Evidence map›Paper›PMID 39164598›Full record

ArticleEnvironmental management2024

Riparian Landscape Change: A Spatial Approach for Quantifying Change and Development of a River Network Restoration Model.

Martin Stieger, Paul Mckenzie

Abstract read
In one paragraph

Article in Environmental management, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Martin StiegerSchool of Geography and Environmental Sciences, Ulster University, Cromore Road, Coleraine Northern Ireland, BT52 1SA, Coleraine, UK.
Paul MckenzieSchool of Geography and Environmental Sciences, Ulster University, Cromore Road, Coleraine Northern Ireland, BT52 1SA, Coleraine, UK. sjp.mckenzie@ulster.ac.uk.ORCID 0000-0003-2117-8724

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Natural river landscapes can be biodiversity hotspots but are one of the most human altered ecosystems with habitats significantly damaged around the world, and a third of fish populations threatened with extinction. While riparian ecosystems have been negatively altered by anthropogenic activities, effective planning and restoration strategies can reverse negative impacts by improving habitat quality. However, restoring rivers requires appropriate data on current riparian health while also considering priorities for different stakeholders. To address this, a Geographic Information System (GIS) was used to create a new and transferable restoration priority model based on a section of the river Linth in Switzerland as a case study. The restoration priority model is founded on connectivity, river condition, national priority species and species hotspots. Landscape change of the riparian zone was analyzed using aerial imagery and landscape metrics. Almost a quarter of rivers within the study area were considered high or very high restoration priority, with many aquatic species set to benefit from restoration. From 1946 to 2019, the riparian landscape became highly fragmented due to significant growth in impervious surfaces and a concomitant loss of agricultural land. The GIS model provides a tool by which environmental agencies can manage natural features over large scales, while also planning priorities and targeting conservation strategies to the areas of greatest need.

Indexed as

BiodiversityConservation of Natural ResourcesEcosystemGeographic Information SystemsRiversAnimalsFishesModels, TheoreticalSwitzerlandGISLandscape MetricsPlanningRestoration Priority ModelRiparian ZoneRiver Restoration

Identifiers

PMID39164598
PMCPMC11438710

What OpenQuestion holds

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LicenceCC BY
Read underepoch 390

Registered trials

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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.