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      Seed ecology of European mesic meadows

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          Abstract

          Background and Aims

          European mesic meadows are semi-natural open habitats of high biodiversity and an essential part of European landscapes. These species-rich communities can be a source of seed mixes for ecological restoration, urban greening and rewilding. However, limited knowledge of species germination traits is a bottleneck to the development of a competitive native seed industry. Here, we synthesize the seed ecology of mesic meadows.

          Methods

          We combined our own experimental data with data obtained from databases to create a combined dataset containing 2005 germination records of 90 plant species from 31 European countries. We performed a Bayesian meta-analysis of this dataset to test the seed germination response to environmental cues including scarification, stratification, temperature, alternating temperature and light. We also used multivariate ordination to check the relationship between seed traits (germination and morphology) and species ecological preferences, and to compare the seed ecology of mesic meadows with that of other herbaceous plant communities from the same geographic area.

          Key Results

          The seed ecology of mesic meadows is characterized by (1) high seed germinability when compared with other herbaceous plant communities; (2) low correspondence between seed traits and species ecological preferences; and (3) a deep phylogenetic separation between the two major families, Poaceae and Fabaceae. Poaceae produce many light seeds that respond to gap-detecting germination cues (alternating temperatures and light); Fabaceae produce fewer heavy seeds, which need scarification to break their physical dormancy.

          Conclusions

          High germinability of meadow seeds will reduce their capacity to form persistent seed banks, resulting in dispersal limitations to passive regeneration. For centuries, human activities have shaped the regeneration of meadows, leading to a loss of seed dormancy and decoupling seeds from seasonal cycles, as has been found in many domesticated species. The same anthropic processes that have shaped semi-natural mesic meadows have left them dependent on continued human intervention for their regeneration, highlighting the importance of active restoration via seed supply.

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          Most cited references123

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          NIH Image to ImageJ: 25 years of image analysis

          For the past twenty five years the NIH family of imaging software, NIH Image and ImageJ have been pioneers as open tools for scientific image analysis. We discuss the origins, challenges and solutions of these two programs, and how their history can serve to advise and inform other software projects.
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            FactoMineR: AnRPackage for Multivariate Analysis

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              Is Open Access

              Climatologies at high resolution for the earth’s land surface areas

              High-resolution information on climatic conditions is essential to many applications in environmental and ecological sciences. Here we present the CHELSA (Climatologies at high resolution for the earth’s land surface areas) data of downscaled model output temperature and precipitation estimates of the ERA-Interim climatic reanalysis to a high resolution of 30 arc sec. The temperature algorithm is based on statistical downscaling of atmospheric temperatures. The precipitation algorithm incorporates orographic predictors including wind fields, valley exposition, and boundary layer height, with a subsequent bias correction. The resulting data consist of a monthly temperature and precipitation climatology for the years 1979–2013. We compare the data derived from the CHELSA algorithm with other standard gridded products and station data from the Global Historical Climate Network. We compare the performance of the new climatologies in species distribution modelling and show that we can increase the accuracy of species range predictions. We further show that CHELSA climatological data has a similar accuracy as other products for temperature, but that its predictions of precipitation patterns are better.
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                Author and article information

                Contributors
                (View ORCID Profile)
                (View ORCID Profile)
                Journal
                Annals of Botany
                Oxford University Press (OUP)
                0305-7364
                1095-8290
                February 01 2022
                January 28 2022
                October 27 2021
                February 01 2022
                January 28 2022
                October 27 2021
                : 129
                : 2
                : 121-134
                Affiliations
                [1 ]IMIB—Biodiversity Research Institute, University of Oviedo, Mieres, Spain
                [2 ]Departamento de Biología de Organismos y Sistemas, Universidad de Oviedo, Oviedo/Uviéu, Spain
                [3 ]Banco Português de Germoplasma Vegetal, Instituto Nacional de Investigação Agrária e Veterinária (INIAV), Braga, Portugal
                [4 ]Departamento de Ciencias Agrarias y Medio Natural, Universidad de Zaragoza, Huesca, Spain
                [5 ]CIRSEC - Centre for Climate Change Impact, University of Pisa, Pisa, Italy
                Article
                10.1093/aob/mcab135
                9f297a81-524d-403d-a835-0d041a73ff0d
                © 2021

                https://academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model

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