Survival of nonspecific porin-deficient mutants of Escherichia coli in black sea water

AIMS: The aim of this study was to investigate the links between survival of Escherichia coli in sea water microcosms in the laboratory and the presence of porins in the outer membrane. The E. coli strains studied were a wild-type strain and a series of outer membrane protein (omp) mutants. METHODS AND RESULTS: Bacteria were suspended in natural or filtered-autoclaved sea water microcosms and numbers determined over an incubation period by plate count and by count of cells capable of respiration. CONCLUSIONS: The type of omp mutation has a significant impact in bacterial survival. The double OmpC-OmpF mutant and the OmpR mutant (which was incapable of synthesizing OmpC and OmpF) survived poorly compared with single omp mutants and the wild-type strain. This suggests that these proteins are important in determining the entry of E. coli into the survival mode. The EnvZ mutant, which lacks the protein by which the cell senses some changes in the environment, survived as well as the wild-type strain when compared by plate counts and by respiring cell count. The loss of the EnvZ protein has no effect on survival but it could prevent the organism sensing the changes in the environment through which entry into the survival state is triggered. SIGNIFICANCE AND IMPACT OF THE STUDY: This work is another piece in the puzzle as to how bacteria survive stress conditions.     

Resolution of (±)‐β‐methylphenylethylamine by a novel chiral stationary phase for Pirkle‐type column chromatography

In this study, a new Pirkle‐type chiral column stationary phase for resolution of β‐methylphenylethyl amine was described by using activated Sepharose 4B as a matrix, L ‐tyrosine as a spacer arm, and an aromatic amine derivative of L ‐glutamic acid as a ligand. The binding capacities of the stationary phase were determined at different pH values (pH = 6, 7, and 8) using buffer solutions as mobile phase, and enantiomeric excess (ee) was determined by HPLC equipped with chiral column. The ee was found to be 47%. Chirality, 2010. © 2009 Wiley‐Liss, Inc.     

Sustainability indices for exploited populations

Evaluating the sustainability of hunting is key to the conservation of species exploited for bushmeat. Researchers are often hampered by a lack of basic biological data, the usual response to which is to develop sustainability indices based on highly simplified population models. However, the standard indices in the bushmeat literature do not perform well under realistic conditions of uncertainty, bias in parameter estimation, and habitat loss. Another possible approach to estimating the sustainability of hunting under uncertainty is to use Bayesian statistics, but this is mathematically demanding. Red listing of threatened species has to be carried out in extremely data-poor situations: uncertainty has been incorporated into this process in a relatively simple and intuitive way using fuzzy numbers. The current methods for estimating sustainability of bushmeat hunting also do not incorporate spatial heterogeneity. No-take areas are one management tool that can address uncertainty in a spatially explicit way.     

Improvements to the Red List Index

The Red List Index uses information from the IUCN Red List to track trends in the projected overall extinction risk of sets of species. It has been widely recognised as an important component of the suite of indicators needed to measure progress towards the international target of significantly reducing the rate of biodiversity loss by 2010. However, further application of the RLI (to non-avian taxa in particular) has revealed some shortcomings in the original formula and approach: It performs inappropriately when a value of zero is reached; RLI values are affected by the frequency of assessments; and newly evaluated species may introduce bias. Here we propose a revision to the formula, and recommend how it should be applied in order to overcome these shortcomings. Two additional advantages of the revisions are that assessment errors are not propagated through time, and the overall level extinction risk can be determined as well as trends in this over time.     

