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  1. 4301

    Skin-associated Corynebacterium amycolatum shares cobamides by M. H. Swaney, N. Henriquez, T. Campbell, J. Handelsman, L. R. Kalan

    Published 2025-01-01
    “…Therefore, we hypothesize that cobamide sharing mediates skin community dynamics. Preliminary work predicts that several skin-associated Corynebacterium species encode de novo cobamide biosynthesis and that their abundance is associated with skin microbiome diversity. …”
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  2. 4302

    Oceanographic and trophodynamic underpinnings of anchovy success in the northern California Current by Kelsey Swieca, Kelsey Swieca, Su Sponaugle, Su Sponaugle, Moritz S. Schmid, Jami Ivory, Robert K. Cowen

    Published 2025-05-01
    “…This suggests that the upwelling front may act as a shoreward boundary for anchovy larvae, affecting their access to the highly nutritious prey base typical of the Oregon continental shelf waters in summer.DiscussionVariation in larval anchovy growth with local oceanographic conditions and fine-scale distributions of prey and predators provides a mechanistic hypothesis of food-web dynamics which will enhance our ability to predict the response of forage fishes to ecosystem variability.…”
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  3. 4303

    Previous reproductive success and environmental variation influence nest‐site fidelity of a subarctic‐nesting goose by Jordan M. Thompson, Brian D. Uher‐Koch, Bryan L. Daniels, Thomas V. Riecke, Joel A. Schmutz, Benjamin S. Sedinger

    Published 2024-10-01
    “…., the win‐stay lose‐switch strategy) may be beneficial when habitat quality is spatially variable and temporally predictable; however, changes in environmental conditions may constrain dispersal decisions despite previous reproductive success. …”
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  4. 4304

    Analysis on Spatio-Temporal Variability of Fractional Vegetation Cover and Influencing Factors from 2000 to 2020 in Southwestern China by Yuting LIU, Lei WANG, Xiehui LI, Lei GUO

    Published 2024-02-01
    “…The southwestern China is a vital ecological safeguard and is characterized by ecological fragility and climate sensitivity.Based on the MOD13A3 NDVI dataset, this study used a pixel dichotomy model to calculate the average Fractional Vegetation Cover (FVC) from 2000 to 2020 annually, during growing seasons, and in each of the four seasons for the whole southwestern China and its provinces, a spatio-temporal variation analysis was then performed on FVC across different time scales.The study also discussed the primary factors influencing FVC changes over the past 21 years, using ERA5 temperature data, GPCP satellite precipitation data, and DEM data.Finally, the Hurst Index was used to predict future FVC trends.(1) The results indicate that from 2000 to 2020, the overall FVC in the eastern part of the southwestern China showed an increasing trend, particularly in Chongqing and Guizhou, while Tibet showed a general decline.(2) Spatially, the FVC generally showed a "higher in the east and lower in the west" trend, with areas where FVC increased accounting for 43.9% of the total area, and areas of decrease accounting for 53.5%.(3) Precipitation promotes FVC, while temperature has varied effects in different regions.(4) Human activity significantly impacts FVC, with promotion, suppression, and no effect zones accounting for 40.4%, 47.6%, and 12.0% of the grid percentage, respectively.(5) Elevation and FVC over different time scales are significantly correlated but both exhibit a pronounced declining trend.FVC increases significantly with slope, but decreases when the slope is greater than 25°.The effect of aspect on FVC in the southwestern China is less significant than that of slope, elevation, and climatic factors.(6) In the future, the FVC in Tibet and the eastern parts of Sichuan, Yunnan, and Guizhou in the southwestern China will increase, while most areas of western Sichuan and the Hengduan Mountains will exhibit a decreasing trend.The results can provide data support and scientific guidance for the formulation of ecological protection plans in the southwestern China.…”
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  5. 4305
  6. 4306

    Tracer-based Rapid Anthropogenic Carbon Estimation (TRACE) by B. R. Carter, B. R. Carter, J. Schwinger, R. Sonnerup, A. J. Fassbender, J. D. Sharp, J. D. Sharp, L. M. Dias, L. M. Dias, D. E. Sandborn

    Published 2025-07-01
    “…For these reasons, we need accessible tools to quantify ocean C<span class="inline-formula"><sub>anth</sub></span> inventories and distributions and to predict how they might evolve in response to future emissions and mitigation activities. …”
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  7. 4307