Restoration success of British floodplain meadows

The Floodplain Meadows Partnership started a national survey (England and Wales) of floodplain-meadow restoration in 2016 by visiting 52 restoration fields. These fields encompassed a wide range of restoration methods and histories, with different degrees of success when evaluated against MG4 grassland as the main target plant community. Of the 52 fields visited, 21 were considered to be progressing well, whilst the remaining 31 had at least one issue that obstructed successful restoration. These issues can be broadly classified as: suboptimal management (39%), excessive nutrient availability (26%), excessive waterlogging (19%) and use of suboptimal propagules (16%). Maintenance of the soil-nutrient balance within the range recommended for the MG4 community should greatly improve the success rate of restoration projects. If nutrient levels on the site are excessive, an early hay cut in June, or double hay cut, should be considered as the most efficient methods for bringing the nutrient balance to the target for the plant community. The survey showed that different species vary greatly in their rate of establishment. Vegetation of MG6, MG7 and MG9 grasslands, according to the National Vegetation Classification, was most widely represented on the restoration sites. MG4 and MG8 plant communities were each recorded in less than 3% of fields

Non-Convex Distributed Optimization

We study distributed non-convex optimization on a time-varying multi-agent network. Each node has access to its own smooth local cost function, and the collective goal is to minimize the sum of these functions. We generalize the results obtained previously to the case of non-convex functions. Under some additional technical assumptions on the gradients we prove the convergence of the distributed push-sum algorithm to some critical point of the objective function. By utilizing perturbations on the update process, we show the almost sure convergence of the perturbed dynamics to a local minimum of the global objective function. Our analysis shows that this noised procedure converges at a rate of $O(1/t)$