Variation in honey bee gut microbial diversity affected by ontogenetic stage, age and geographic location

Social honey bees, Apis mellifera, host a set of distinct microbiota, which is similar across the continents and various honey bee species. Some of these bacteria, such as lactobacilli, have been linked to immunity and defence against pathogens. Pathogen defence is crucial, particularly in larval stages, as many pathogens affect the brood. However, information on larval microbiota is conflicting. Seven developmental stages and drones were sampled from 3 colonies at each of the 4 geographic locations of A. mellifera carnica, and the samples were maintained separately for analysis. We analysed the variation and abundance of important bacterial groups and taxa in the collected bees. Major bacterial groups were evaluated over the entire life of honey bee individuals, where digestive tracts of same aged bees were sampled in the course of time. The results showed that the microbial tract of 6-day-old 5th instar larvae were nearly equally rich in total microbial counts per total digestive tract weight as foraging bees, showing a high percentage of various lactobacilli (Firmicutes) and Gilliamella apicola (Gammaproteobacteria 1). However, during pupation, microbial counts were significantly reduced but recovered quickly by 6 days post-emergence. Between emergence and day 6, imago reached the highest counts of Firmicutes and Gammaproteobacteria, which then gradually declined with bee age. Redundancy analysis conducted using denaturing gradient gel electrophoresis identified bacterial species that were characteristic of each developmental stage. The results suggest that 3-day 4th instar larvae contain low microbial counts that increase 2-fold by day 6 and then decrease during pupation. Microbial succession of the imago begins soon after emergence. We found that bacterial counts do not show only yearly cycles within a colony, but vary on the individual level. Sampling and pooling adult bees or 6th day larvae may lead to high errors and variability, as both of these stages may be undergoing dynamic succession

Genotypes and phenotypes for apolipoprotein E and Alzheimer disease in the Honolulu-Asia aging study

BACKGROUND: The utility of apolipoprotein E (ApoE) type as an indicator of genetic susceptibility to Alzheimer disease (AD) depends on the reliability of typing. Although ApoE protein isoform phenotyping is generally assumed equivalent to genotyping from DNA, phenotype-genotype differences have been reported. METHODS: ApoE genotype and phenotype results were examined for 3564 older (ages 71-93 years) Japanese-American male participants of the Honolulu-Asia Aging Study, an ongoing population-based study of aging and dementia. RESULTS: Both methods demonstrated similar associations of ApoE type with AD: a direct association with ApoE4 and a less dramatic inverse association ApoE2. Advanced age did not appear to influence the ApoE4-AD association. The association with AD among ApoE4 homozygotes [odds ratio (OR) = 14.7] was higher than expected based on an observed OR of 2.0 in heterozygotes. Phenotype-genotype nonconcordance was more frequent for ApoE2 than for ApoE4. The ApoE2 phenotype occurred at a frequency of 7.9% vs a genotype frequency of 4.9%, corresponding to a probability of 56% that an individual with ApoE2 phenotype had the same genotype. CONCLUSIONS: Whereas E4 and E2 phenotypes and genotypes were comparably associated with AD, neither method would be expected to substantially improve the efficiency of case finding in the context of population screening beyond prediction based on age and education. Nonconcordance of phenotype and genotype was substantial for E2 and modest for E4 in this population. The ApoE4-AD association was independent of age

Hotspots of uncertainty in land-use and land-cover change projections: a global-scale model comparison

Model-based global projections of future land use and land cover (LULC) change are frequently used in environmental assessments to study the impact of LULC change on environmental services and to provide decision support for policy. These projections are characterized by a high uncertainty in terms of quantity and allocation of projected changes, which can severely impact the results of environmental assessments. In this study, we identify hotspots of uncertainty, based on 43 simulations from 11 global-scale LULC change models representing a wide range of assumptions of future biophysical and socio-economic conditions. We attribute components of uncertainty to input data, model structure, scenario storyline and a residual term, based on a regression analysis and analysis of variance. From this diverse set of models and scenarios we find that the uncertainty varies, depending on the region and the LULC type under consideration. Hotspots of uncertainty appear mainly at the edges of globally important biomes (e.g. boreal and tropical forests). Our results indicate that an important source of uncertainty in forest and pasture areas originates from different input data applied in the models. Cropland, in contrast, is more consistent among the starting conditions, while variation in the projections gradually increases over time due to diverse scenario assumptions and different modeling approaches. Comparisons at the grid cell level indicate that disagreement is mainly related to LULC type definitions and the individual model allocation schemes. We conclude that improving the quality and consistency of observational data utilized in the modeling process as well as improving the allocation mechanisms of LULC change models remain important challenges. Current LULC representation in environmental assessments might miss the uncertainty arising from the diversity of LULC change modeling approaches and many studies ignore the uncertainty in LULC projections in assessments of LULC change impacts on climate, water resources or biodiversity

Land-based measures to mitigate climate change : potential and feasibility by country

Acknowledgements The design of this study and the data generated was guided by expert consultations and relied on the help of many. We thank all those who contributed: Sierra Gladfelter, Jo House, Mercedes Bustamante, Susan Cook-Patton, Sara Leavitt, Nick Wolff, and Thomas Worthington. We thank M.-J. Valentino at Imaginary Office for helping to design the first three figures. This work was supported by the authors’ institutions and funding sources, including the Climate and Land-use Alliance, the Dutch Ministry of Agriculture, Nature Management and Food Quality, and the EU H2020 projects VERIFY and ENGAGE (grant agreement no. 821471).Peer reviewedPublisher PD

