On the problems of the evolutionary optimization of life history. I. Markov model of Leslie life cycle and optimization of fertility

The paper considers the properties of individual life history corresponding to the Leslie model of age-structured population. The life history is modelled as a finite Markov chain with absorption at a death state of individual. In this model, individual longevity, average number of offspring R _L (produced by an individual over the entire life), and some other known characteristics of the life history have been derived using simple probability methods that do not involve matrix calculus and their individual components have been interpreted. In the linear Leslie population model (derived by simple modification of a Markov chain), R _L determines the growth or decline of a population. Individuals with higher R _L values have evolutionary advantages in the population due to accelerated growth in their number. The selection of fertility as a factor of the increase in R _L is considered. In the Leslie model, fertility is a set of correlated quantitative traits, where the age-specific fertility components are determined both by multiple loci and the environment. According to the genetic theory of quantitative trait selection, they evolve towards an increase in R _L . Taking into account the limited resources for reproduction, selection optimizes the fertility distribution according to age. Optimal distribution corresponds to the attainment of the maximum R _L . This complies with the maximization of the rate of population growth (r-selection), which is characteristic of linear population models. The search for the R _L maximum and optimal distribution of fertility belongs to the field of linear programming.     

Evolutionary optimization of life-history traits in the sea beet Beta vulgaris subsp. maritima: Comparing model to data

At evolutionary equilibrium, ecological factors will determine the optimal combination of life-history trait values of an organism. This optimum can be assessed by assuming that the species maximizes some criterion of fitness such as the Malthusian coefficient or lifetime reproductive success depending on the degree of density-dependence. We investigated the impact of the amount of resources and habitat stability on a plant's age at maturity and life span by using an evolutionary optimization model in combination with empirical data. We conducted this study on sea beet, Beta vulgaris subsp. maritima, because of its large variation in life span and age at first reproduction along a latitudinal gradient including considerable ecological variation. We also compared the consequence in our evolutionary model of maximizing either the Malthusian coefficient or the lifetime reproductive success. Both the data analysis and the results of evolutionary modeling pointed to habitat disturbance and resources like length of the growing season as factors negatively related to life span and age at maturity in sea beet. Resource availability had a negative theoretical influence with the Malthusian coefficient as the chosen optimality criterion, while there was no influence in the case of lifetime reproductive success. As suggested by previous theoretical work the final conclusion on what criterion is more adequate depends on the assumptions of how in reality density-dependence restrains population growth. In our case of sea beet data R₀ seems to be less appropriate than λ.     

三种温带树种叶片呼吸的时间动态及其影响因子

为认知叶片呼吸(RL)的季节变化格局及其影响因子,以东北东部山区3个主要树种(红松Pinus koraiensis、樟子松P.sylvestris var.mongolica和白桦Betula platyphylla)为对象,采用红外气体分析法在2011年生长季(常绿树4月至10月;落叶树6月至9月)测定了自然条件下叶片气体交换及其相关生理特征的季节变化,探索了RL与空气温度(Tair)和相关叶片特征之间的关系.结果表明:红松和樟子松基于叶面积的RL(RL-area)表现为生长季初期和末期较大,而白桦RL-area则随生长季进程而逐渐减小.在生长季中,RL-area与叶片总光合之比的时间动态明显.红松、樟子松RL-area与Tair关系显著,而白桦RL-area与Tair关系不显著;但3种树种基于叶质量的RL(RL-mass)与Tair均呈显著的指数函数关系.叶片特征(包括可溶性糖、淀粉、氮、比叶面积等参数)也有明显的季节变化.影响RL的叶片特征参数因树种而异,其中可溶性糖浓度对3种树种的RL均有显著影响.可见,RL的季节变化格局受树木的生长节律、温度和叶片特征的联合控制.     

