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Fashion products made from repurposed materials (e.g., backpacks made from pineapple leaves) have become more prevalent nowadays, and their environmental sustainability is one of the core advantages. Yet, it is currently unclear how consumers respond to products made from repurposed materials. We conducted three experiments to examine the effects of three material features, namely function, sustainability, and distinguishability, on consumer preferences for fashion products made from repurposed materials. The results indicate that, when the function of repurposed materials is as good as that of conventional materials, consumers prefer a product made from repurposed materials over the same product made from conventional materials. Also, consumers in general prefer repurposed materials to be less visually distinguishable. Finally, when the sustainability of the repurposed products is emphasized, consumers appear more likely to choose products made from repurposed materials, even when these products have an inferior function. In conclusion, to promote fashion products made from repurposed materials, marketers may emphasize the function and sustainability of repurposed materials, and producers may manufacture repurposed materials that visually resemble conventional materials.

Abstract Background The ascomycete fungus Podospora anserina has been appreciated for its targeted carbohydrate-active enzymatic arsenal. As a late colonizer of herbivorous dung, the fungus acts specifically on the more recalcitrant fraction of lignocellulose and this lignin-rich biotope might have resulted in the evolution of ligninolytic activities. However, the lignin-degrading abilities of the fungus have not been demonstrated by chemical analyses at the molecular level and are, thus far, solely based on genome and secretome predictions. To evaluate whether P. anserina might provide a novel source of lignin-active enzymes to tap into for potential biotechnological applications, we comprehensively mapped wheat straw lignin during fungal growth and characterized the fungal secretome. Results Quantitative 13C lignin internal standard py-GC–MS analysis showed substantial lignin removal during the 7 days of fungal growth (24% w/w), though carbohydrates were preferably targeted (58% w/w removal). Structural characterization of residual lignin by using py-GC–MS and HSQC NMR analyses demonstrated that Cα-oxidized substructures significantly increased through fungal action, while intact β-O-4′ aryl ether linkages, p-coumarate and ferulate moieties decreased, albeit to lesser extents than observed for the action of basidiomycetes. Proteomic analysis indicated that the presence of lignin induced considerable changes in the secretome of P. anserina. This was particularly reflected in a strong reduction of cellulases and galactomannanases, while H2O2-producing enzymes clearly increased. The latter enzymes, together with laccases, were likely involved in the observed ligninolysis. Conclusions For the first time, we provide unambiguous evidence for the ligninolytic activity of the ascomycete fungus P. anserina and expand the view on its enzymatic repertoire beyond carbohydrate degradation. Our results can be of significance for the development of biological lignin conversion technologies by contributing to the quest for novel lignin-active enzymes and organisms. Background The ascomycete fungus Podospora anserina has been appreciated for its targeted carbohydrate-active enzymatic arsenal. As a late colonizer of herbivorous dung, the fungus acts specifically on the more recalcitrant fraction of lignocellulose and this lignin-rich biotope might have resulted in the evolution of ligninolytic activities. However, the lignin-degrading abilities of the fungus have not been demonstrated by chemical analyses at the molecular level and are, thus far, solely based on genome and secretome predictions. To evaluate whether P. anserina might provide a novel source of lignin-active enzymes to tap into for potential biotechnological applications, we comprehensively mapped wheat straw lignin during fungal growth and characterized the fungal secretome. Results Quantitative 13C lignin internal standard py-GC–MS analysis showed substantial lignin removal during the 7 days of fungal growth (24% w/w), though carbohydrates were preferably targeted (58% w/w removal). Structural characterization of residual lignin by using py-GC–MS and HSQC NMR analyses demonstrated that Cα-oxidized substructures significantly increased through fungal action, while intact β-O-4′ aryl ether linkages, p-coumarate and ferulate moieties decreased, albeit to lesser extents than observed for the action of basidiomycetes. Proteomic analysis indicated that the presence of lignin induced considerable changes in the secretome of P. anserina. This was particularly reflected in a strong reduction of cellulases and galactomannanases, while H2O2-producing enzymes clearly increased. The latter enzymes, together with laccases, were likely involved in the observed ligninolysis. Conclusions For the first time, we provide unambiguous evidence for the ligninolytic activity of the ascomycete fungus P. anserina and expand the view on its enzymatic repertoire beyond carbohydrate degradation. Our results can be of significance for the development of biological lignin conversion technologies by contributing to the quest for novel lignin-active enzymes and organisms.

