Evidence map›Paper›PMID 39664720›Full record

ArticleEcology and evolution2024

The Environmental Light Characteristics of Forest Under Different Logging Regimes.

Bernard W T Coetzee, Layla van Zyl

Abstract read
In one paragraph

Article in Ecology and evolution, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

2 authors.

Bernard W T CoetzeeConservation Ecology Research Unit, Department of Zoology and Entomology University of Pretoria Pretoria South Africa.ORCID https://orcid.org/0000-0002-9913-6295
Layla van ZylConservation Ecology Research Unit, Department of Zoology and Entomology University of Pretoria Pretoria South Africa.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Light is a fundamental attribute and key abiotic driver in forest ecosystems. Although the ecological effects of light itself is well studied, capturing the complex parameters that constitute the whole light environment remain an intricate research endeavor. Here, we apply the newly introduced environmental light field (ELF) technique in Kibale National Park, Uganda. We captured whole light scenes with repeat photography and processed it to measure both the spectral composition of light in the red-green-blue range, as well as its variation, or "contrast-span", using the newly introduced International System of Units (SI); "lit". We compare across major and globally common utilized forest types-primary, secondary, and selectively logged areas, as well as a completely cleared area as a control. We find that the ELF system is able to effectively capture key aspects of the local light environment across the range of forest types. The distribution of light intensity and its spectral composition across our study is hardly uniform, with primary forest and a clearing showing two orders of magnitude difference in light. Blue light predominates the sky areas of the clearing, indicating the Rayleigh scattering of sunlight in the atmosphere. In general, radiance decrease with increasing intactness of the forest, and selectively logged and primary forest show the most similar environmental light characteristics. Owing to its ability to capture fine scale variations in light across elevation gradients, their spectral characteristics, as well as their intensities, the ELF system should become a useful tool in better quantifying light in ecology. In particular, we discuss its potential use in restoration ecology.

Indexed as

ecologyplant traitsradiancerestoration ecology

Identifiers

PMID39664720
PMCPMC11631710

What OpenQuestion holds

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Registered trials

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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.