Population dynamics of three songbird species in a nestbox population in Central Europe show effects of density,climate and competitive interactions

Unravelling the contributions of density‐dependent and density‐independent factors in determining species population dynamics is a challenge, especially if the two factors interact. One approach is to apply stochastic population models to long‐term data, yet few studies have included interactions between density‐dependent and density‐independent factors, or explored more than one type of stochastic population model. However, both are important because model choice critically affects inference on population dynamics and stability. Here, we used a multiple models approach and applied log‐linear and non‐linear stochastic population models to time series (spanning 29 years) on the population growth rates of Blue Tits Cyanistes caeruleus, Great Tits Parus major and Pied Flycatchers Ficedula hypoleuca breeding in two nestbox populations in southern Germany. We focused on the roles of climate conditions and intra‐ and interspecific competition in determining population growth rates. Density dependence was evident in all populations. For Blue Tits in one population and for Great Tits in both populations, addition of a density‐independent factor improved model fit. At one location, Blue Tit population growth rate increased following warmer winters, whereas Great Tit population growth rates decreased following warmer springs. Importantly, Great Tit population growth rate also decreased following years of high Blue Tit abundance, but not vice versa. This finding is consistent with asymmetric interspecific competition and implies that competition could carry over to influence population dynamics. At the other location, Great Tit population growth rate decreased following years of high Pied Flycatcher abundance but only when Great Tit population numbers were low, illustrating that the roles of density‐dependent and density‐independent factors are not necessarily mutually exclusive. The dynamics of this Great Tit population, in contrast to the other populations, were unstable and chaotic, raising the question of whether interactions between density‐dependent and density‐independent factors play a role in determining the (in) stability of the dynamics of species populations.     

Bcl-2 prevents hypoxia/reoxygenation-induced cell death through suppressed generation of reactive oxygen species and upregulation of Bcl-2 proteins

The function of bcl-2 in preventing cell death is well known, but the mechanisms whereby bcl-2 functions are not well characterized. One mechanism whereby bcl-2 is thought to function is by alleviating the effects of oxidative stress upon the cell. To examine whether Bcl-2 can protect cells against oxidative injury resulting from post-hypoxic reoxygenation (H/R), we subjected rat fibroblasts Rat-1 and their bcl-2 transfectants b5 to hypoxia (5% CO2, 95% N2) followed by reoxygenation (5% CO2, 95% air). The bcl-2 transfectants exhibited the cell viability superior to that of their parent non-transfectants upon treatment with reoxygenation after 24-, 48-, or 72-h hypoxia, but not upon normoxic serum-deprivation or upon serum-supplied hypoxic treatment alone. Thus bcl-2 transfection can prevent cell death of some types, which occurred during H/R but yet not appreciably until termination of hypoxia. The time-sequential events of H/R-induced cell death were shown to be executed via (1) reactive oxygen species (ROS) production at 1-12 h after H/R, (2) activation of caspases-1 and -3, at 1-3 h and 3-6 h after H/R, respectively, and (3) loss of mitochondrial membrane potential (DeltaPsi) at 3-12 h after H/R. These cell death-associated events were prevented entirely except caspase-1 activation by bcl-2 transfection, and were preceded by Bcl-2 upregulation which was executed as early as at 0-1 h after H/R for the bcl-2 transfectants but not their non-transfected counterpart cells. Thus upregulation of Bcl-2 proteins may play a role in prevention of H/R-induced diminishment of cell viability, but may be executed not yet during hypoxia itself and be actually operated as promptly as ready to go immediately after beginning of H/R, resulting in cytoprotection through blockage of either ROS generation, caspase-3 activation, or DeltaPsi decline.     

Determinants of sperm transfer in the scorpionfly Panorpa cognate: male variation, female condition and copulation duration

Recent studies suggest that sperm production and transfer may have significant costs to males. Male sperm investment into a current copulation may therefore influence resources available for future matings, which selects for male strategic mating investment. In addition, females may also benefit from actively or passively altering the number of sperm transferred by males. In the scorpionfly Panorpa cognata, the number of sperm transferred during copulation depended on copulation duration and males in good condition (residual weight) copulated longer and also transferred more sperm. Moreover, sperm transferred and stored per unit time was higher in copulations with females in good condition than in copulations with females in poor condition. Males varied greatly and consistently in their sperm transfer rate, indicative of costs associated with this trait. The duration of the pairing prelude also varied between males and correlated negatively with the male's sperm transfer rate, but no other male character correlated significantly with male sperm transfer rate. The results are consistent with strategic mating effort but sperm transfer could also be facilitated by the physical size of females and/or females in good condition may be more cooperative during sperm transfer.     

