Evidence map›Paper›PMID 39246247›Full record

ArticleTree physiology2024

Extreme precipitation reduces the recent photosynthetic carbon isotope signal detected in ecosystem respiration in an old-growth temperate forest.

Haoyu Diao, Jiabing Wu

Abstract read
In one paragraph

Article in Tree physiology, 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

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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

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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.

Haoyu DiaoForest Dynamics, Swiss Federal Institute for Forest, Snow and Landscape Research WSL, Zürcherstrasse 111, 8903 Birmensdorf, Switzerland.ORCID 0000-0002-8404-1998
Jiabing WuCAS Key Laboratory of Forest Ecology and Management, Institute of Applied Ecology, Chinese Academy of Sciences, Wenhua Road 72, 110016 Shenyang, China.ORCID 0000-0002-9448-5779

Funding

National Natural Science Foundation of China 32271873Velux Stiftung 1733
6 · The paper itself

Abstract

The successful utilization of stable carbon isotope approaches in investigating forest carbon dynamics has relied on the assumption that the carbon isotope compositions (δ13C) therein have detectable temporal variations. However, interpreting the δ13C signal transfer can be challenging, given the complexities involved in disentangling the effect of a single environmental factor, the isotopic dilution effect from background CO2 and the lack of high-resolution δ13C measurements. In this study, we conducted continuous in situ monitoring of atmospheric CO2 (δ13Ca) across a canopy profile in an old-growth temperate forest in northeast China during the normal year 2020 and the wet year 2021. Both years exhibited similar temperature conditions in terms of both seasonal variations and annual averages. We tracked the natural carbon isotope composition from δ13Ca to photosynthate (δ13Cp) and to ecosystem respiration (δ13CReco). We observed significant differences in δ13Ca between the two years. Contrary to in 2020, in 2021 there was a δ13Ca valley in the middle of the growing season, attributed to surges in soil CO2 efflux induced by precipitation, while in 2020 values peaked during that period. Despite substantial and similar seasonal variations in canopy photosynthetic discrimination (Δ13Ccanopy) in the two years, the variability of δ13Cp in 2021 was significantly lower than in 2020, due to corresponding differences in δ13Ca. Furthermore, unlike in 2020, we found almost no changes in δ13CReco in 2021, which we ascribed to the imprint of the δ13Cp signal on above-ground respiration and, more importantly, to the contribution of stable δ13C signals from soil heterotrophic respired CO2. Our findings suggest that extreme precipitation can impede the detectability of recent photosynthetic δ13C signals in ecosystem respiration in forests, thus complicating the interpretation of above- and below-ground carbon linkage using δ13CReco. This study provides new insights for unravelling precipitation-related variations in forest carbon dynamics using stable isotope techniques.

Indexed as

Carbon IsotopesForestsPhotosynthesisRainCarbon DioxideChinaEcosystemSeasonsTreesCarbon DioxideCarbon IsotopesBirch effectgross primary productionrainfallsoil respirationstable isotope

Identifiers

PMID39246247
PMCPMC11469762

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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.