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NIBIOs ansatte publiserer flere hundre vitenskapelige artikler og forskningsrapporter hvert år. Her finner du referanser og lenker til publikasjoner og andre forsknings- og formidlingsaktiviteter. Samlingen oppdateres løpende med både nytt og historisk materiale. For mer informasjon om NIBIOs publikasjoner, besøk NIBIOs bibliotek.

2024

Sammendrag

Species-rich natural and semi-natural ecosystems are under threat owing to land use change. To conserve the biodiversity associated with these ecosystems, we must identify and target conservation efforts towards functionally important species and supporting habitats that create connections between remnant patches in the landscape. Here, we use a multi-layer network approach to identify species that connect a metanetwork of plant–bee interactions in remnant semi-natural grasslands which are biodiversity hotspots in European landscapes. We investigate how these landscape connecting species, and their interactions, persist in their proposed supporting habitat, road verges, across a landscape with high human impact. We identify 11 plant taxa and nine bee species that connect semi-natural grassland patches. We find the beta diversity of these connector species to be low across road verges, indicating a poor contribution of these habitats to the landscape-scale diversity in semi-natural grasslands. We also find a significant influence of the surrounding landscape on the beta diversity of connector species and their interactions with implications for landscape-scale management. Conservation actions targeted toward species with key functional roles as connectors of fragmented ecosystems can provide cost-effective management of the diversity and functioning of threatened ecosystems.

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Exploring the complex mechanism of anaerobic digestion with hydrothermal pretreatment (HTAD) for biomass efficiently and optimising the reaction conditions are critical to improving the performance of methane production. This study used H2O automated machine learning (AutoML) for comprehensive prediction, analysis, and targeted optimization of the HTAD system. An IterativeImputer system for data filling was constructed. The comparison of three basic regressors showed that random forest performed optimally for filling (R2 > 0.95). The gradient boosting machine (GBM) model was searched by H2O AutoML to show optimal performance in prediction (R2 > 0.96). The software was developed based on the GBM model, and two prediction schemes were devised. The generalization error of the software was less than 10%. The Shapley Additive exPlanations value showed that solid to liquid ratio, hydrothermal pretreatment (HT) temperature, and particle size have greater potential for improving cumulative methane production (CMP). A Bayesian-HTAD optimization strategy was devised, using the Bayesian optimization to directionally optimize the reaction conditions, and performing experiments to validate the results. The experimental results showed that the CMP was significantly improved by 51.63%. Compared to the response surface methodology, the Bayesian optimization relatively achieved a 2.21–2.50 times greater effect. Mechanism analyses targeting the experiments showed that HT was conducive to improving the relative abundance of Sphaerochaeta, Methanosaeta, and Methanosarcina. This research achieved accurate prediction and targeted optimization for the HTAD system and proposed multiple filling, prediction, and optimization strategies, which are expected to provide an AutoML optimization paradigm for anaerobic digestion in the future.