A Proposed Framework for the Identification of Habitat Utilisation Patterns of Macrophytes in River Po Catchment Basin Lakes (Italy)

The main aim of this study, which is based on cover-abundance values of 20 species of floating-leaved and submersed macrophytes collected in 18 lakes located within the River Po catchment basin (Italy), is to investigate the relationships between lake-scale environmental features (including morphology, hydrology, trophic state and water quality variables) and the changes in species composition and species richness in macrophyte communities. The findings reveal that the main changes in community composition of the pondweed communities, identified by principal component analysis, could be effectively explained by a newly introduced morpho-hydrological parameter (the theoretical laminar water renewal rate – R _L) and by the trophic state of the lakes, expressed as Carlson’s trophic state index (TSI). The results of the multivariate analyses performed also show that the grouping of species is strictly related to growth-form traits. In particular, floating-leaved and submerged macrophytes appeared mutually exclusive in dominating different habitat types defined on the basis of the R _L–TSI framework. An increasing trend in species richness was also observed in relation to R _L. The possibility of using the bivariate R _L–TSI framework for the identification of habitat utilisation patterns by single species was also investigated, and cover-abundance prediction maps based on the R _L–TSI framework were produced for several macrophyte species. Observations in another system characterised by high spatial heterogeneity for hydrological and trophic conditions, support the predictions in terms of growth-form prevalence, species richness and single species cover-abundance (where available), and also suggest a more refined application of the proposed approach.     

Optimum DNA Curvature Using a Hybrid Approach Involving an Artificial Neural Network and Genetic Algorithm

Abstract

In the present paper, a hybrid technique involving artificial neural network (ANN) and genetic algorithm (GA) has been proposed for performing modeling and optimization of complex biological systems. In this approach, first an ANN approximates (models) the nonlinear relationship(s) existing between its input and output example data sets. Next, the GA, which is a stochastic optimization technique, searches the input space of the ANN with a view to optimize the ANN output. The efficacy of this formalism has been tested by conducting a case study involving optimization of DNA curvature characterized in terms of the R_L value. Using the ANN-GA methodology, a number of sequences possessing high R_L values have been obtained and analyzed to verify the existence of features known to be responsible for the occurrence of curvature. A couple of sequences have also been tested experimentally. The experimental results validate qualitatively and also near-quantitatively, the solutions obtained using the hybrid formalism. The ANN-GA technique is a useful tool to obtain, ahead of experimentation, sequences that yield high R_L values. The methodology is a general one and can be suitably employed for optimizing any other biological feature.     

Modeling combined transport of water and charged graded-size molecules across the glomerular capillary wall

Clearance studies using various probe molecules established that the passage of molecules/proteins across the glomerular capillary wall of mammalian kidneys is increasingly restricted as their size and net negative charge increase. An extended mathematical model, based on the Fiber Matrix theory, was developed to describe the dynamics of the size- and charge-selective functions of the glomerular capillary barrier using mainly its hemodynamic, morphometric, and electrostatic variables. The glomerular basement membrane was represented as a homogeneous three-dimensional network of fibers of uniform length (L_f), radius (R_f), and packing density (N_fv) and characteristic Darcy permeability. The model was appropriate for simulating fractional clearance data of neutral and charged solutes from an experimental modeling exercise. We believe that the L_f and R_f best-fit numerical values may signify new insights for the diagnosis of some human nephropathies.     

Mathematical analysis of the asymptotic behavior of the Leslie population matrix model

LetL be a Leslie population matrix. Leslie (1945) and others have shown that the matrixL has a leading positive eigenvalueλ ₀ and that in general: (1) $$\mathop {\lim }\limits_{t \to \infty } \frac{{L^t X}}{{\lambda _0^t }} = \gamma X_{\lambda _0 } $$ whereX _{λ 0} is an eigenvector corresponding toλ ₀,X is any initial population vector, and γ is a scalar quantity detormined byX. In this article we generalize (1) exhaustively by removing the mild restrictions on the fertility rates which most writers impose. The result is an oscillatory limit of a kind first noted by Bernardelli (1941) and Lewis (1942) and described by Bernardelli as “population waves”. We calculate in terms ofλ ₀ and the entries of the matrixL the values of this oscillatory limit as well as its time-independent average over one period. This calculation includes as its leading special case the result of (1), confirming incidentally that γ is nonzero. To stabilize a population, the matrixL must be adjusted so thatλ ₀=1. The limits calculated for the oscillatory and non-oscillatory cases then have maximum significance since they represent the limiting population vectors. We discuss a simple scheme for accomplishing stanbilization which yields as a byproduct an easily accessible scalar measure ofL's tendency to promote population growth. The reciprocal of this measure is the familiar net reproduction rate.     

