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Local adaptation to different pollinators is considered one of the possible initial stages of ecological speciation as reproductive isolation is a by-product of the divergence in pollination systems. However, pollinator-mediated divergent selection will not necessarily result in complete reproductive isolation, because incipient speciation is often overcome by gene flow. We investigated the potential of pollinator shift in the sexually deceptive orchids Ophrys sphegodes and Ophrys exaltata and compared the levels of floral isolation vs. genetic distance among populations with contrasting predominant pollinators. We analysed floral hydrocarbons as a proxy for floral divergence between populations. Floral adoption of pollinators and their fidelity was tested using pollinator choice experiments. Interpopulation gene flow and population differentiation levels were estimated using AFLP markers. The Tyrrhenian O.sphegodes population preferentially attracted the pollinator bee Andrena bimaculata, whereas the Adriatic O.sphegodes population exclusively attracted A.nigroaenea. Significant differences in scent component proportions were identified in O.sphegodes populations that attracted different preferred pollinators. High interpopulation gene flow was detected, but populations were genetically structured at species level. The high interpopulation gene flow levels independent of preferred pollinators suggest that local adaptation to different pollinators has not (yet) generated detectable genome-wide separation. Alternatively, despite extensive gene flow, few genes underlying floral isolation remain differentiated as a consequence of divergent selection. Different pollination ecotypes in O.sphegodes might represent a local selective response imposed by temporal variation in a geographical mosaic of pollinators as a consequence of the frequent disturbance regimes typical of Ophrys habitats.
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.