Designing connected marine reserves in the face of global warming

Marine reserves are widely used to protect species important for conservation and fisheries and to help maintain ecological processes that sustain their populations, including recruitment and dispersal. Achieving these goals requires well‐connected networks of marine reserves that maximize larval connectivity, thus allowing exchanges between populations and recolonization after local disturbances. However, global warming can disrupt connectivity by shortening potential dispersal pathways through changes in larval physiology. These changes can compromise the performance of marine reserve networks, thus requiring adjusting their design to account for ocean warming. To date, empirical approaches to marine prioritization have not considered larval connectivity as affected by global warming. Here, we develop a framework for designing marine reserve networks that integrates graph theory and changes in larval connectivity due to potential reductions in planktonic larval duration (PLD) associated with ocean warming, given current socioeconomic constraints. Using the Gulf of California as case study, we assess the benefits and costs of adjusting networks to account for connectivity, with and without ocean warming. We compare reserve networks designed to achieve representation of species and ecosystems with networks designed to also maximize connectivity under current and future ocean‐warming scenarios. Our results indicate that current larval connectivity could be reduced significantly under ocean warming because of shortened PLDs. Given the potential changes in connectivity, we show that our graph‐theoretical approach based on centrality (eigenvector and distance‐weighted fragmentation) of habitat patches can help design better‐connected marine reserve networks for the future with equivalent costs. We found that maintaining dispersal connectivity incidentally through representation‐only reserve design is unlikely, particularly in regions with strong asymmetric patterns of dispersal connectivity. Our results support previous studies suggesting that, given potential reductions in PLD due to ocean warming, future marine reserve networks would require more and/or larger reserves in closer proximity to maintain larval connectivity.     

Proteomic analysis of acidic chaperones, and stress proteins in extreme halophile Halobacterium NRC-1: a comparative proteomic approach to study heat shock response

Background  

Halobacterium sp. NRC-1 is an extremely halophilic archaeon and has adapted to optimal growth under conditions of extremely high salinity. Its proteome is highly acidic with a median pI of 4.9, a unique characteristic which helps the organism to adapt high saline environment. In the natural growth environment, Halobacterium NRC-1 encounters a number of stressful conditions including high temperature and intense solar radiation, oxidative and cold stress. Heat shock proteins and chaperones play indispensable roles in an organism's survival under many stress conditions. The aim of this study was to develop an improved method of 2-D gel electrophoresis with enhanced resolution of the acidic proteome, and to identify proteins with diverse cellular functions using in-gel digestion and LC-MS/MS and MALDI-TOF approach.     

Infrared spectroscopic analysis of tumor pathology

Infrared spectra of normal and malignant breast tissues were measured in the 600 cm(-1) to 4000 cm(-1) region. The measured spectroscopic features which are the spectroscopic fingerprints of the tissues contain the vital information about the malignant and normal tissues. Fourier Transform Infrared (FTIR) data on 25 cases of infiterating ductal carcinoma of breast with different grades of malignancy from patients of different age groups were analyzed. The samples were taken from the tumor sections of the tissue removed during surgery. Infrared spectra demonstrate significant spectral differences between the normal and the cancerous breast tissues. In particular changes in frequency and intensity in the spectra of protein, nucleic acid and glycogen vibrational modes as well as the band intensity ratios for lipid/proteins, protein/nucleic acids, protein/glycogen were observed. This allows to make a qualitative and semi quantitative evaluation of the changes in proliferation activity from normal to diseased tissue. It was evident that the sample to sample or patient to patient variations were small and the spectral differences between normal and diseased tissues were reproducible. The findings establish a framework for additional studies, which may enable us to establish a relation of the diseased state with its infrared spectra.     

Robust modeling of additive and nonadditive variation with intuitive inclusion of expert knowledge

We propose a novel Bayesian approach that robustifies genomic modeling by leveraging expert knowledge (EK) through prior distributions. The central component is the hierarchical decomposition of phenotypic variation into additive and nonadditive genetic variation, which leads to an intuitive model parameterization that can be visualized as a tree. The edges of the tree represent ratios of variances, for example broad-sense heritability, which are quantities for which EK is natural to exist. Penalized complexity priors are defined for all edges of the tree in a bottom-up procedure that respects the model structure and incorporates EK through all levels. We investigate models with different sources of variation and compare the performance of different priors implementing varying amounts of EK in the context of plant breeding. A simulation study shows that the proposed priors implementing EK improve the robustness of genomic modeling and the selection of the genetically best individuals in a breeding program. We observe this improvement in both variety selection on genetic values and parent selection on additive values; the variety selection benefited the most. In a real case study, EK increases phenotype prediction accuracy for cases in which the standard maximum likelihood approach did not find optimal estimates for the variance components. Finally, we discuss the importance of EK priors for genomic modeling and breeding, and point to future research areas of easy-to-use and parsimonious priors in genomic modeling.     

Conservation strategies for coastal wetlands in the Gulf of California,Mexico

Wetlands worldwide, the fisheries they support, and the communities that depend on them are threatened by habitat modification. We describe strategies being used for wetland conservation in the Gulf of California, Mexico, their effectiveness, and challenges for implementation. We base our analysis on the authors’ experience working for local environmental non-governmental organizations and available literature. The strategies discussed include public and private policy instruments such as Environmental Impact Evaluations, environmental land easements, concessions and transfer agreements, Natural Protected Areas, and international agreements such as the Ramsar convention and the North American Wetlands Conservation Act. We present examples from the Gulf of California that highlight some of the challenges to wetland conservation. These challenges range from governmental failure to enforce existing environmental legislation, lack of verification of requirements for development projects, to low economic penalties for wetland modification or destruction. We found that in the Gulf of California successful conservation of coastal wetlands required a combination of policy instruments and relied on integrating science, management, and public participation through partnerships between non-governmental institutions, academic institutions, community stakeholders, and government agencies.     

Characterization of the Rust Fungus, Puccinia emaculata, and Evaluation of Genetic Variability for Rust Resistance in Switchgrass Populations

Several fungal pathogens have been identified on ornamental and native stands of switchgrass (Panicum virgatum L.). Diseases of switchgrass, particularly rust, have been largely neglected and are likely to become the major limiting factor to biomass yield and quality, especially when monocultured over a large acreage. Based on teliospore morphology and internal transcribed spacer-based diagnostic primers, the rust pathogen collected from switchgrass research fields in Oklahoma was identified as Puccinia emaculata. Furthermore, to identify genetically diverse source(s) of rust resistance, several switchgrass genotypes from both upland (cv. ‘Summer’ and ‘Cave-in-Rock’) and lowland (cv. ‘Alamo’ and ‘Kanlow’) ecotypes were evaluated in Ardmore, Oklahoma during 2008 and 2009 and in growth chamber assays. Field and growth chamber evaluations revealed a high degree of genetic variation within and among switchgrass cultivars. In general, Alamo and Kanlow showed moderate resistance to P. emaculata, while Summer was highly susceptible. Distinct ecotypic variations for reactions to rust were also prevalent with the lowlands maintaining a high level of resistance. These results suggest the potential for improvement of rust resistance via the selection of resistant individuals from currently available cultivars. Further, the selection pressure on the pathogen would also be reduced by employing several rust resistant cultivars in production-scale situations.