterization factors more than the location of emissions. The human and ecosystem exposures occur through different mechanisms, and impacts calculated at the fate level based only on ozone concentration are not a good indicator for ecosystem impacts. Recommendations and perspectives  Spatial and temporal differentiation account for fate and transport of the pollutant, and the exposure of and effect on the sensitive human population or ecosystem. Adequate resolution for seasonal and regional processes, like photochemical oxidant formation, is important to reduce the uncertainty in impact assessment and improve decision-making power. An emphasis on incorporating some form of spatial and temporal information within standard LCI databases and using adequately resolved characterization factors will greatly increase the fidelity of a standard LCA. Electronic supplementary material  The online version of this article (doi:) contains supplementary material, which is available to authorized users.