The effect of starvation stress on the porin protein expression of Escherichia coli in lake water

AIMS: The aim was to identify changes in outer membrane proteins (omps), OmpA, OmpC and OmpF, in Escherichia coli under starvation conditions in lake water microcosms studied at different temperatures. METHODS AND RESULTS: Escherichia coli was incubated in lake water microcosms at a variety of temperatures and the omps studied using quantitative densitometric analysis of protein bands of sodium dodecyl sulphate gels of omp preparations. The amount of OmpF increased over the incubation period relative to that of OmpC whereas the relative abundance of OmpA declined, most notably at 25 and 37 degrees C. This change was linked to changes in peak cell volume as determined by cell measurements. CONCLUSIONS: Major changes to the omps of E. coli accompany the adaptation of the organism to starvation conditions in lake water microcosms. SIGNIFICANCE AND IMPACT OF THE STUDY: Prolonged starvation affects the relative amounts of outer membrane porins. This study furthers the understanding of the role played by changes in the omp composition in E. coli during survival in lake water environments.     

The impact of sea‐level rise on Snowy Plovers in Florida: integrating geomorphological,habitat, and metapopulation models

Sea‐level rise (SLR) is a projected consequence of global climate change that will result in complex changes in coastal ecosystems. These changes will cause transitions among coastal habitat types, which will be compounded by human‐made barriers to the gradual inland migration of these habitat types. The effect of these changes on the future viability of coastal species will depend on the habitat requirements and population dynamics of these species. Thus, realistic assessments of the impact of SLR require linking geomorphological models with habitat and population models. In this study, we implemented a framework that allows this linkage, and demonstrated its feasibility to assess the effect of SLR on the viability of the Snowy Plover population in Florida. The results indicate that SLR will cause a decline in suitable habitat and carrying capacity for this species, and an increase in the risk of its extinction and decline. The model projected that the population size will decline faster than the area of habitat or carrying capacity, demonstrating the necessity of incorporating population dynamics in assessing the impacts of SLR on coastal species. The results were most sensitive to uncertainties in survival rate and fecundity, and suggested that future studies on this species should focus on the average and variability of these demographic rates and their dependence on population density. The effect of SLR on this species’ viability was qualitatively similar with most alternative models that used the extreme values of each uncertain parameter, indicating that the results are robust to uncertainties in the model.     

Plant extinction risk under climate change: are forecast range shifts alone a good indicator of species vulnerability to global warming?

Models that couple habitat suitability with demographic processes offer a potentially improved approach for estimating spatial distributional shifts and extinction risk under climate change. Applying such an approach to five species of Australian plants with contrasting demographic traits, we show that: (i) predicted climate‐driven changes in range area are sensitive to the underlying habitat model, regardless of whether demographic traits and their interaction with habitat patch configuration are modeled explicitly; and (ii) caution should be exercised when using predicted changes in total habitat suitability or geographic extent to infer extinction risk, because the relationship between these metrics is often weak. Measures of extinction risk, which quantify threats to population persistence, are particularly sensitive to life‐history traits, such as recruitment response to fire, which explained approximately 60% of the deviance in expected minimum abundance. Dispersal dynamics and habitat patch structure have the strongest influence on the amount of movement of the trailing and leading edge of the range margin, explaining roughly 40% of modeled structural deviance. These results underscore the need to consider direct measures of extinction risk (population declines and other measures of stochastic viability), as well as measures of change in habitat area, when assessing climate change impacts on biodiversity. Furthermore, direct estimation of extinction risk incorporates important demographic and ecosystem processes, which potentially influence species’ vulnerability to extinction due to climate change.     

Predicting extinction risks under climate change: coupling stochastic population models with dynamic bioclimatic habitat models

Species responses to climate change may be influenced by changes in available habitat, as well as population processes, species interactions and interactions between demographic and landscape dynamics. Current methods for assessing these responses fail to provide an integrated view of these influences because they deal with habitat change or population dynamics, but rarely both. In this study, we linked a time series of habitat suitability models with spatially explicit stochastic population models to explore factors that influence the viability of plant species populations under stable and changing climate scenarios in South African fynbos, a global biodiversity hot spot. Results indicate that complex interactions between life history, disturbance regime and distribution pattern mediate species extinction risks under climate change. Our novel mechanistic approach allows more complete and direct appraisal of future biotic responses than do static bioclimatic habitat modelling approaches, and will ultimately support development of more effective conservation strategies to mitigate biodiversity losses due to climate change.