Evidence map›Paper›PMID 41177555›Full record

ArticleEcological applications : a publication of the Ecological Society of America2025

Carbon costs of different pathways for reducing fire hazard in the Sierra Nevada.

Yihong Zhu, Daniel E Foster, Brandon M Collins, Scott L Stephens, Robert A York, Ariel T Roughton, Emily E Y Moghaddas, John E Sanders, John J Battles

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Ecological applications : a publication of the Ecological Society of America, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Prescribed fire is unlikely to reduce net PMProceedings of the National Academy of Sciences of the United States of America · 2026
    Article
  2. Carbon costs of different pathways for reducing fire hazard in the Sierra Nevada.Ecological applications : a publication of the Ecological Society of America · 2025
    Article
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

9 authors.

Yihong ZhuDepartment of Environmental Science, Policy, and Management, University of California, Berkeley, Berkeley, California, USA.ORCID https://orcid.org/0000-0001-6937-9467
Daniel E FosterDepartment of Environmental Science, Policy, and Management, University of California, Berkeley, Berkeley, California, USA.
Brandon M CollinsDepartment of Environmental Science, Policy, and Management, University of California, Berkeley, Berkeley, California, USA.
Scott L StephensDepartment of Environmental Science, Policy, and Management, University of California, Berkeley, Berkeley, California, USA.
Robert A YorkDepartment of Environmental Science, Policy, and Management, University of California, Berkeley, Berkeley, California, USA.
Ariel T RoughtonBerkeley Forests, University of California, Berkeley, Berkeley, California, USA.
Emily E Y MoghaddasUSDA Forest Service, Pacific Southwest Region, Vallejo, California, USA.
John E SandersDepartment of Environmental Science, Policy, and Management, University of California, Berkeley, Berkeley, California, USA.
John J BattlesDepartment of Environmental Science, Policy, and Management, University of California, Berkeley, Berkeley, California, USA.ORCID https://orcid.org/0000-0001-7124-7893

Funding

CalFire Forest Health Research Program
6 · The paper itself

Abstract

Restoring a low-intensity, frequent-fire regime in fire-prone forests offers a promising natural climate solution. Management interventions that include prescribed fire and/or mechanical treatments have effectively reduced fire hazards in the Western United States, yet concerns remain regarding their impact on forest carbon storage. This study used results from a long-term, replicated field experiment to assess the impacts of a restored disturbance regime on carbon dynamics in a Sierra Nevada, mixed conifer forest. The carbon consequences of the treatments were compared to a dynamic baseline of untreated controls (Control). After 19 years of wildfire mitigation, all treated stands stored less carbon than Control, but a larger proportion was sequestered in wildfire-resistant pools (i.e., large trees or fire-resistant species). Notably, only the most intensive treatment regime-thinning, mastication, and prescribed fire (Mech+Fire)-became a net carbon source by Year 20 (-60 MgC/ha). Annual average net ecosystem productivity (NEP) in Control and prescribed fire-only (Fire, 5.6-5.8 MgC/ha/year) more than doubled that of the mechanical treatments (2.0-2.1 MgC/ha/year). Moreover, temporal trends diverged. By the 3rd post-fire interval, the live vegetation carbon accumulation stalled in Control (0.9 ± 1.0 MgC/ha/year, mean ± SE) and accelerated in Fire (6.6 ± 1.2 MgC/ha/year). In contrast, surface fuel recovery was initially faster in Fire but slowed significantly by the 3rd interval, suggesting that the increased productivity under a frequent-fire regime does not necessarily lead to rapid surface fuel buildup once the regime is established. A simulated wildfire in Year 20 killed 6×-16× more live tree carbon in Control (46% mortality). Still, Control maintained the highest post-fire carbon storage. Despite the inherent carbon costs of wildfire mitigation, our 20-year study highlights management pathways that minimize the trade-off between wildfire hazard and carbon storage in Sierra Nevada mixed conifer forests.

Indexed as

CarbonConservation of Natural ResourcesFiresForestryForestsWildfiresCaliforniaTime FactorsCarbonCalifornia conifer forestcarbon dynamicsfrequent‐fire regimenet ecosystem productivityprescribed firewildfire mitigation

Identifiers

PMID41177555
PMCPMC12580073

What OpenQuestion holds

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LicenceCC BY
Read underepoch 390

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.