The estimation of growth yield and maintenance parameters for photoautotrophic growth

Methods are presented for examining the consistency of experimental data for microbial growth where light energy is converted to chemical energy through photosynthesis. True growth yield and maintenance parameters are estimated for several sets of available experimental data. Methods of parameter estimation are presented which allow all of the measured variables to be used simultaneously for parameter estimation. The results show that a wide range of values have been found for the true growth yield and maintenance parameters. Values of the true growth yield range from 0.04 to values above those predicted by the Z-scheme model for photosynthesis.     

The estimation of algal yield parameters associated with mixotrophic and photoheterotrophic growth under batch cultivation

The true growth yield and maintenance coefficient for algal growth under batch cultivation are estimated by employing a modified yield model used for continuous systems. The data of aerobic and anaerobic growth are utilized for mixotrophic and photoheterotrophic growth. Chlorella species show the highest bioenergetic yield of 0·6802 for aerobic and mixotrophic growth. Yeast extract proved to be a good nutrient with a high growth yield of 0·7838. Aerobic and mixotrophic growth required less maintenance energy than anaerobic and photoheterotrophic growth: 0·0057 (1/h) and 0·0494 (1/h) for aerobic and anaerobic growth, respectively. It is concluded that mixotrophic growth utilizing glucose is the most efficient process for producing photosynthetic biomass.     

Evaluation of data consistency and estimation of yield parameters in hydrocarbon fermentations

The consistency of experimental data for hydrocarbon fermentations is reviewed using carbon and available electron balances and the mean values of the regularities for carbon weight fraction in biomass and biomass reductance degree. True growth yields and maintenance coefficients are estimated from both batch and continuousculture data and the results are compared.     

Honeydew composition and its effect on life-history parameters of hyperparasitoids

1. Diets that maximise life span often differ from diets that maximise reproduction. Animals have therefore evolved advanced foraging strategies to acquire optimal nutrition and maximise their fitness. The free-living adult females of parasitoid wasps (Hymenoptera) need to balance their search for hosts to reproduce and for carbohydrate resources to feed. 2. Honeydew, excreted by phloem-feeding insects, presents a widely available carbohydrate source in nature that can benefit natural enemies of honeydew-producing insects. However, the effects of variation in honeydew on organisms in the fourth trophic level, such as hyperparasitoids, are not yet understood. 3. This study examined how five different honeydew types influence longevity and fecundity of four hyperparasitoid taxa. Asaphes spp. (Pteromalidae) and Dendrocerus spp. (Megaspilidae) are secondary parasitoids of aphid parasitoids and are thus associated with honeydew-producing insects. Gelis agilis and Acrolyta nens (both Ichneumonidae) are secondary parasitoids of species that do not use honeydew-producing hosts. 4. Most honeydew types had a positive or neutral effect on life span and fecundity of hyperparasitoids compared with controls without honeydew, although negative effects were also found for both aphid hyperparasitoids. Honeydew produced by aphids feeding on sweet pepper plants was most beneficial for all hyperparasitoid taxa, which can partially be explained by the high amount of honeydew, but also by the composition of dietary sugars in these honeydew types. 5. The findings of this study underline the value of aphid honeydew as a carbohydrate resource for fourth-trophic-level organisms, not only those associated with honeydew-producing insects but also ‘interlopers’ without such a natural association.     

Incongruence between life-history traits and conservation status in reef corals

Coral Reefs - Comparative lists of species’ extinction risk are increasingly being used to prioritise conservation resources. Extinction risk is most rigorously assessed using quantitative...     

Hamilton goes empirical: estimation of inclusive fitness from life-history data

Hamilton's theory of kin selection is one of the most important advances in evolutionary biology since Darwin. Central to the kin-selection theory is the concept of inclusive fitness. However, despite the importance of inclusive fitness in evolutionary theory, empirical estimation of inclusive fitness has remained an elusive task. Using the concept of individual fitness, I present a method for estimating inclusive fitness and its components for diploid organisms with age-structured life histories. The method presented here: (i) allows empirical estimation of inclusive fitness from life-history data; (ii) simultaneously considers all components of fitness, including timing and magnitude of reproduction; (iii) is consistent with Hamilton's definition of inclusive fitness; and (iv) adequately addresses shortcomings of existing methods of estimating inclusive fitness. I also demonstrate the application of this new method for testing Hamilton's rule.     

Early social status and the development of life-history strategies in Atlantic salmon

Atlantic salmon have a variable life cycle. In good growing conditions, underyearling fish may metamorphose into the migratory smolt phase during their second spring, or delay at least a further year. The strategy adopted by particular fish appears to become fixed during their first summer. This paper examines whether either feeding efficiency or dominance in mid-summer correlates with the life-history strategy adopted. Eighty fish were individually marked and their feeding efficiency (= mean handling time for food items) and dominance rank measured under laboratory conditions in mid-July. Growth rates of the fish were then monitored over the next three months, until developmental strategies became apparent. Discriminant and logistic regression analyses revealed that both dominance rank and size attained by July were independent, significant predictors of future developmental pattern (the age at metamorphosis being correctly predicted on the basis of rank and size in 84% of cases) whereas feeding efficiency had no effect. Thus fish that were dominant or larger two months after first feeding or both had a greater probability of migrating after only one year in freshwater than those more subordinate or smaller or both.     

