Evidence map›Paper›PMID 35342569›Full record

ArticleEcology and evolution2022

SizeExtractR: A workflow for rapid reproducible extraction of object size metrics from scaled images.

Liam Lachs, Fiona Chong, Maria Beger, Holly K East, James R Guest, Brigitte Sommer

Abstract read
In one paragraph

Article in Ecology and evolution, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing 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

6 citing papers in PubMed.

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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

6 authors.

Liam LachsSchool of Natural & Environmental Sciences Newcastle University Newcastle upon Tyne UK.ORCID https://orcid.org/0000-0003-3712-6144
Fiona ChongDepartment of Biological & Marine Sciences University of Hull Hull UK.ORCID https://orcid.org/0000-0002-8921-4878
Maria BegerSchool of Biology Faculty of Biological Sciences University of Leeds Leeds UK.ORCID https://orcid.org/0000-0003-1363-3571
Holly K EastDepartment of Geography and Environmental Sciences Northumbria University Newcastle upon Tyne UK.ORCID https://orcid.org/0000-0002-0392-639X
James R GuestSchool of Natural & Environmental Sciences Newcastle University Newcastle upon Tyne UK.ORCID https://orcid.org/0000-0002-9714-9009
Brigitte SommerSchool of Life Sciences University of Technology Sydney Sydney New South Wales Australia.ORCID https://orcid.org/0000-0003-0617-7790

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Size is a biological characteristic that drives ecological processes from microscopic to geographic spatial scales, influencing cellular energetics, species fitness, population dynamics, and ecological interactions. Methods to measure size from images (e.g., proxies of body size, leaf area, and cell area) occur along a gradient from manual approaches to fully automated technologies (e.g., machine learning). These methods differ in terms of time investment, expertise required, and data or resource availability. While manual methods can improve accuracy through human recognition, they can be labor intensive, highlighting the need for semi-automated, and user-friendly software or workflows to increase the efficiency of manual techniques.Here, we present SizeExtractR, an open-source workflow that enables faster extraction of size metrics from scaled images (e.g., each image includes a ruler) using semi-automated protocols. It comprises a set of ImageJ macros to speed up size extraction and annotation, and an R-package for the quality control of annotations, data collation, calibration, and visualization.SizeExtractR extracts seven common size dimensions, including planar area, min/max diameter, and perimeter. Users can record additional categorical variables relating to their own study, for example species ID, by simply adding alphanumeric annotations to individual objects when prompted. Using a population size structure case study for hard corals as an example, we show how SizeExtractR was used to quantify the impact of mass coral bleaching on coral population dynamics. Lastly, the time saving benefit of using SizeExtractR was quantified during a series of timed image analyses, revealing up to a 49% reduction in image analysis time compared to a fully manual approach.SizeExtractR automatically archives results, allowing re-analysis of size extraction and promoting quality control and reproducibility. It has already been employed in marine and terrestrial sciences to assess population dynamics and demography, energy investment in eggs, and growth of nursery reared corals, with potential to be applied to a wide range of other research fields.

Indexed as

coral reefsimage analysisobject dimensionspopulation dynamicsreproducibilitysize frequency distributionssize metricstime saving

Identifiers

PMID35342569
PMCPMC8928875

What OpenQuestion holds

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LicenceCC BY
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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.