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Predicting 3D and 2D surface area of corals from simple field measurements.

ABSTRACT: The structural architecture of coral reefs is a known predictor of species richness, fish biomass and reef resilience. At a smaller scale, three-dimensional (3D) surface area of corals is a fundamental determinant of physical and biological processes. Quantifying the 3D surface area of corals has applications for a broad range of scientific disciplines, including carbonate production estimates, coral predation studies, and assessments of reef growth. Here, we present morphotaxon-specific conversion metrics to estimate total 3D surface area and projected 2D surface area of individual colonies from simple field measurements of colony maximum diameter. Underwater photogrammetry techniques were used to quantify surface area and estimate conversion metrics. Bayesian models showed strong non-linear (power) relationships between colony maximum diameter and both total 3D surface area and projected 2D surface area for 13 out of 15 morphotaxa. This study presents a highly resolved and efficient method for obtaining critical surface area assessments of corals for various applications, including assessments of biotic surface area, tissue biomass, calcification rates, coral demographic rates, and reef restoration monitoring.

SUBMITTER: Chandler JF

PROVIDER: S-EPMC11375061 | biostudies-literature | 2024 Sep

REPOSITORIES: biostudies-literature

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Predicting 3D and 2D surface area of corals from simple field measurements.

Chandler Josie F JF Figueira Will F WF Burn Deborah D Doll Peter C PC Johandes Abby A Piccaluga Agustina A Pratchett Morgan S MS

Scientific reports 20240904 1

The structural architecture of coral reefs is a known predictor of species richness, fish biomass and reef resilience. At a smaller scale, three-dimensional (3D) surface area of corals is a fundamental determinant of physical and biological processes. Quantifying the 3D surface area of corals has applications for a broad range of scientific disciplines, including carbonate production estimates, coral predation studies, and assessments of reef growth. Here, we present morphotaxon-specific convers ...[more]

PMID: 39232103

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Project description:PurposeTotal and regional body composition are important indicators of health and mortality risk, but their measurement is usually restricted to controlled environments in clinical settings with expensive and specialized equipment. A method that approaches the accuracy of the current gold standard method, dual-energy x-ray absorptiometry (DXA), while only requiring input from widely available consumer grade equipment, would enable the measurement of these important biometrics in the wild, enabling data collection at a scale that would have previously been prohibitive in time and expense. We describe an algorithm for predicting three-dimensional (3D) body shape and composition from a single frontal 2-dimensional image acquired with a digital consumer camera.MethodsDuplicate 3D optical scans, two-dimensional (2D) optical images, and DXA whole-body scans were available for 183 men and 233 women from the Shape Up! Adults Study. A principal component analysis vector basis was fit to 3D point clouds of a training subset of 152 men and 194 women. The relationship between this vector space and DXA-derived body composition was modeled with linear regression. The principal component 3D shape was then fitted to match a silhouette extracted from a 2D photograph of a novel body. Body composition was predicted from the resulting 3D shape match using the linear mapping between the principal component parameters and the DXA metrics. Accuracy of body composition estimates from the silhouette method was evaluated against a simple model using height and weight as a baseline, and against DXA measurements as ground truth. Test-retest precision of the silhouette method was evaluated using the duplicate 2D optical images and compared against precision of the duplicate DXA scans. Paired t-tests were performed to detect significant differences between the sets.ResultsResults were reported on a held-out set. Body composition prediction achieved R2 s of 0.81 and 0.74 for percent fat prediction of males and females, respectively, on a held-out test set consisting of 31 males and 39 females. Precision estimates for fat mass were 2.31% and 2.06% for males and females, respectively, compared to 1.26% and 0.68% for DXA scans. The t-tests revealed no statistically significant differences between the silhouette method measurements and DXA measurements, or between retests.ConclusionTotal and regional body composition measures can be estimated from a single frontal photograph of a human body. Body composition prediction using consumer level photography can enable early screening and monitoring of possible physiological indicators of metabolic disease in regions where medical imagery or clinical assessment is inaccessible.

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Predicting 3D and 2D surface area of corals from simple field measurements.

Publications

Predicting 3D and 2D surface area of corals from simple field measurements.

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