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Alpine grassland vegetation supports globally important biodiversity and ecosystems that are increasingly threatened by climate warming and other environmental changes. Trait-based approaches can support understanding of vegetation responses to global change drivers and consequences for ecosystem functioning. In six sites along a 1314 m elevational gradient in Puna grasslands in the Peruvian Andes, we collected datasets on vascular plant composition, plant functional traits, biomass, ecosystem fluxes, and climate data over three years. The data were collected in the wet and dry season and from plots with different fire histories. We selected traits associated with plant resource use, growth, and life history strategies (leaf area, leaf dry/wet mass, leaf thickness, specific leaf area, leaf dry matter content, leaf C, N, P content, C and N isotopes). The trait dataset contains 3,665 plant records from 145 taxa, 54,036 trait measurements (increasing the trait data coverage of the regional flora by 420%) covering 14 traits and 121 plant taxa (ca. 40% of which have no previous publicly available trait data) across 33 families., (© 2024. The Author(s).)

Understanding the relationships among multiple land use functions (LUFs) is crucial for land-based spatial planning that can guide targeted land use policy-making in complex socio-ecological systems. However, few studies concerned the interactions among various LUFs integrating the issues of economy, environment, and society at a fine scale. In this study, we quantified 12 LUFs using a geospatial model and statistical analysis at the grid scale in Jiangsu Province. Then, we identified the relationships among three primary LUFs-agricultural production function (APF), urban-rural living function (ULF), and ecological maintenance function (EMF)-and further explored the determinants of LUF trade-offs aimed to provide a reference for policy-makers to make decisions in future land use planning and management. The results revealed that the high trade-off areas for APF and ULF are mainly distributed in central and northern Jiangsu, and the trade-offs for both APF-EMF and ULF-EMF were higher in the area covered with water and forest. The determinants of LUF trade-offs mainly refers to land use/land cover, potential evapotranspiration, and vegetation coverage ratio. Moreover, landscape configuration metrics and distance to the nearest county and nearest road also have remarkable impacts on the trade-offs of APF-EMF and ULF-EMF. Finally, we proposed that the concepts of LUF trade-offs should be incorporated into the processes of delineating boundaries for urban growth, farmland, and natural areas. We also propose that land consolidation projects should be implemented in an orderly manner to alleviate LUF trade-offs., Competing Interests: Declaration of competing interest The authors declared that they have no conflicts of interest to this work., (Copyright © 2021 Elsevier B.V. All rights reserved.)

As Open Science practices become more commonplace, there is a need for the next generation of scientists to be well versed in these aspects of scientific research. Yet, many training opportunities for early career researchers (ECRs) could better emphasize or integrate Open Science elements. Field courses provide opportunities for ECRs to apply theoretical knowledge, practice new methodological approaches, and gain an appreciation for the challenges of real-life research, and could provide an excellent platform for integrating training in Open Science practices. Our recent experience, as primarily ECRs engaged in a field course interrupted by COVID-19, led us to reflect on the potential to enhance learning outcomes in field courses by integrating Open Science practices and online learning components. Specifically, we highlight the opportunity for field courses to align teaching activities with the recent developments and trends in how we conduct research, including training in: publishing registered reports, collecting data using standardized methods, adopting high-quality data documentation, managing data through reproducible workflows, and sharing and publishing data through appropriate channels. We also discuss how field courses can use online tools to optimize time in the field, develop open access resources, and cultivate collaborations. By integrating these elements, we suggest that the next generation of field courses will offer excellent arenas for participants to adopt Open Science practices., Competing Interests: None declared., (© 2020 The Authors. Ecology and Evolution published by John Wiley & Sons Ltd.)

Land use function (LUF) is a valuable concept that allows for more integrated assessments of land system change. Identifying the changes and relationships of multiple LUFs is pertinent to land use planning and management. Selection and quantification of LUF indicators are critical for LUFs assessment. However, past studies have mostly assessed LUFs using socioeconomic data, which are not suitable for spatial variable quantification. In this study, we proposed a new LUFs classification system based on spatial planning goals, and we applied the system to assess 12 LUFs across 63 counties in Jiangsu Province of eastern China based on multi-source data using geospatial modeling tools combined with statistical analysis of socioeconomic data. We also analyzed the change in LUFs between 2000 and 2015, as well as the interactions among multiple functions. Finally, we identified distinct function zones based on the LUFs assessment in 2000 and 2015 using k-means clustering. The result showed that 12 LUFs displayed significant changes and interactions between 2000 and 2015, which can be explained by differing topography and social-ecological characteristics among counties. Additionally, we found four distinct LUF zones that are spatially agglomerated in similar landscapes and characterize specific LUF relationships in each cluster. In the future, local LUFs and their changes over time should be taken into consideration for land use planning and management, which provide a reference for policy-makers to make decisions that better match local development realities., (Copyright © 2018 Elsevier B.V. All rights reserved.)