Control of pattern duplication in the retinotectal system of Xenopus. Suppression of duplication by eye-fragment interactions.

Recombining right nasal half-eyes with left temporal half-eyes at embryonic stage 32 in Xenopus produces a double or “twinned” pattern of functional connections between retina and midbrain optic tectum. The left temporal half-eye is reprogrammed such that it projects to the tectum as a mirror-image duplicate pattern of the nasal right half-eye; both half-eyes project across the entire tectum. However, recombining a right nasal half-eye (in situ) with a right dorsal half-eye (grafted into the temporal position; N_RD_R eye) produces a single normal retinotectal projection. Where interactions between N_R and T_L involve both axial reprogramming and duplication of N_R positional values in T_L, N_RD_R interactions involve axial reprogramming in D_R without duplication of N_R values. In a second approach to interactions which suppress pattern duplication, both nasal and temporal one-third-sized eye fragments form approximate “NN” or “TT” duplicate pattern maps, respectively, when either is allowed to round up and form a whole eye. Allowing nasal and temporal thirds to permanently fuse (after removal of a one-third-sized vertical center strip of retina at stage 32) produces a normal projection; allowing the nasal and temporal thirds to interact (fuse) for 35–40 hr, followed by removal of one or the other third, suppresses pattern duplication (produces normal maps) in the remaining third in a majority of cases. Allowing the thirds to interact for 18–30 hr before removal of the temporal third produces a majority result of partial duplication in the remaining nasal third. Partial duplicates are apparent spatial intermediates with regard to interactions which suppress duplication in either fragment type.     

A test of the thermal coadaptation hypothesis with ultimate measures of fitness in flour beetles

Whole-organism performance of ectotherms depends on body temperature, which is tightly linked to environmental temperatures. Individuals attempting to optimize fitness must thus select appropriate temperatures. The thermal coadaptation hypothesis posits that T_o for traits closely linked to fitness should match temperatures selected by a species (T_set) and should coevolve with T_set. T_o may mismatch T_set if the thermal reaction norm for fitness is asymmetric. In this study, we examined six traits related to fitness in red and in confused flour beetles (Tribolium castaneum and T. confusum, respectively), including longevity, lifetime reproductive success, reproductive rate, and development time at four temperatures between 23 and 32 °C. For reproductive traits, T_o matched T_set whereas for longevity T_o was lower than T_set. Tribolium species have a strongly r-selected life history strategy, therefore reproductive traits are likely more tightly linked to fitness than longevity due to high predation rates at early life stages. We therefore provide support for the thermal coadaptation hypothesis for reproductive traits that are tightly linked to fitness. Our results highlight the importance of knowing the relationships of traits to fitness when studying thermal physiology.     

Light inhibition of leaf respiration as soil fertility declines along a post-glacial chronosequence in New Zealand: an analysis using the Kok method

Background and aims

Our study quantified variations leaf respiration in darkness (R _D) and light (R _L), and associated traits along the Franz Josef Glacier soil development chronosequence in New Zealand.

Methods

At six sites along the chronosequence (soil age: 6, 60, 150, 500, 12,000 and 120,000 years old), we measured rates of leaf R _D, R _L (using Kok method), light-saturated CO₂ assimilation rates (A), leaf mass per unit area (M _A), and concentrations of leaf nitrogen ([N]), phosphorus ([P]), soluble sugars and starch.

Results

The chronosequence was characterised by decreasing R _D, R _L and A, reduced [N] and [P] and increasing M _A as soil age increased. Light inhibition of R occurred across the chronosequence (mean inhibition = 16 %), resulting in ratios of R _L:A being lower than for R _D:A. Importantly, the degree of light inhibition differed across the chronosequence, being lowest at young sites and highest at old sites. This resulted in R _L:A ratios being relatively constant across the chronosequence, whereas R _D:A ratios increased with increasing soil age. Log-log R-A-M _A-[N] relationships remained constant along the chronosequence. By contrast, relationships linking rates of leaf R to [P] differed among leaves with low vs high [N]:[P] ratios. Slopes of log-log bivariate relationships linking R _L to A, M _A, [N] and [P] were steeper than that for R _D.

