Grunwald, L. | bioRxiv Science

bioRxiv2026

Small stream restoration increases habitat and plant diversity across scales

A majority of stream restoration efforts in central Europe focus on streams that are less than five meters wide. Restoration aims to increase structural complexity, thereby enhancing habitat heterogeneity, promoting biodiversity, and reestablishing aquatic-terrestrial linkages that can drive responses in adjacent terrestrial communities. However, the effects of small stream restoration on terrestrial biodiversity remain poorly understood because research focuses mainly on large rivers. Here, we investigated the effects of restoration on the structural complexity of the stream channel as well as the terrestrial habitat and plant diversity on a local and landscape scale across 55 small streams in an agricultural landscape. We compared restored stream sections with non-restored ones that were similar to the conditions before restoration. We also assessed how restored sections changed over time since restoration. Restored stream sections showed a higher stream structural complexity and habitat diversity, both of which are targets of active restoration measures. Restoration also increased riparian plant diversity, both directly and indirectly through structural complexity and habitat diversity. Although time since restoration did not influence structural complexity, it drove successional changes in plant communities that became increasingly associated with wetland habitat conditions. Our results demonstrate that small stream restoration effectively increases floodplain habitat and plant diversity in agricultural landscapes, primarily by enhancing water availability in the floodplain. Restoration actions on small streams support biodiversity if they improve stream channel complexity, connect the stream with its floodplain, and create floodplain habitats.

ecology↗

bioRxiv2025

Constrained template matching using rejection sampling

Identifying macromolecular complexes in situ using cryo-electron tomography remains challenging, with low signal-to-noise ratios, the missing wedge, and crowded backgrounds among the key limiting factors. By integrating prior knowledge on macromolecular localization, such as the preferred orientations of membrane-associated proteins, detection can be improved by constraining searches to biologically feasible orientations. Here we describe rejection sampling, an approach for integrating such constraints at voxel resolution that remains both accurate and computationally efficient. Using synthetic and experimental data, we show that these constraints improve detection, orientational assignment, and discrimination between macromolecules. The resulting picks match the performance of deep-learning methods informed by membrane structure without requiring annotated data. We further apply rejection sampling to ATP synthase on mitochondrial cristae, illustrating how it extends macromolecular detection to the large and highly curved membrane systems that pervade cells.

biophysics↗