Stochastic exits from dormancy give rise to heavy‐tailed distributions of descendants in bacterial populations

Variance in reproductive success is a major determinant of the degree of genetic drift in a population. While many plants and animals exhibit high variance in their number of progeny, far less is known about these distributions for microorganisms. Here, we used a strain barcoding approach to quantify variability in offspring number among replicate bacterial populations and developed a Bayesian method to infer the distribution of descendants from this variability. We applied our approach to measure the offspring distributions for five strains of bacteria from the genus Streptomyces after germination and growth in a homogenous laboratory environment. The distributions of descendants were heavy‐tailed, with a few cells effectively ‘winning the jackpot’ to become a disproportionately large fraction of the population. This extreme variability in reproductive success largely traced back to initial populations of spores stochastically exiting dormancy, which provided early‐germinating spores with an exponential advantage. In simulations with multiple dormancy cycles, heavy‐tailed distributions of descendants decreased the effective population size by many orders of magnitude and led to allele dynamics differing substantially from classical population genetics models with matching effective population size. Collectively, these results demonstrate that extreme variability in reproductive success can occur even in growth conditions that are far more homogeneous than the natural environment. Thus, extreme variability in reproductive success might be an important factor shaping microbial population dynamics with implications for predicting the fate of beneficial mutations, interpreting sequence variability within populations and explaining variability in infection outcomes across patients.     

The formation of titan cells in Cryptococcus neoformans depends on the mouse strain and correlates with induction of Th2‐type responses

Cryptococcus neoformans is a pathogenic yeast that can form titan cells in the lungs, which are fungal cells of abnormal enlarged size. Little is known about the factors that trigger titan cells. In particular, it is not known how the host environment influences this transition. In this work, we describe the formation of titan cells in two mouse strains, CD1 and C57BL/6J. We found that the proportion of C. neoformans titan cells was significantly higher in C57BL/6J mice than in CD1. This higher proportion of titan cells was associated with a higher dissemination of the yeasts to the brain. Histology sections demonstrated eosinophilia in infected animals, although it was significantly lower in the CD1 mice which presented infiltration of lymphocytes. Both mouse strains presented infiltration of granulocytes, but the amount of eosinophils was higher in C57BL/6J. CD1 mice showed a significant accumulation of IFN‐γ, TNF‐α and IL17, while C57BL/BL mice had an increase in the anti‐inflammatory cytokine IL‐4. IgM antibodies to the polysaccharide capsule and total IgE were more abundant in the sera from C57BL/6J, confirming that these animals present a Th2‐type response. We conclude that titan cell formation in C. neoformans depends, not only on microbe factors, but also on the host environment.     

Validating and improving elastic network models with molecular dynamics simulations

Elastic network models (ENMs) are a class of simple models intended to represent the collective motions of proteins. In contrast to all‐atom molecular dynamics simulations, the low computational investment required to use an ENM makes them ideal for speculative hypothesis‐testing situations. Historically, ENMs have been validated via comparison to crystallographic B‐factors, but this comparison is relatively low‐resolution and only tests the predictions of relative flexibility. In this work, we systematically validate and optimize a number of ENM‐type models by quantitatively comparing their predictions to microsecond‐scale all‐atom simulations of three different G protein coupled receptors. We show that, despite their apparent simplicity, well‐optimized ENMs perform remarkably well, reproducing the protein fluctuations with an accuracy comparable to what one would expect from all‐atom simulations run for several hundred nanoseconds. Proteins 2010. © 2010 Wiley‐Liss, Inc.     

Prenatal data impacts common bottlenose dolphin (Tursiops truncatus) growth parameters estimated by length‐at‐age curves

Compilation of marine mammal demographic data is central to management efforts. However, marine mammal length‐at‐age growth curves demonstrate limitations. Physiological growth parameters of terrestrial mammals are typically estimated using curvilinear models fit to size‐at‐age data along a time series from conception to senescence. The difficulty of collecting and aging prenatal cetaceans is addressed here, and growth parameters of common bottlenose dolphins (Tursiops truncatus) along coastal Texas were estimated using length‐at‐age information from a broader scope of age classes, including late‐term fetuses. A Gompertz growth curve fit to pre‐ and postnatal data underestimated size parameters, but demonstrated similar growth rate constants (k) to an exclusively postnatal model. However, when growth parameters were broken out, the absolute growth rate (G) and rate of growth decay (g) decreased (0.44 from 0.27 and 0.55 from 0.39, respectively), which underscores the importance of reporting k in its expanded form (G/g). Although the Gompertz fits most age classes well, it cannot explain growth in all age classes. We argue that a novel sigmoidal model would be more useful for inference.