Conclusions

Our findings have important implications for predictive models that seek to account for light inhibition of R, and for our understanding of how environmental gradients impact on leaf trait relationships     

Swainsonine,a novel fungal metabolite: optimization of fermentative production and bioreactor operations using evolutionary programming

The optimization of bioreactor operations towards swainsonine production was performed using an artificial neural network coupled evolutionary program (EP)-based optimization algorithm fitted with experimental one-factor-at-a-time (OFAT) results. The effects of varying agitation (300–500 rpm) and aeration (0.5–2.0 vvm) rates for different incubation hours (72–108 h) were evaluated in bench top bioreactor. Prominent scale-up parameters, gassed power per unit volume (P _g/V _L, W/m³) and volumetric oxygen mass transfer coefficient (K _L a, s^?1) were correlated with optimized conditions. A maximum of 6.59 ± 0.10 μg/mL of swainsonine production was observed at 400 rpm-1.5 vvm at 84 h in OFAT experiments with corresponding P _g/V_L and K _L a values of 91.66 W/m³ and 341.48 × 10^?4 s^?1, respectively. The EP optimization algorithm predicted a maximum of 10.08 μg/mL of swainsonine at 325.47 rpm, 1.99 vvm and 80.75 h against the experimental production of 7.93 ± 0.52 μg/mL at constant K _L a (349.25 × 10^?4 s^?1) and significantly reduced P _g/V _L (33.33 W/m³) drawn by the impellers.     

Chromosomal polymorphism in the populations of malaria vector mosquito Anopheles messeae at the south of Russian Plain

Zonal features of the geographic distribution of chromosomal inversions in the populations of An. messae at the south of the Russian Plain were examined. The An. messae was identified based on morphological characters and cytogenetically. The chromosomal inversions identified in populations of An. messae comprised XL₁, XL₄, 2R₁, 3R₁, and 3L₁. Inversion XL₄ was endemic and found with low frequency (2%) in Penza oblast and Moscow oblast. Based on the population karyotype structure similarity, on the territory of Russian Plain, three zones (southwestern, southeastern, and central) were identified. Central zone was characterized by higher levels of inversion polymorphism in all chromosomes, except arm 3L. In the two southern zones, high frequency of the XL₀ inversion, along with complete absence of homo- and heterozygotes for the 2R₁ inversion, and high proportion of the individuals with inversion 3L₁ was observed. Specific feature of southeastern zone was the increased frequency of hetero- and homozygotes for the 3R₁ and 3L₁ inversions. Zonal differences reflected adaptive character of chromosomal polymorphism and pointed to hierarchic organization of the species population structure.     

Multistage affinity cross-flow filtration: process optimization

The recovery yield (REC) and productivity (PRD) are used as objective functions to optimize the multistage affinity cross-flow filtration (mACFF) process. The effects of the operating conditions such as feed loading volume (Q _L ⁺), total protein concentration and target protein purity in the feeding broth are analyzed. For higher affinity system or by a mACFF process with larger number of stages as well as more macroligand loading, there is a critical value of Q _L ⁺ below which the REC keeps constant and maximal. This maximal value of REC is affected by the stage number as well as macroligand loading of the mACFF process and the affinity system (i.e., the binding constant of the target protein to its macroligand), but independent of the feeding broth properties (i.e., total protein concentration and target protein purity) and membrane permeability. An optimum of Q _L ⁺ exists to give a maximum of PRD. The optimal Q _L ⁺ is somewhat larger than the critical Q _L ⁺ value below which REC keeps constant. The maximum of PRD is raised by increasing the stage number and macroligand loading of the mACFF process, affinity binding constant, and total protein concentration as well as target protein purity in the feeding broth, but reduced by increasing the membrane rejection coefficient (R). However, it is encouraging that the decrease of the maximal PRD is less significant when R is less than 0.5. Therefore, if it is not possible to find a membrane that is completely permeable to proteins and at the same time completely impermeable to the macroligand, a membrane with R less than 0.5 may be selected to obtain a larger PRD. The results obtained in this work give further predictive understanding of the mACFF technique, and will be useful to the process design.     

