ArticleEcology letters2026
A New Framework to Empower Ecosystem Assessment Through the Integration of eDNA Inventories, Graph Theory and Niche Modelling.
Article in Ecology letters, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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
1 citing paper in PubMed.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
Rethinking the ecological significance of network topology in the area of environmental DNA (eDNA) data represents a major challenge in ecology, as network structure fundamentally influences ecosystem functioning. However, translating network analysis into tools for ecological assessment is still in its infancy. Here, we present an innovative approach integrating eDNA inventories with graph theory (specifically graphlet analysis), niche modelling, and machine learning to build a predictive model for ecological assessment. We validated our approach using 599 phytoplankton inventories collected from 186 lakes spanning a gradient of human-induced eutrophication. We demonstrated the effectiveness of graphlet-based network topological metrics for ecological assessment (explaining 67% of the anthropogenic gradient variability), as well as their link with ecological processes. This framework offers a scalable and transposable approach to a wide range of ecological situations to obtain assessment tools grounded on graphlet-based network topology, paving the way for a more comprehensive understanding of ecosystem functioning.
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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.