Key determinants of global land-use projections

Land use is at the core of various sustainable development goals. Long-term climate foresight studies have structured their recent analyses around five socio-economic pathways (SSPs), with consistent storylines of future macroeconomic and societal developments; however, model quantification of these scenarios shows substantial heterogeneity in land-use projections. Here we build on a recently developed sensitivity approach to identify how future land use depends on six distinct socio-economic drivers (population, wealth, consumption preferences, agricultural productivity, land-use regulation, and trade) and their interactions. Spread across models arises mostly from diverging sensitivities to long-term drivers and from various representations of land-use regulation and trade, calling for reconciliation efforts and more empirical research. Most influential determinants for future cropland and pasture extent are population and agricultural efficiency. Furthermore, land-use regulation and consumption changes can play a key role in reducing both land use and food-security risks, and need to be central elements in sustainable development strategies

Protecting 30% of the planet for nature: costs, benefits and economic implications

A. Waldron, K. Nakamura, J. Sze, T. Vilela, A. Escobedo, P. Negret Torres, R. Button, K. Swinnerton, A. Toledo, P. Madgwick, N. Mukherjee were supported by National Geographic and the Resources Legacy Fund. V. Christensen was supported by NSERC Discovery Grant RGPIN-2019-04901. M. Coll and J. Steenbeek were supported by EU Horizon 2020 research and innovation programme under grant agreement No 817578 (TRIATLAS). D. Leclere was supported by TradeHub UKRI CGRF project. R. Heneghan was supported by Spanish Ministry of Science, Innovation and Universities, Acciones de Programacion Conjunta Internacional (PCIN-2017-115). M. di Marco was supported by MIUR Rita Levi Montalcini programme. A. Fernandez-Llamazares was supported by Academy of Finland (grant nr. 31176). S. Fujimori and T. Hawegawa were supported by The Environment Research and Technology Development Fund (2-2002) of the Environmental Restoration and Conservation Agency of Japan and the Sumitomo Foundation. V. Heikinheimo was supported by Kone Foundation, Social Media for Conservation project. K. Scherrer was supported by the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme under grant agreement No 682602. U. Rashid Sumaila acknowledges the OceanCanada Partnership, which funded by the Social Sciences and Humanities Research Council of Canada (SSHRC). T. Toivonen was supported by Osk. Huttunen Foundation & Clare Hall college, Cambridge. W. Wu was supported by The Environment Research and Technology Development Fund (2-2002) of the Environmental Restoration and Conservation Agency of Japan. Z. Yuchen was supported by a Ministry of Education of Singapore Research Scholarship Block (RSB) Research Fellowship

Working paper analysing the economic implications of the proposed 30% target for areal protection in the draft post-2020 Global Biodiversity Framewor

58 pages, 5 figures, 3 tables- The World Economic Forum now ranks biodiversity loss as a top-five risk to the global economy, and the draft post-2020 Global Biodiversity Framework proposes an expansion of conservation areas to 30% of the earth’s surface by 2030 (hereafter the “30% target”), using protected areas (PAs) and other effective area-based conservation measures (OECMs). - Two immediate concerns are how much a 30% target might cost and whether it will cause economic losses to the agriculture, forestry and fisheries sectors. - Conservation areas also generate economic benefits (e.g. revenue from nature tourism and ecosystem services), making PAs/Nature an economic sector in their own right. - If some economic sectors benefit but others experience a loss, high-level policy makers need to know the net impact on the wider economy, as well as on individual sectors. [...]A. Waldron, K. Nakamura, J. Sze, T. Vilela, A. Escobedo, P. Negret Torres, R. Button, K. Swinnerton, A. Toledo, P. Madgwick, N. Mukherjee were supported by National Geographic and the Resources Legacy Fund. V. Christensen was supported by NSERC Discovery Grant RGPIN-2019-04901. M. Coll and J. Steenbeek were supported by EU Horizon 2020 research and innovation programme under grant agreement No 817578 (TRIATLAS). D. Leclere was supported by TradeHub UKRI CGRF project. R. Heneghan was supported by Spanish Ministry of Science, Innovation and Universities, Acciones de Programacion Conjunta Internacional (PCIN-2017-115). M. di Marco was supported by MIUR Rita Levi Montalcini programme. A. Fernandez-Llamazares was supported by Academy of Finland (grant nr. 311176). S. Fujimori and T. Hawegawa were supported by The Environment Research and Technology Development Fund (2-2002) of the Environmental Restoration and Conservation Agency of Japan and the Sumitomo Foundation. V. Heikinheimo was supported by Kone Foundation, Social Media for Conservation project. K. Scherrer was supported by the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme under grant agreement No 682602. U. Rashid Sumaila acknowledges the OceanCanada Partnership, which funded by the Social Sciences and Humanities Research Council of Canada (SSHRC). T. Toivonen was supported by Osk. Huttunen Foundation & Clare Hall college, Cambridge. W. Wu was supported by The Environment Research and Technology Development Fund (2-2002) of the Environmental Restoration and Conservation Agency of Japan. Z. Yuchen was supported by a Ministry of Education of Singapore Research Scholarship Block (RSB) Research FellowshipPeer reviewe