Consumer pressure, seed versus safe-site limitation, and plant population dynamics

Plants often suffer reductions in fecundity due to insect herbivory. Whether this loss of seeds has population-level consequences is much debated and often unknown. For many plants, particularly those with long-lived seedbanks, it is frequently asserted that herbivores have minimal impacts on plant abundance because safe-site availability rather than absolute seed number determines the magnitude of future plant recruitment and hence population abundance. However, empirical tests of this assertion are generally lacking and the interplay between herbivory, spatio-temporal variability in seed- or safe-site-limited recruitment, and seedbank dynamics is likely to be complex. Here we use a stochastic simulation model to explore how changes in the spatial and temporal frequency of seed-limited recruitment, the strength of density-dependent seedling survival, and longevity of seeds in the soil influence the population response to herbivory. Model output reveals several surprising results. First, given a seedbank, herbivores can have substantial effects on mean population abundance even if recruitment is primarily safe-site-limited in either time or space. Second, increasing seedbank longevity increases the population effects of herbivory, because annual reductions in seed input due to herbivory are accumulated in the seedbank. Third, population impacts of herbivory are robust even in the face of moderately strong density-dependent seedling mortality. These results imply that the conditions under which herbivores influence plant population dynamics may be more widespread than heretofore expected. Experiments are now needed to test these predictions. Received: 3 November 1999 / Accepted: 15 February 2000     

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.     

The impacts of agricultural payments on groundwater quality: Spatial analysis on the case of Slovenia

The European Common Agricultural Policy still follows its primary goals, i.e. quality food at affordable prices and a decent standard of living for farmers, fifty years after its adoption. Moreover, this policy adapts to the changing needs of society and the new challenges, mostly preservation of the environment, nature and biodiversity in rural areas. Although the Common Agricultural Policy receives the largest share of European budget, the funds are decreasing over time, especially direct payments, which aim to provide basic income support to farmers in the European Union. On the other hand, agri-environmental payments are gaining importance. Policy decision-makers should be interested in the question of impacts of growing eco-conditionality of agricultural spending. New insights would help them to be successful in achieving the goals of sustainable agriculture. The purpose of this paper is to estimate the impacts of production support payments and rural development payments on the quality of groundwater. We use the small EU country Slovenia as an example. The baseline indicators are the level of nitrates and pesticides in groundwater, while the impacts were estimated using spatial error model. The results show that direct payments, coupled subsidies and investment grants raise the level of pesticides in groundwater, but do not have any statistically significant impact on the level of nitrates in groundwater. Furthermore, we did not find any statistically significant effects of agri-environmental payments on decrease of groundwater pollution with nitrates. However, our findings revealed that agri-environmental payments are effective in reducing pesticides in groundwater, although only to a limited extent. These results imply a problem of insufficient targeting of agri-environmental measures on the one hand, and suggest that greening of direct payments is necessary and entirely justified.     

Modelling HIV immune response and validation with clinical data

A system of ordinary differential equations is formulated to describe the pathogenesis of HIV infection, wherein certain features that have been shown to be important by recent experimental research are incorporated in the model. These include the role of CD4+memory cells that serve as a major reservoir of latently infected cells, a critical role for T-helper cells in the generation of CD8 memory cells capable of efficient recall response, and stimulation by antigens other than HIV. A stability analysis illustrates the capability of this model in admitting multiple locally asymptotically stable (locally a.s.) off-treatment equilibria. We show that this more biologically detailed model can exhibit the phenomenon of transient viremia experienced by some patients on therapy with viral load levels suppressed below the detection limit. We also show that the loss of CD4+T-cell help in the generation of CD8+memory cells leads to larger peak values for the viral load during transient viremia. Censored clinical data is used to obtain parameter estimates. We demonstrate that using a reduced set of 16 free parameters, obtained by fixing some parameters at their population averages, the model provides reasonable fits to the patient data and, moreover, that it exhibits good predictive capability. We further show that parameter values obtained for most clinical patients do not admit multiple locally a.s off-treatment equilibria. This suggests that treatment to move from a high viral load equilibrium state to an equilibrium state with a lower (or zero) viral load is not possible for these patients.     