Adjoint multi-start-based estimation of cardiac hyperelastic material parameters using shear data

Cardiac muscle tissue during relaxation is commonly modeled as a hyperelastic material with strongly nonlinear and anisotropic stress response. Adapting the behavior of such a model to experimental or patient data gives rise to a parameter estimation problem which involves a significant number of parameters. Gradient-based optimization algorithms provide a way to solve such nonlinear parameter estimation problems with relatively few iterations, but require the gradient of the objective functional with respect to the model parameters. This gradient has traditionally been obtained using finite differences, the calculation of which scales linearly with the number of model parameters, and introduces a differencing error. By using an automatically derived adjoint equation, we are able to calculate this gradient more efficiently, and with minimal implementation effort. We test this adjoint framework on a least squares fitting problem involving data from simple shear tests on cardiac tissue samples. A second challenge which arises in gradient-based optimization is the dependency of the algorithm on a suitable initial guess. We show how a multi-start procedure can alleviate this dependency. Finally, we provide estimates for the material parameters of the Holzapfel and Ogden strain energy law using finite element models together with experimental shear data.     

Rapid estimation of potential yield for data-poor Tapes philippinarum fisheries in North Adriatic coastal lagoons

We show how a simple species distribution model can be used for the rapid estimation of potential yield and for the identification of suitable sites for farming of Tapes philippinarum in two North Adriatic lagoons (Caleri and Marinetta-Vallona, Italy) in the face of limited data. We used a two-part species distribution model with sediment type, hydrodynamism, dissolved oxygen, and salinity as predictors of T. philippinarum potential yield. The first model component uses logistic regression to identify the areas in which clams occur, while the second component uses a weighted geometric mean of suitability values to estimate the potential annual yield (kg m^?2 year^?1) for the sites where T. philippinarum is predicted to be present. We used site-specific yield data from Caleri and Marinetta-Vallona to estimate the weights of the geometric mean by constrained linear regression. We validated the two-part model on an independent set of yield data (R _adj ²  = 0.82), and we then estimated the spatial distribution of potential yield in the two lagoons. The calibration and application of a simple species distribution model are useful tools for objectively identifying the most suitable sites for farming of T. philippinarum in North Adriatic lagoons.     

EEG-based estimation of mental fatigue by using KPCA–HMM and complexity parameters

Two complexity parameters of EEG, i.e. approximate entropy (ApEn) and Kolmogorov complexity (Kc) are utilized to characterize the complexity and irregularity of EEG data under the different mental fatigue states. Then the kernel principal component analysis (KPCA) and Hidden Markov Model (HMM) are combined to differentiate two mental fatigue states. The KPCA algorithm is employed to extract nonlinear features from the complexity parameters of EEG and improve the generalization performance of HMM. The investigation suggests that ApEn and Kc can effectively describe the dynamic complexity of EEG, which is strongly correlated with mental fatigue. Both complexity parameters are significantly decreased (P < 0.005) as the mental fatigue level increases. These complexity parameters may be used as the indices of the mental fatigue level. Moreover, the joint KPCA–HMM method can effectively reduce the dimensionality of the feature vectors, accelerate the classification speed and achieve higher classification accuracy (84%) of mental fatigue. Hence KPCA–HMM could be a promising model for the estimation of mental fatigue.     

The May threshold and life-history allometry

One of Robert May''s classic results was finding that population dynamics become chaotic when the average lifetime rate of reproduction exceeds a certain value. Populations whose reproductive rates exceed this May threshold probably become extinct. The May threshold in each case depends upon the shape of the density-dependence curve, which differs among models of population growth. However, species of different sizes and generation times that share a roughly similar density-dependence curve will also share a similar May threshold. Here, we argue that this fact predicts a striking allometric regularity among animal taxa: lifetime reproductive rate should be roughly independent of body size. Such independence has been observed in diverse taxa, but has usually been ascribed to a fortuitous combination of physiologically based life-history allometries. We suggest, instead, that the ecological elimination of unstable populations within groups that share a value of the May threshold is a likely cause of this allometry.     

Joint estimation of immigration and mating system parameters in gymnosperms using the EM algorithm

Summary An EM algorithm procedure is presented for the maximum-likelihood joint estimation of immigration and mating system parameters of mixed-mating system models for gymnosperms. In addition to accommodating multiallelic and multilocus data in mature populations and pollen pools, the EM estimates are insensitive to allelic frequency changes in foreign population and may approach closer to global maximum-likelihood estimates with each iteration, regardless of initial starting values. Estimates of rates of selfing (), outcrossing (Ô), and immigration (Î) derived from the model are bounded strictly within the natural biological range (i.e., 0 Ô + Î 1; + Ô + Î = 1).