Refine
Has Fulltext
- no (2)
Document Type
- Article (1)
- Working Paper (1)
Language
- English (2)
Is part of the Bibliography
- yes (2)
Keywords
- Aggregation (1)
- Ecological interactions (1)
- Flagellate grazing (1)
- Microbial carbon transfer (1)
- Polymer degradation (1)
- System ecology (1)
- conomics (1)
- open science (1)
- political science (1)
- replication (1)
Institute
Complex biopolymers (BPs) such as chitin and cellulose provide the majority of organic carbon in aquatic ecosystems, but the mechanisms by which communities of bacteria in natural systems exploit them are unclear. Previous degradation experiments in artificial systems predominantly used microcosms containing a single bacterial species, neglecting effects of interspecific interactions. By constructing simplified aquatic microbial communities, we tested how the addition of other bacterial species, of a nanoflagellate protist capable of consuming bacteria, or of both, affect utilization of BPs. Surprisingly, total abundance of resident bacteria in mixed communities increased upon addition of the protist. Concomitantly, bacteria shifted from free-living to aggregated morphotypes that seemed to promote utilization of BPs. In our model system, these interactions significantly increased productivity in terms of overall bacterial numbers and carbon transfer efficiency. This indicates that interactions on microbial aggregates may be crucial for chitin and cellulose degradation. We therefore suggest that interspecific microbial interactions must be considered when attempting to model the turnover of the vast pool of complex biopolymers in aquatic ecosystems.
This study pushes our understanding of research reliability by reproducing and replicating claims from 110 papers in leading economic and political science journals. The analysis involves computational reproducibility checks and robustness assessments. It reveals several patterns. First, we uncover a high rate of fully computationally reproducible results (over 85%). Second, excluding minor issues like missing packages or broken pathways, we uncover coding errors for about 25% of studies, with some studies containing multiple errors. Third, we test the robustness of the results to 5,511 re-analyses. We find a robustness reproducibility of about 70%. Robustness reproducibility rates are relatively higher for re-analyses that introduce new data and lower for re-analyses that change the sample or the definition of the dependent variable. Fourth, 52% of re-analysis effect size estimates are smaller than the original published estimates and the average statistical significance of a re-analysis is 77% of the original. Lastly, we rely on six teams of researchers working independently to answer eight additional research questions on the determinants of robustness reproducibility. Most teams find a negative relationship between replicators' experience and reproducibility, while finding no relationship between reproducibility and the provision of intermediate or even raw data combined with the necessary cleaning codes.