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.     

Assessing population size and structure for Andean Condor Vultur gryphus in Bolivia using a photographic ‘capture‐recapture’ method

The Andean Condor Vultur gryphus is a globally threatened and declining species. Problems of surveying Andean Condor populations using traditional survey methods are particularly acute in Bolivia, largely because only few roosts are known there. However, similar to other vulture species, Andean Condors aggregate at animal carcasses, and are individually recognizable due to unique morphological characteristics (size and shape of male crests and pattern of wing coloration). This provided us with an opportunity to use a capture‐recapture (‘sighting‐resighting’) modelling framework to estimate the size and structure of an Andean Condor population in Bolivia using photographs of individuals taken at observer‐established feeding stations. Between July and December 2014, 28 feeding stations were established in five different zones throughout the eastern Andean region of Bolivia, where perched and flying Andean Condors were photographed. Between one and 57 (mean = 20.2 ± 14.6 sd) Andean Condors were recorded visiting each feeding station and we were able to identify 456 different individuals, comprising 134 adult males, 40 sub‐adult males, 79 juvenile males, 80 adult females, 30 sub‐adult females and 93 juvenile females. Open population capture‐recapture models produced population estimates ranging from 52 ± 14 (se) individuals to 678 ± 269 individuals across the five zones, giving a total of 1388 ± 413 sd individuals, which is roughly 20% of the estimated Andean Condor global population. Future trials of this method need to consider explicitly knowledge of Andean Condor movements and home‐ranges, habitat preferences when selecting suitable sites as feeding stations, juvenile movements and other behaviours. Sighting‐resighting methods have considerable potential to increase the accuracy of surveys of Andean Condors and other bird species with unique individual morphological characteristics.     

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.     

Prevalence, whole genome characterization and phylogenetic analysis of hepatitis B virus in captive orangutan and gibbon

Background Hepatitis B virus (HBV) is a public health problem worldwide and apart from infecting humans, HBV has been found in non‐human primates. Methods We subjected 93 non‐human primates comprising 12 species to ELISA screening for the serological markers HBsAg, antiHBs and antiHBc. Subsequently, we detected HBV DNA, sequenced the whole HBV genome and performed phylogenetic analysis. Results HBV infection was detected in gibbon (4/15) and orangutan (7/53). HBV DNA isolates from two gibbons and seven orangutans were chosen for complete genome amplification. We aligned the Pre‐S/S, Pre‐C/C and entire genomes with HBV sequences and performed phylogenetic analysis. The gibbon and orangutan viruses clustered within their respective groups. Conclusions Both geographic location and host species influence which HBV variants are found in gibbons and orangutans. Hence, HBV transmission between humans and non‐human primates might be a distinct possibility and additional studies will be required to further investigate this potential risk.     

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.