An actuated dissipative spring-mass walking model: Predicting human-like ground reaction forces and the effects of model parameters

Simple models are widely used to understand the mechanics of human walking. The optimization-based minimal biped model and spring-loaded-inverted-pendulum (SLIP) model are two popular models that can achieve human-like walking patterns. However, ground reaction forces (GRF) from these two models still deviate from experimental data. In this paper, we proposed an actuated dissipative spring-mass model by integrating these two models to realize more human-like GRF patterns. We first explored the function of stiffness, damping, and weights of both energy cost and force cost in the objective function and found that these parameters have distinctly different influences on the optimized gait and GRF profiles. The stiffness and objective weight affect the number and size of peaks in the vertical GRF and stance time. The damping changes the relative size of the peaks but has little influence on stance time. Based on these observations, these parameters were manually tuned at three different speeds to approach experimentally measured vertical GRF and the highest correlation coefficient can reach 0.983. These results indicate that the stiffness, damping, and proper objective functions are all important factors in achieving human-like motion for this simple walking model. These findings can facilitate the understanding of human walking dynamics and may be applied in future biped models.     

Influences of temporal independence of data on modelling species distributions

Modelling species distributions has been widely used to understand present and future potential distributions of species, and can provide adaptation and mitigation information as references for conservation and management under climate change. However, various methods of data splitting to develop and validate functions of the models do not get enough attention, which may mislead the interpretation of predicted results. We used the Taiwanese endemic birds to test the influences of temporal independence of datasets on model performance and prediction. Training and testing data were considered to be independent if they were collected during different survey periods (1993–2004 and 2009–2010). The results indicated no significant differences of six model performance measures (AUC, kappa, TSS, accuracy, sensitivity, and specificity) among the combinations of training and testing datasets. Both species- and grid cell-based assessments differed significantly between predictions by the annual and pooled training data. We also found an average of 85.8% similarity for species presences and absences in different survey periods. The remaining dissimilarity was mostly caused by species observed in the late survey period but not in the early one. The method of data splitting, yielding training and testing data, is critical for resulting model species distributions. Even if similar model performance exists, different methods can lead to different species distributional maps. More attention needs to be given to this issue, especially when amplifying these models to project species distributions in a changing world.     

An integrated network analysis reveals that nitric oxide reductase prevents metabolic cycling of nitric oxide by Pseudomonas aeruginosa

Nitric oxide (NO) is a chemical weapon within the arsenal of immune cells, but is also generated endogenously by different bacteria. Pseudomonas aeruginosa are pathogens that contain an NO-generating nitrite (NO₂⁻) reductase (NirS), and NO has been shown to influence their virulence. Interestingly, P. aeruginosa also contain NO dioxygenase (Fhp) and nitrate (NO₃⁻) reductases, which together with NirS provide the potential for NO to be metabolically cycled (NO→NO₃⁻→NO₂⁻→NO). Deeper understanding of NO metabolism in P. aeruginosa will increase knowledge of its pathogenesis, and computational models have proven to be useful tools for the quantitative dissection of NO biochemical networks. Here we developed such a model for P. aeruginosa and confirmed its predictive accuracy with measurements of NO, O₂, NO₂⁻, and NO₃⁻ in mutant cultures devoid of Fhp or NorCB (NO reductase) activity. Using the model, we assessed whether NO was metabolically cycled in aerobic P. aeruginosa cultures. Calculated fluxes indicated a bottleneck at NO₃⁻, which was relieved upon O₂ depletion. As cell growth depleted dissolved O₂ levels, NO₃⁻ was converted to NO₂⁻ at near-stoichiometric levels, whereas NO₂⁻ consumption did not coincide with NO or NO₃⁻ accumulation. Assimilatory NO₂⁻ reductase (NirBD) or NorCB activity could have prevented NO cycling, and experiments with ΔnirB, ΔnirS, and ΔnorC showed that NorCB was responsible for loss of flux from the cycle. Collectively, this work provides a computational tool to analyze NO metabolism in P. aeruginosa, and establishes that P. aeruginosa use NorCB to prevent metabolic cycling of NO.