This dataset contains measurements of downwelling short wave irradiance, measured in a small scale grid setup at Falkenberg: 20 sensors in 4 by 5 grid with a 50 meter grid spacing. Another 4 sensors were placed in all direction about 5 km away from the main grid at Falkenberg. The sampling rate is 10 Hz, to catch all irradiance variability, and is calibrated against a high quality sun tracker. The strength of this dataset is not the absolute accuracy, but rather the spatial measurements and ability to catch variability. Quality: Accuracy is estimated to be within 5% of a conventional pyranometer. Quality varies depending on weather type, but is best for high solar elevation angles (solar noon +/- 4 hours). Data is manually quality controlled, with detailed quality flags included in the dataset. Some anomalous data is not caught, in particular noisy data due to many insects on the sensor or small dirt from birds that reduces the signal slightly. These effects are much smaller than the driving weather patterns. The data is unsuitable for calculating radiation balances, but it is particularly useful for studying variability and patterns of solar irradiance on small scales. Funding: Dutch Research Council (NWO), Shedding Light On Cloud Shadows: VI.Vidi.192.068 Project: FESSTVaL (Field Experiment on submesoscale spatio-temporal variability in Lindenberg), a measurement campaign initiated by the Hans-Ertel-Center for Weather Research.

Table S1-1 Quantification of GHG and Nr flow intensities [kg CO2eq (kg product)-1 yr-1] or [g N (kg product)-1 yr-1] with the CAPRI N-LCA model for six main livestock products (BEEF: beef, PORK: pork, EGGS: eggs, POUM: poultry meat; DAIR: milk and dairy products, SGMP: meat from sheep and goats) and six main vegetable food groups (POTA: potatoes, SUGB: sugar beet before processing, OILP: oil seeds before processing; CERR: cereals, LEGU: leguminous crops) as well as other crops (OCRP) and aggregated livestock (ANIMP) and vegetable (CROPP) food. Table S2-1 Quantification of the main N budget flows in the EU25 agriculture sector

This repository contains additional data associated to Tedersoo et al. (2022) (https://doi.org/10.1111/gcb.16398).This repository contains those data necessary to rerun the superecoregion design and the endemicity analyses as well as vector maps on the endemicity and OTU richness of individual superecoregions. Further data associated with the same publication, regarding the vulnerability analysis, can be found here: 10.5281/zenodo.6983158. input.zip - Input data needed to run the analysis scripts provided [here](https://github.com/Mycology-Microbiology-Center/Fungal_Endemicity_and_Vulnerability/superecoregions_and_endemicity). The entire folder is to be copied in the working directory to run the pipeline. output.zip - Further data needed to run the analysis scripts provided [here](https://github.com/Mycology-Microbiology-Center/Fungal_Endemicity_and_Vulnerability/superecoregions_and_endemicity). The entire folder is to be copied in the working directory to run the pipeline. maps_otu_numbers_and_se_characteristics - vector maps of the superecoregions and the number of OTUs and endemic OTUs per region for different functional groups maps_traits_endemism - vector maps of endemicity indices per region f different functional groups 01_super_Ecoregions_numbers.pdf - vector map of the superecoregions used in the study numbered from 1 - 174. legend_01_super_ecoregions_numbers.csv - legend with superecoregion names for 01_super_Ecoregions_numbers.pdf. super_eoregions_results - Superecoregions with number of OTUs, number of endemic OTUs and endemicity indices in R data format, for further analyses and visualization super_ecoregions.shp - Superecoregions with number of OTUs, number of endemic OTUs and endemicity indices as shape file, for further analyses and visualization This repository contains additional data associated to Tedersoo et al. (2022) (https://doi.org/10.1111/gcb.16398).This repository contains those data necessary to rerun the superecoregion design and the endemicity analyses as well as vector maps on the endemicity and OTU richness of individual superecoregions. Further data associated with the same publication, regarding the vulnerability analysis, can be found here: 10.5281/zenodo.6983158.

The dataset covers biotic and abiotic data from the aquatic habitat of a population of the sediment-rooted macrophyte Myriophyllum spicatum in the temperate climate region (The Netherlands). The growth of M. spicatum was monitored in 0.2025 m2 plant baskets installed in an experimental ditch. Parameters monitored included biomass (fresh and dry weight), shoot length, seasonal short-term growth rates of shoots, relevant environmental parameters and weather data. This dataset includes the 2-year experimental biotic (macrophyte biomass and growth data) and environmental data (water quality data, sediment data). A second file includes the statistical data. A third file includes the weather data.

