Investigating how climate change alters selection regimes is a crucial step toward understanding the potential of populations to evolve in the face of changing conditions. Previous studies have mainly focused on understanding how changing climate directly influences selection, while the role of species' interactions has received little attention. Here, we used a transplant experiment along an elevation gradient to estimate how climate warming and competitive interactions lead to shifts in directional phenotypic selection on morphology and phenology of four alpine plants. We found that warming generally imposed novel selection, with the largest shifts in regimes acting on specific leaf area and flowering time across species. Competitors instead weakened the selection acting on traits that was imposed directly by warming. Weakened or absent selection in the presence of competitors was largely associated with the suppression of absolute means and variation of fitness. Our results suggest that although climate change can impose strong selection, competitive interactions within communities might act to limit selection and thereby stymie evolutionary responses in alpine plants facing climate change.
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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC10871967 | PMC |
http://dx.doi.org/10.1093/evlett/qrad066 | DOI Listing |
This study presents an integrated framework that combines spatial clustering techniques and multi-source geospatial data to comprehensively assess and understand geological hazards in Hunan Province, China. The research integrates self-organizing map (SOM) and geo-self-organizing map (Geo-SOM) to explore the relationships between environmental factors and the occurrence of various geological hazards, including landslides, slope failures, collapses, ground subsidence, and debris flows. The key findings reveal that annual average precipitation (Pre), profile curvature (Pro_cur), and slope (Slo) are the primary factors influencing the composite geological hazard index (GI) across the province.
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January 2025
Department of Agronomy, Faculty of Agriculture and Environment, The Islamia University of Bahawalpur, Bahawalpur, 63100, Pakistan.
Potato is cultivated all the year round in Pakistan. However, the major crop is the autumn crop which is planted in mid-October and contributes 80-85% of the total production. The abrupt climate change has affected the weather patterns all over the world, resulting in the reduction of the mean air temperature in autumn by almost 1.
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January 2025
State Key Laboratory of Mariculture Breeding, Key Laboratory of Marine Biotechnology of Fujian Province, Institute of Oceanology, College of Marine Sciences, Haixia Institute of Science and Technology, Fujian Agriculture and Forestry University, Fuzhou, 350002, China.
Anisarchus medius (Reinhardt, 1837) is a widely distributed Arctic fish, serving as an indicator of climate change impacts on coastal Arctic ecosystems. This study presents a chromosome-level genome assembly for A. medius using PacBio sequencing and Hi-C technology.
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January 2025
Division of Natural Sciences, German Archaeological Institute, Berlin, Germany.
The first Neolithic farmers arrived in the Western Mediterranean area from the East. They established settlements in coastal areas and over time migrated to new environments, adapting to changing ecological and climatic conditions. While farming practices and settlements in the Western Mediterranean differ greatly from those known in the Eastern Mediterranean and central Europe, the extent to which these differences are connected to the local environment and climate is unclear.
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January 2025
State Key Joint Laboratory of Environment Simulation and Pollution Control (SKLESPC), School of Environment, Tsinghua University, Beijing, 100084, China.
Assessing the dynamics of offshore wind potential and costs is essential for low-carbon energy policy decision-making and energy modeling, but no open-source, spatial explicit and technologically detailed dataset is available. This study addresses this gap by employing a consistent assessment framework that integrates GIS analysis, a wind reanalysis model, a component-based cost model and scenario analysis. It identifies suitable space for offshore wind deployment considering 12 technical and policy constraints, estimates hourly output curves, capacity factors, and technology cost dynamics by components across 5058 grid points with a 10 km resolution from 2020 to 2035 under three technical change scenarios.
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