Evidence map›Paper›PMID 42707061›Full record

ReviewEcology and evolution2026

Biomonitoring 3.0: Beyond Taxa Lists to Evidence-Tiered Monitoring of Ecosystem Dynamics and Interactions.

Leandro Lofeu, Ehsan Pashay Ahi

Abstract readReview
In one paragraph

Review in Ecology and evolution, 2026. 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.

Leandro LofeuDepartment of Biology University of Sao Paulo Sao Paulo Brazil.ORCID https://orcid.org/0000-0001-8102-7878
Ehsan Pashay AhiOrganismal and Evolutionary Biology Research Programme, Faculty of Biological and Environmental Sciences University of Helsinki Helsinki Finland.ORCID https://orcid.org/0000-0002-6528-1187

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

High-throughput sequencing has transformed biomonitoring by enabling scalable molecular inventories, but most programs still emphasize taxa lists. In this Perspective, Biomonitoring 3.0 is proposed as a framework that builds on molecular inventories to monitor ecological dynamics, interactions, and biological responses through time. Environmental RNA is presented as a complementary source of information because, in some settings, it can provide more temporally local evidence of recent biological activity and responses than DNA alone. An inference ladder is introduced to grade interaction evidence from co-detection and statistical associations to interaction-explicit links, coupled signal-response dynamics and, where feasible, ecosystem-level consequences. Field designs are outlined that use repeated time-series sampling and paired sampling of potential signal sources and recipients to improve temporal interpretation and strengthen attribution. Minimum reporting elements are also recommended to support transparent comparison, validation, and cross-study synthesis. The "3.0" designation therefore refers to a change in the primary monitoring objective: from documenting community membership alone to evaluating ecological dynamics and feedbacks relevant to ecosystem condition and management. We conclude with a practical agenda for developing environmental nucleic-acid measurements into decision-relevant indicators of interactions and change.

Indexed as

biomonitoring 3.0cross‐kingdom interactionsecological networksenvironmental RNAenvironmental transcriptomicsinteraction‐ready monitoringtime‐resolved ecosystem dynamics

Identifiers

PMID42707061
PMCPMC13546864

What OpenQuestion holds

Textmetadata
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Registered trials

None linked

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.