Figure 1data_Exp 2Figure 1 data: Condition of experimental seedlings in hummocks with contrasting shrub density and tree canopy in Experiment 2: No Trees - Low Shrub biomass (NTLS), No Trees - High Shrub biomass (NTHS), Present Trees - Low Shrub biomass (PTLS) and Present Trees - High shrub biomass (PTHS) during the warmest growing season (2011) and at the end of the experiment (2013). Seedling condition was defined as: healthy (< 50% of the needles turned yellow or brown) or unhealthy (> 50% of the needles turned yellow or brown). Seedlings were 1 month old at plantation time in the July 2010.Table 1_environmental conditions_Exp 1Table 1 data: Environmental conditions and vegetation characteristics in hummocks (circular and bands) and lawns for Experiment 1. Water table depth below surface is an average for the four growing seasons (2010-2013)Table 2_ photosynthesis data_Exp 1Table 2 photosynthesis data: Photosynthesis rates for experimental pine seedlings in hummocks (circular and bands) versus adjacent lawns for Experiment 1.Table 2_seedling responses_Exp 1Table 2 data: Responses of experimental pine seedlings in hummocks (circular and bands) versus adjacent lawns for Experiment 1 after 4 growing seasons. ST: Seeds inserted on top of moss; SB: Seeds inserted below moss; Small seedling (1 month old at plantation time); Large seedling (2 months old at plantation time). Emergence = % of planted seeds emerged after 1 year. Condition = % healthy seedlings. Stem growth corresponds to vertical stem growth for germinating (ST and SB) seedlings and new stem growth for older (small and large) seedlings.Table 3_regression seedling-environment_Exp 1Table 3 data for generalized linear models assessing the responses of experimental pine seedlings in hummocks (circular and bands) and adjacent lawns for Experiment 1 during the whole experimental period (2010-2013). ST: Seedlings from seeds inserted on top of moss; SB: Seedlings from seeds inserted below moss; Small seedling (1 month old at plantation time); Large seedling (2 months old at plantation time). Condition = % healthy seedlings. Growth = stem growth.Table 4_Environmental data_Exp 2Table 4: Environmental conditions in hummocks with contrasting shrub density and tree canopy in Experiment 2: No Trees - Low Shrub biomass (NTLS), No Trees - High Shrub biomass (NTHS), Present Trees - Low Shrub biomass (PTLS) and Present Trees - High shrub biomass (PTHS).Table 4 and Table S5a_seedling performance_Exp 2Table 4: Seedling performance in hummocks with contrasting shrub density and tree canopy in Experiment 2: No Trees - Low Shrub biomass (NTLS), No Trees - High Shrub biomass (NTHS), Present Trees - Low Shrub biomass (PTLS) and Present Trees - High shrub biomass (PTHS). Seedling emergence, condition and survival from seeds inserted below the moss (SB), and from small planted seedlings.Table S3_cox regression (survival analysis)_Exp 1Table S3: Data for Cox survival analysis for experimental pine seedlings in hummocks (circular and bands) versus adjacent lawns during 2010-2013. ST: Seedlings from seeds inserted on top of moss; SB: Seedlings from seeds inserted below moss; Small seedling (1 month old, 10 cm tall at plantation time); Large seedling (2 months old, 30 cm tall at plantation time).Table S4_ regression seedling-environment 2011_Exp 1Table S4: Data for generalized linear models assessing the responses of experimental pine seedlings in hummocks (circular and bands) and adjacent lawns for Experiment 1 in 2011. Small seedling (1 month old, 10 cm tall at plantation time); Large seedling (2 months old, 30 cm tall at plantation time). Condition = % healthy seedlings. Growth = stem growth. Boreal ecosystems are warming roughly twice as fast as the global average, resulting in woody expansion that could further speed up the climate warming. Boreal peatbogs are waterlogged systems that store more than 30% of the global soil carbon. Facilitative effects of shrubs and trees on the establishment of new individuals could increase tree cover with profound consequences for the structure and functioning of boreal peatbogs, carbon sequestration and climate. We conducted two field experiments in boreal peatbogs to assess the mechanisms that explain tree seedling recruitment and to estimate the strength of positive feedbacks between shrubs and trees. We planted seeds and seedlings of Pinus sylvestris in microsites with contrasting water-tables and woody cover and manipulated both shrub canopy and root competition. We monitored seedling emergence, growth and survival for up to four growing seasons and assessed how seedling responses related to abiotic and biotic conditions. We found that tree recruitment is more successful in drier topographical microsites with deeper water-tables. On these hummocks, shrubs have both positive and negative effects on tree seedling establishment. Shrub cover improved tree seedling condition, growth and survival during the warmest growing season. In turn, higher tree basal area correlates positively with soil nutrient availability, shrub biomass and abundance of tree juveniles. Synthesis. Our results suggest that shrubs facilitate tree colonization of peatbogs which further increases shrub growth. These facilitative effects seem to be stronger under warmer conditions suggesting that a higher frequency of warmer and dry summers may lead to stronger positive interactions between shrubs and trees that could eventually facilitate a shift from moss to tree-dominated systems.