Forest Habitat Use by White-tailed Deer in the Arkansas Coastal Plain

Forest habitat use by five radio-equipped white-tailed deer (Odocoileus virginianus) was monitored in the Arkansas Coastal Plain during 1982-84. The deer were located 821 times. Use of forest types was compared to expected use as calculated from availability. The study area was also divided into 491 two-hectare cells for which timber characteristics and number of deer locations were determined. Pine sawtimber was the most heavily used forest type in all seasons and was used more often than expected during spring. Also used more than expected were brushy areas (clearcut but not site prepared) during spring, summer and fall and openings (grass fields and a site-prepared clearcut) during summer. Hardwood stands were used less often than expected during every season. Also used less than expected were pine pulpwood stands in summer and pine-hardwood stands during spring and summer. A significant (P \u3c 0.001) discriminant function correctly classified 74% of the two-hectare cells as used (1+ locations) or not used (0 locations). Used cells often had less hardwood pulpwood and sawtimber and more pine sawtimber than nonused cells. Use by deer of cells containing stand edges did not differ from use of cells without edges

Emulating IPCC AR4 atmosphere-ocean and carbon cycle models for projecting global-mean, hemispheric and land/ocean temperatures: MAGICC 6.0

International audienceCurrent scientific knowledge on the future response of the climate system to human-induced perturbations is comprehensively captured by various model intercomparison efforts. In the preparation of the Fourth Assessment Report (AR4) of the Intergovernmental Panel on Climate Change (IPCC), intercomparisons were organized for atmosphere-ocean general circulation models (AOGCMs) and carbon cycle models, named "CMIP3" and "C4MIP", respectively. Despite their tremendous value for the scientific community and policy makers alike, there are some difficulties in interpreting the results. For example, key radiative forcings have not been considered or standardized in the majority of AOGCMs integrations and carbon cycle runs. Furthermore, the AOGCM analysis of plausible emission pathways was restricted to only three SRES scenarios. This study attempts to address these issues. We present an updated version of MAGICC, the simple carbon cycle-climate model used in past IPCC Assessment Reports with enhanced representation of time-varying climate sensitivities, carbon cycle feedbacks, aerosol forcings and ocean heat uptake characteristics. This new version of MAGICC (6.0) is successfully calibrated against the higher complexity AOGCM and carbon cycle models. Parameterizations of MAGICC 6.0 are provided. Previous MAGICC versions and emulations shown in IPCC AR4 (WG1, Fig. 10.26, page 803) yielded, in average, a 10% larger global-mean temperature increase over the 21st century compared to the AOGCMs. The reasons for this difference are discussed. The emulations presented here using MAGICC 6.0 match the mean AOGCM responses to within 2.2% for the SRES scenarios. This enhanced emulation skill is due to: the comparison on a "like-with-like" basis using AOGCM-specific subsets of forcings, a new calibration procedure, as well as the fact that the updated simple climate model can now successfully emulate some of the climate-state dependent effective climate sensitivities of AOGCMs. The mean diagnosed effective climate sensitivities of the AOGCMs is 2.88°C, about 0.33°C cooler than the reported slab ocean climate sensitivities. Finally, we examine the combined climate system and carbon cycle emulations for the complete range of IPCC SRES emission scenarios and some lower mitigation pathways

Guide to chicken health and management in Ethiopia: For farmers and development agents

Biotechnology and Biological Sciences Research Council, United KingdomDepartment for International Development, United Kingdo

Emulating coupled atmosphere-ocean and carbon cycle models with a simpler model, MAGICC6 - Part 2: Applications

