ArticleDiversity2019
Assessing Ecological Risks from Atmospheric Deposition of Nitrogen and Sulfur to US Forests Using Epiphytic Macrolichens.
Article in Diversity, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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
12 citing papers in PubMed, 40 citations in OpenAlex.
- Review
- Critical load exceedances for North America and Europe using an ensemble of models and an investigation of causes of environmental impact estimate variability: an AQMEII4 study.Atmospheric chemistry and physics · 2025Article
- Developing spatially explicit critical loads for herbaceous species across the United States using convex hulls.Ecological applications : a publication of the Ecological Society of America · 2025Article
- Characterizing localized nitrogen sensitivity of tree species and the associated influences of mediating factors.Ecosphere (Washington, D.C) · 2024Article
- Sensitive tree species remain at risk despite improved air quality benefits to US forests.Nature sustainability · 2023Article
- Standardized naming convention and classification system for critical loads of nitrogen and sulfur deposition.Ecosphere (Washington, D.C) · 2023Article
- Empirical nitrogen and sulfur critical loads of U.S. tree species and their uncertainties with machine learning.The Science of the total environment · 2023Article
- Multiple drivers of large-scale lichen decline in boreal forest canopies.Global change biology · 2022Article
- Patagonian sheepdog: Genomic analyses trace the footprints of extinct UK herding dogs to South America.PLoS genetics · 2022Article
- Article
- Functional Traits in Lichen Ecology: A Review of Challenge and Opportunity.Microorganisms · 2021Review
- Aspects of uncertainty in total reactive nitrogen deposition estimates for North American critical load applications.The Science of the total environment · 2019Article
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 at 5 institutions in 1 country.
Funding
Abstract
Critical loads of atmospheric deposition help decision-makers identify levels of air pollution harmful to ecosystem components. But when critical loads are exceeded, how can the accompanying ecological risk be quantified? We use a 90% quantile regression to model relationships between nitrogen and sulfur deposition and epiphytic macrolichens, focusing on responses of concern to managers of US forests: Species richness and abundance and diversity of functional groups with integral ecological roles. Analyses utilized national-scale lichen survey data, sensitivity ratings, and modeled deposition and climate data. We propose 20, 50, and 80% declines in these responses as cut-offs for low, moderate, and high ecological risk from deposition. Critical loads (low risk cut-off) for total species richness, sensitive species richness, forage lichen abundance and cyanolichen abundance, respectively, were 3.5, 3.1, 1.9, and 1.3 kg N and 6.0, 2.5, 2.6, and 2.3 kg S ha
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Identifiers
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.