Additive main effects and multiplicative interaction (AMMI) analysis of genotype-location interaction in variety trials repeated over years

Genotype-location (GL) interaction effects are of special interest for breeding programmes to identify adaptation targets, adaptive traits and test sites. These effects, generally having relatively low repeatability between years, should be studied on a multiyear basis in annual crops. Their assessment by additive main effects and multiplicative interaction (AMMI) analysis is currently defined for this situation. Two procedures based on cross validations are proposed for testing the GL-interaction principal component axes, exploiting the utilities of the computer programme MATMODEL. The use of Gollob’s F test, F _GH2 test, F _R test and the heuristic criterion based on the signal-to-noise ratio is also envisaged. The consistency of results provided by the testing procedures was verified on four data sets of different cereal crops. Gollob’s test tended to be the most liberal, while the F _GH2 test appeared somewhat more liberal than the F _R test. The signal-to-noise ratio gave results consistent with the F _R test considered at a P?0.01 level of significance. These criteria disagreed in two data sets with the conclusions provided by the two cross-validation procedures which, in turn, also disagreed in one data set. Preference could be given to different testing procedures depending on the number of test years, locations and genotypes.     

Characterizing complex dynamics in the transactivation response element apical loop and motional correlations with the bulge by NMR, molecular dynamics, and mutagenesis

The HIV-1 transactivation response element (TAR) RNA binds a variety of proteins and is a target for developing anti-HIV therapies. TAR has two primary binding sites: a UCU bulge and a CUGGGA apical loop. We used NMR residual dipolar couplings, carbon spin relaxation (R₁ and R₂), and relaxation dispersion (R_1ρ) in conjunction with molecular dynamics and mutagenesis to characterize the dynamics of the TAR apical loop and investigate previously proposed long-range interactions with the distant bulge. Replacement of the wild-type apical loop with a UUCG loop did not significantly affect the structural dynamics at the bulge, indicating that the apical loop and the bulge act largely as independent dynamical recognition centers. The apical loop undergoes complex dynamics at multiple timescales that are likely important for adaptive recognition: U31 and G33 undergo limited motions, G32 is highly flexible at picosecond-nanosecond timescales, and G34 and C30 form a dynamic Watson-Crick basepair in which G34 and A35 undergo a slow (∼30 μs) likely concerted looping in and out motion, with A35 also undergoing large amplitude motions at picosecond-nanosecond timescales. Our study highlights the power of combining NMR, molecular dynamics, and mutagenesis in characterizing RNA dynamics.     

Quantitative genetic study of the adaptive process

The additive genetic variance with respect to absolute fitness, V_A(W), divided by mean absolute fitness, , sets the rate of ongoing adaptation. Fisher''s key insight yielding this quantitative prediction of adaptive evolution, known as the Fundamental Theorem of Natural Selection, is well appreciated by evolutionists. Nevertheless, extremely scant information about V_A(W) is available for natural populations. Consequently, the capacity for fitness increase via natural selection is unknown. Particularly in the current context of rapid environmental change, which is likely to reduce fitness directly and, consequently, the size and persistence of populations, the urgency of advancing understanding of immediate adaptive capacity is extreme. We here explore reasons for the dearth of empirical information about V_A(W), despite its theoretical renown and critical evolutionary role. Of these reasons, we suggest that expectations that V_A(W) is negligible, in general, together with severe statistical challenges of estimating it, may largely account for the limited empirical emphasis on it. To develop insight into the dynamics of V_A(W) in a changing environment, we have conducted individual-based genetically explicit simulations. We show that, as optimizing selection on a trait changes steadily over generations, V_A(W) can grow considerably, supporting more rapid adaptation than would the V_A(W) of the base population. We call for direct evaluation of V_A(W) and in support of prediction of rates adaptive evolution, and we advocate for the use of aster modeling as a rigorous basis for achieving this goal.     

Statistical optimization of critical medium components for lipase production from Yarrowia lipolytica (MTCC 35)

Statistical optimization is an effective technique for the investigation of complex processes with minimal number of experimental runs. In this study, statistical approach was used to study the optimization of media components for lipase production from Yarrowia lipolytica MTCC 35. Mahua cake, glucose, MnCl₂ and KH₂PO₄ were screened to be the most significant variables among the nine medium variables that were tested to determine influence on lipase production by Plackett–Burman design. Central Composite Design was used for further optimization of these screened variables for enhanced lipase production. The determination coefficient (R²) value of 0.922 showed that the regression models adequately explain the data variation and represent the actual relationships between the variables and response. The optimum values of investigated variables for the maximum lipase production were 6.0% Mahua cake, 2.0% glucose, 0.2% MnCl₂ and 0.2% KH₂PO₄. The maximum lipase production (9.40 U mL^?1) was obtained under optimal condition.     

