Evidence map›Paper›PMID 33991051›Full record

ArticleIntegrated environmental assessment and management2021

Causal Bayesian networks in assessments of wildfire risks: Opportunities for ecological risk assessment and management.

John F Carriger, Matthew Thompson, Mace G Barron

Abstract read
In one paragraph

Article in Integrated environmental assessment and management, 2021. 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. Article
  2. 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

3 authors.

John F CarrigerOffice of Research and Development, US Environmental Protection Agency, Cincinnati, Ohio, USA.ORCID http://orcid.org/0000-0002-6275-8752
Matthew ThompsonHuman Dimensions Program, USDA Forest Service, Fort Collins, Colorado, USA.
Mace G BarronOffice of Research and Development, US Environmental Protection Agency, Gulf Breeze, Florida, USA.

Funding

Intramural EPA EPA999999
6 · The paper itself

Abstract

Wildfire risks and losses have increased over the last 100 years, associated with population expansion, land use and management practices, and global climate change. While there have been extensive efforts at modeling the probability and severity of wildfires, there have been fewer efforts to examine causal linkages from wildfires to impacts on ecological receptors and critical habitats. Bayesian networks are probabilistic tools for graphing and evaluating causal knowledge and uncertainties in complex systems that have seen only limited application to the quantitative assessment of ecological risks and impacts of wildfires. Here, we explore opportunities for using Bayesian networks for assessing wildfire impacts to ecological systems through levels of causal representation and scenario examination. Ultimately, Bayesian networks may facilitate understanding the factors contributing to ecological impacts, and the prediction and assessment of wildfire risks to ecosystems. Integr Environ Assess Manag 2021;17:1168-1178. Published 2021. This article is a U.S. Government work and is in the public domain in the USA.

Indexed as

WildfiresBayes TheoremClimate ChangeEcosystemRisk AssessmentUnited StatesAssessmentAvian ecologyBayesian belief networksCausal modellingDecision model

Identifiers

PMID33991051
PMCPMC10119872

What OpenQuestion holds

Textmetadata
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.