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QuestionDisturbed areas offer great opportunities for restoring native biodiversity, but they are also prone to invasion by alien plants. Following the limiting similarity hypothesis, we address the question of whether or not similarity of plant functional traits helps developing seed mixtures of native communities with high resistance to invasive species at an early stage of restoration. LocationCentre of Greenhouses and Laboratories Durnast, Technische Universitat Munchen, Freising, Germany. MethodsUsing a system of linear equations, we designed native communities maximizing the similarity between the native and two invasive species according to ten functional traits. We used native grassland plants, two invasive alien species that are often problematic in disturbed areas (i.e., Ambrosia artemisiifolia and Solidago gigantea) and trait information obtained from databases. The two communities were then tested for resistance against establishment of the two invaders separately in a greenhouse experiment. We measured height of the invasive species and above-ground biomass, along with leaf area index, 4 and 8months after sowing respectively. ResultsBoth invasive species were successfully reduced by the native community designed to suppress S. gigantea dominated by small-seeded species. These results could be considered as partial support for the limiting similarity hypothesis. However, given the success of this mixture against both invasive species, suppression was better explained by a seed density effect resulting from the smaller seed mass of the native species included in this mixture. Further, the dominance of a fast-developing competitive species could also contribute to its success. ConclusionsThere was no unequivocal support for the limiting similarity hypothesis in terms of the traits selected. Instead we found that increasing seeding density of native species and selecting species with a fast vegetative development is an effective way to suppress invasive plants during early stages of restoration. If limiting similarity is used to design communities for restoration, early life-history traits should be taken into account.
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.