An Adaptive Ridge Procedure for L0 Regularization

Penalized selection criteria like AIC or BIC are among the most popular methods for variable selection. Their theoretical properties have been studied intensively and are well understood, but making use of them in case of high-dimensional data is difficult due to the non-convex optimization problem induced by L₀ penalties. In this paper we introduce an adaptive ridge procedure (AR), where iteratively weighted ridge problems are solved whose weights are updated in such a way that the procedure converges towards selection with L₀ penalties. After introducing AR its specific shrinkage properties are studied in the particular case of orthogonal linear regression. Based on extensive simulations for the non-orthogonal case as well as for Poisson regression the performance of AR is studied and compared with SCAD and adaptive LASSO. Furthermore an efficient implementation of AR in the context of least-squares segmentation is presented. The paper ends with an illustrative example of applying AR to analyze GWAS data.     

Parasite‐mediated selection in a natural metapopulation of Daphnia magna

Parasite‐mediated selection varying across time and space in metapopulations is expected to result in host local adaptation and the maintenance of genetic diversity in disease‐related traits. However, nonadaptive processes like migration and extinction‐(re)colonization dynamics might interfere with adaptive evolution. Understanding how adaptive and nonadaptive processes interact to shape genetic variability in life‐history and disease‐related traits can provide important insights into their evolution in subdivided populations. Here we investigate signatures of spatially fluctuating, parasite‐mediated selection in a natural metapopulation of Daphnia magna. Host genotypes from infected and uninfected populations were genotyped at microsatellite markers, and phenotyped for life‐history and disease traits in common garden experiments. Combining phenotypic and genotypic data a Q_ST–F_ST‐like analysis was conducted to test for signatures of parasite mediated selection. We observed high variation within and among populations for phenotypic traits, but neither an indication of host local adaptation nor a cost of resistance. Infected populations have a higher gene diversity (Hs) than uninfected populations and Hs is strongly positively correlated with fitness. These results suggest a strong parasite effect on reducing population level inbreeding. We discuss how stochastic processes related to frequent extinction‐(re)colonization dynamics as well as host and parasite migration impede the evolution of resistance in the infected populations. We suggest that the genetic and phenotypic patterns of variation are a product of dynamic changes in the host gene pool caused by the interaction of colonization bottlenecks, inbreeding, immigration, hybrid vigor, rare host genotype advantage and parasitism. Our study highlights the effect of the parasite in ameliorating the negative fitness consequences caused by the high drift load in this metapopulation.     

On the application of statistical physics to evolutionary biology

There is a close analogy between statistical thermodynamics and the evolution of allele frequencies under mutation, selection and random drift. Wright's formula for the stationary distribution of allele frequencies is analogous to the Boltzmann distribution in statistical physics. Population size, 2N, plays the role of the inverse temperature, 1/kT, and determines the magnitude of random fluctuations. Log mean fitness, , tends to increase under selection, and is analogous to a (negative) energy; a potential function, U, increases under mutation in a similar way. An entropy, S_H, can be defined which measures the deviation from the distribution of allele frequencies expected under random drift alone; the sum gives a free fitness that increases as the population evolves towards its stationary distribution. Usually, we observe the distribution of a few quantitative traits that depend on the frequencies of very many alleles. The mean and variance of such traits are analogous to observable quantities in statistical thermodynamics. Thus, we can define an entropy, S_Ω, which measures the volume of allele frequency space that is consistent with the observed trait distribution. The stationary distribution of the traits is ; this applies with arbitrary epistasis and dominance. The entropies S_Ω, S_H are distinct, but converge when there are so many alleles that traits fluctuate close to their expectations. Populations tend to evolve towards states that can be realised in many ways (i.e., large S_Ω), which may lead to a substantial drop below the adaptive peak; we illustrate this point with a simple model of genetic redundancy. This analogy with statistical thermodynamics brings together previous ideas in a general framework, and justifies a maximum entropy approximation to the dynamics of quantitative traits.