Intercomparisons of coupled atmosphere-ocean general circulation models (AOGCMs) and carbon cycle models are important for galvanizing our current scientific knowledge to project future climate. Interpreting such intercomparisons faces major challenges, not least because different models have been forced with different sets of forcing agents. Here, we show how an emulation approach with MAGICC6 can address such problems. In a companion paper (Meinshausen et al., 2011a), we show how the lower complexity carbon cycle-climate model MAGICC6 can be calibrated to emulate, with considerable accuracy, globally aggregated characteristics of these more complex models. Building on that, we examine here the Coupled Model Intercomparison Project's Phase 3 results (CMIP3). If forcing agents missed by individual AOGCMs in CMIP3 are considered, this reduces ensemble average temperature change from pre-industrial times to 2100 under SRES A1B by 0.4 degree(s)C. Differences in the results from the 1980 to 1999 base period (as reported in IPCC AR4) to 2100 are negligible, however, although there are some differences in the trajectories over the 21st century. In a second part of this study, we consider the new RCP scenarios that are to be investigated under the forthcoming CMIP5 intercomparison for the IPCC Fifth Assessment Report. For the highest scenario, RCP8.5, relative to pre-industrial levels, we project a median warming of around 4.6 degree(s)C by 2100 and more than 7 degree(s)C by 2300. For the lowest RCP scenario, RCP3-PD, the corresponding warming is around 1.5 degree(s)C by 2100, decreasing to around 1.1 degree(s)C by 2300 based on our AOGCM and carbon cycle model emulations. Implied cumulative CO2 emissions over the 21st century for RCP8.5 and RCP3-PD are 1881 GtC (1697 to 2034 GtC, 80% uncertainty range) and 381 GtC (334 to 488 GtC), when prescribing CO2 concentrations and accounting for uncertainty in the carbon cycle. Lastly, we assess the reasons why a previous MAGICC version (4.2) used in IPCC AR4 gave roughly 10% larger warmings over the 21st century compared to the CMIP3 average. We find that forcing differences and the use of slightly too high climate sensitivities inferred from idealized high-forcing runs were the major reasons for this difference.M. Meinshausen, T. M. L. Wigley, and S. C. B. Rape

Extreme value and cluster analysis of European daily temperature series

Time series of daily mean temperature obtained from the European Climate Assessment data set is analyzed with respect to their extremal properties. A time-series clustering approach which combines Bayesian methodology, extreme value theory and classification techniques is adopted for the analysis of the regional variability of temperature extremes. The daily mean temperature records are clustered on the basis of their corresponding predictive distributions for 25-, 50- and 100-year return values. The results of the cluster analysis showa clear distinction between the highest altitude stations, for which the return values are lowest, and the remaining stations. Furthermore, a clear distinction is also found between the northernmost stations in Scandinavia and the stations in central and southern Europe. This spatial structure of the return period distributions for 25-, 50- and 100-years seems to be consistent with projected changes in the variability of temperature extremes over Europe pointing to a different behavior in central Europe than in northern Europe and the Mediterranean area, possibly related to the effect of soil moisture and land-atmosphere coupling.‘Acções Integradas Luso-Espanholas’ under the grants E-83/09 and HP2008- 008

NamesforLife Semantic Resolution Services for the Life Sciences

A major challenge in bioinformatics, life sciences, and medicine is using correct and informative names. While this sounds simple enough, many different naming conventions exist in the life sciences and medicine that may be either complementary or competitive with other naming conventions. For a variety of reasons, proper names are not always used, leading to an accumulated semantic ambiguity that readers of the literature and end users of databases are left to resolve on their own. This ambiguity is a growing problem and the biocuration community is aware of its consequences. 

To assist those confronted with ambiguous names (which not only includes researchers but clinicians, manufacturers, patent attorneys, and others who use biological data in their routine work), we developed a generalizable semantic model that represents names, concepts, and exemplars (representations of biological entities) as distinct objects. By identifying each object with a Digital Object Identifier (DOI) it becomes possible to place forward-pointing links in the published literature, in databases, and vector graphics that can be used as part of a mechanism for resolving ambiguities, thereby “future proofing” a nomenclature or terminology. A full implementation of the N4L model for the _Bacteria_ and _Archaea_ was released in April, 2010. The system is professionally curated and represents a Tier III resource in Parkhill’s view of bioinformatic services. A variety of tools and web services have been developed for readers, publishers, and others (N4L Guide, N4L Autotagger, N4L Semantic Search, N4L Taxonomic Abstracts) and we are incorporating other taxonomies into the N4L data model, as well as adding additional phenotypic, genotypic, and genomic information to the existing exemplars to add greater value to end users