ArticleProceedings. Biological sciences2019
Optimizing coastal restoration with the stress gradient hypothesis.
Article in Proceedings. Biological sciences, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
7 citing papers in PubMed.
- Hydro-geomorphological drivers across scales shape the trajectory of coastal wetland restoration.Nature communications · 2026Article
- Optimizing ecosystem restoration with facilitation cascades.Bioscience · 2026Review
- Two sides of the coin: Feedback-driven landscape formation results in trade-off between establishment and resilience of marram grass.Oecologia · 2025Article
- Sediment availability provokes a shift from Brownian to Lévy-like clonal expansion in a dune building grass.Ecology letters · 2021Article
- Facultative mutualisms: A double-edged sword for foundation species in the face of anthropogenic global change.Ecology and evolution · 2021Article
- Toward spatio-temporal delineation of positive interactions in ecology.Ecology and evolution · 2020Review
- Optimizing coastal restoration with the stress gradient hypothesis.Proceedings. Biological sciences · 2019Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Restoration efforts have been escalating worldwide in response to widespread habitat degradation. However, coastal restoration attempts notoriously vary in their ability to establish resilient, high-functioning ecosystems. Conventional restoration attempts disperse transplants in competition-minimizing arrays, yet recent studies suggest that clumping transplants to maximize facilitative interactions may improve restoration success. Here, we modify the stress gradient hypothesis to generate predictions about where each restoration design will perform best across environmental stress gradients. We then test this conceptual model with field experiments manipulating transplant density and configuration across dune elevations and latitudes. In hurricane-damaged Georgia (USA) dunes, grass transplanted in competition-minimizing (low-density, dispersed) arrays exhibited the highest growth, resilience to disturbance and dune formation in low-stress conditions. In contrast, transplants survived best in facilitation-maximizing (high-density, clumped) arrays in high-stress conditions, but these benefits did not translate to higher transplant growth or resilience. In a parallel experiment in Massachusetts where dune grasses experience frequent saltwater inundation, fewer transplants survived, suggesting that there are thresholds above which intraspecific facilitation cannot overcome local stressors. These results suggest that ecological theory can be used to guide restoration strategies based on local stress regimes, maximizing potential restoration success and return-on-investment of future efforts.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.