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- Biurrun, Idoia, et al.
(författare)
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Benchmarking plant diversity of Palaearctic grasslands and other open habitats
- 2021
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Ingår i: Journal of Vegetation Science. - Oxford : John Wiley & Sons. - 1100-9233 .- 1654-1103. ; 32:4
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Tidskriftsartikel (refereegranskat)abstract
- Journal of Vegetation Science published by John Wiley & Sons Ltd on behalf of International Association for Vegetation Science.Aims: Understanding fine-grain diversity patterns across large spatial extents is fundamental for macroecological research and biodiversity conservation. Using the GrassPlot database, we provide benchmarks of fine-grain richness values of Palaearctic open habitats for vascular plants, bryophytes, lichens and complete vegetation (i.e., the sum of the former three groups). Location: Palaearctic biogeographic realm. Methods: We used 126,524 plots of eight standard grain sizes from the GrassPlot database: 0.0001, 0.001, 0.01, 0.1, 1, 10, 100 and 1,000 m2 and calculated the mean richness and standard deviations, as well as maximum, minimum, median, and first and third quartiles for each combination of grain size, taxonomic group, biome, region, vegetation type and phytosociological class. Results: Patterns of plant diversity in vegetation types and biomes differ across grain sizes and taxonomic groups. Overall, secondary (mostly semi-natural) grasslands and natural grasslands are the richest vegetation type. The open-access file ”GrassPlot Diversity Benchmarks” and the web tool “GrassPlot Diversity Explorer” are now available online (https://edgg.org/databases/GrasslandDiversityExplorer) and provide more insights into species richness patterns in the Palaearctic open habitats. Conclusions: The GrassPlot Diversity Benchmarks provide high-quality data on species richness in open habitat types across the Palaearctic. These benchmark data can be used in vegetation ecology, macroecology, biodiversity conservation and data quality checking. While the amount of data in the underlying GrassPlot database and their spatial coverage are smaller than in other extensive vegetation-plot databases, species recordings in GrassPlot are on average more complete, making it a valuable complementary data source in macroecology. © 2021 The Authors.
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- Carpio, Antonio J., et al.
(författare)
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Wild boar effects on fungal abundance and guilds from sporocarp sampling in a boreal forest ecosystem
- 2022
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Ingår i: Animals. - : MDPI. - 2076-2615. ; 12:19
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Tidskriftsartikel (refereegranskat)abstract
- Native wild boar (Sus scrofa) populations are expanding across Europe. This is cause for concern in some areas where overabundant populations impact natural ecosystems and adjacent agronomic systems. To better manage the potential for impacts, managers require more information about how the species may affect other organisms. For example, information regarding the effect of wild boar on soil fungi for management application is lacking. Soil fungi play a fundamental role in ecosystems, driving essential ecological functions; acting as mycorrhizal symbionts, sustaining plant nutrition and providing defense; as saprotrophs, regulating the organic matter decomposition; or as plant pathogens, regulating plant fitness and survival. During autumn (Sep–Nov) 2018, we investigated the effects of wild boar (presence/absence and rooting intensity) on the abundance (number of individuals) of fungal sporocarps and their functional guilds (symbiotic, saprotrophic and pathogenic). We selected eleven forested sites (400–500 × 150–200 m) in central Sweden; six with and five without the presence of wild boar. Within each forest, we selected one transect (200 m long), and five plots (2 × 2 m each) for sites without wild boar, and ten plots for sites with boars (five within and five outside wild boar disturbances), to determine the relationship between the intensity of rooting and the abundance of sporocarps for three fungal guilds. We found that the presence of wild boar and rooting intensity were associated with the abundance of sporocarps. Interestingly, this relationship varied depending on the fungal guild analyzed, where wild boar rooting had a positive correlation with saprophytic sporocarps and a negative correlation with symbiotic sporocarps. Pathogenic fungi, in turn, were more abundant in undisturbed plots (no rooting) but located in areas with the presence of wild boar. Our results indicate that wild boar activities can potentially regulate the abundance of fungal sporocarps, with different impacts on fungal guilds. Therefore, wild boar can affect many essential ecosystem functions driven by soil fungi in boreal forests, such as positive effects on energy rotation and in creating mineral availability to plants, which could lead to increased diversity of plants in boreal forests.
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- Garcia, Cesar, et al.
(författare)
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Predicting the impact of adding metakaolin on the splitting strength of concrete using ensemble ML classification and symbolic regression techniques –a comparative study
- 2024
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Ingår i: Frontiers in Built Environment. - : Frontiers. - 2297-3362. ; 10
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Tidskriftsartikel (refereegranskat)abstract
- The mechanical characteristics of concrete are crucial factors in structural design standards especially in concrete technology. Employing reliable prediction models for concrete’s mechanical properties can reduce the number of necessary laboratory trials, checks and experiments to obtain valuable representative design data, thus saving both time and resources. Metakaolin (MK) is commonly utilized as a supplementary replacement for Portland cement in sustainable concrete production due to its technical and environmental benefits towards net-zero goals of the United Nations Sustainable Development Goals (UNSDGs). In this research work, 204 data entries from concrete mixes produced with the addition of metakaolin (MK) were collected and analyzed using eight (8) ensemble machine learning tools and one (1) symbolic regression technique. The application of multiple machine learning protocols such as the ensemble group and the symbolic regression techniques have not been presented in any previous research work on the modeling of splitting tensile strength of MK mixed concrete. The data was partitioned and applied according to standard conditions. Lastly, some selected performance evaluation indices were used to test the models’ accuracy in predicting the splitting strength (Fsp) of the studied MK-mixed concrete. At the end, results show that the k-nearest neighbor (KNN) outperformed the other techniques in the ensemble group with the following indices; SSE of 4% and 1%, MAE of 0.1 and 0.2 MPa, MSE of 0, RMSE of 0.1 and 0.2 MPa, Error of 0.04% and 0.04%, Accuracy of 0.96 and 0.96 and R2 of 0.98 and 0.98 for the training and validation models, respectively. This is followed closely by the support vector machine (SVM) with the following indices; SSE of 7% and 3%, MAE of 0.2 and 0.2 MPa, MSE of 0.0 and 0.1 MPa, RMSE of 0.2 and 0.3 MPa, Error of 0.05% and 0.06%, Accuracy of 0.95 and 0.94, and R2 of 0.96 and 0.95, for the training and validation models, respectively. The third model in the superiority rank is the CN2 with the following performance indices; SSE of 15% and 4%, MAE of 0.2 and 0.2 MPa, MSE of 0.1 and 0.1 MPa, RMSE of 0.3 and 0.3 MPa, Error of 0.08% and 0.07%, Accuracy of 0.92 and 0.93 and R2 of 0.92 and 0.93, for the training and validation models, respectively. These models outperformed the models utilized on the MK-mixed concrete found in the literature, therefore are the better decisive modes for the prediction of the splitting strength (Fsp) of the studied MK-mixed concrete with 204 mix data entries. Conversely, the NB and SGD produced unacceptable model performances, however, this is true for the modeled database collected for the MK-mixed Fsp. The RSM model also produced superior performance with an accuracy of over 95% and adequate precision of more than 27. Overall, the KNN, SVM, CN2 and RSM have shown to possess the potential to predict the MK-mixed Fsp for structural concrete designs and production.
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