Quantifying coral reef carbonate budgets: a comparison between ReefBudget and CoralNet

被引:0
作者
Pilly, Sivajyodee Sannassy [1 ,2 ]
Townsend, Joseph E. [3 ]
Alisa, Cut Aja Gita [4 ]
Razak, Tries B. [4 ,5 ]
Roche, Ronan C. [2 ]
Turner, John R. [2 ]
Chan, Stephen [6 ]
Kriegman, David J. [6 ]
Andersson, Andreas J. [7 ]
Perry, Chris T. [1 ]
Lange, Ines D. [1 ]
Courtney, Travis A. [3 ]
机构
[1] Univ Exeter, Fac Environm Sci & Econ, Geog, Exeter EX4 4RJ, England
[2] Bangor Univ, Sch Ocean Sci, Bangor, Wales
[3] Univ Puerto R Mayaguez, Dept Marine Sci, Mayaguez, PR 00680 USA
[4] IPB Univ, Fac Fisheries & Marine Sci, Dept Marine Sci & Technol, Bogor, Indonesia
[5] IPB Univ, Fac Fisheries & Marine Sci, Sch Coral Reef Restorat SCORES, Dept Marine Sci & Technol, Bogor, Indonesia
[6] Univ Calif San Diego, Dept Comp Sci & Engn, La Jolla, CA USA
[7] Univ Calif San Diego, Scripps Inst Oceanog, La Jolla, CA USA
关键词
Reef carbonate budgets; ReefBudget; CoralNet; In-water survey methods; Image-based approaches; FRAMEWORK; IMPACTS;
D O I
10.1007/s00338-025-02620-1
中图分类号
Q17 [水生生物学];
学科分类号
071004 ;
摘要
Calcium carbonate production constitutes one of the core processes that drive coral reef ecosystem functioning and can be assessed using in-water or image-based survey methods, which have not previously been compared. This study compares carbonate production estimates from in-water ReefBudget surveys and image-based CoralNet analyses in Puerto Rico, Indonesia, and Chagos Archipelago. Methods were compared for different regions (Western Atlantic and Indo-Pacific), reef settings (low and high coral cover), CoralNet calcification versions (v1 and v2), and input metrics (regional vs. local coral growth rates). We show similar gross carbonate production estimates between methods, indicating that area-normalised scaling of calcification rates and assumptions about colony size and rugosity employed in CoralNet produce comparable estimates to ReefBudget surveys. Divergences in carbonate production estimates are potentially driven by differences in survey methods (reef contour measurements vs. planar imagery) and survey effort, which affect calcifier cover estimates, particularly at low coral cover sites. Local versus regional growth rate comparisons suggest site-specific factors can influence accuracy more than method choice. Our findings suggest that image-based methods can allow rapid reef-scale calcification estimates from photo or video imagery. These methods, combined with machine learning substrate classification algorithms, can estimate both benthic cover and carbonate production over larger reef areas and can be applied to historically collect benthic cover data to track carbonate production trends. We encourage researchers to recognise situation-specific differences in methodologies and select the one most suitable for their specific study site, required level of accuracy, and time constraints for fieldwork and image analysis.
引用
收藏
页码:513 / 527
页数:15
相关论文
共 67 条
  • [1] Comparison of Standard Caribbean Coral Reef Monitoring Protocols and Underwater Digital Photogrammetry to Characterize Hard Coral Species Composition, Abundance and Cover
    Barrera-Falcon, Erick
    Rioja-Nieto, Rodolfo
    Hernandez-Landa, Roberto C.
    Torres-Irineo, Edgar
    [J]. FRONTIERS IN MARINE SCIENCE, 2021, 8
  • [2] The global flood protection savings provided by coral reefs
    Beck, Michael W.
    Losada, Inigo J.
    Menendez, Pelayo
    Reguero, Borja G.
    Diaz-Simal, Pedro
    Fernandez, Felipe
    [J]. NATURE COMMUNICATIONS, 2018, 9
  • [3] Towards Automated Annotation of Benthic Survey Images: Variability of Human Experts and Operational Modes of Automation
    Beijbom, Oscar
    Edmunds, Peter J.
    Roelfsema, Chris
    Smith, Jennifer
    Kline, David I.
    Neal, Benjamin P.
    Dunlap, Matthew J.
    Moriarty, Vincent
    Fan, Tung-Yung
    Tan, Chih-Jui
    Chan, Stephen
    Treibitz, Tali
    Gamst, Anthony
    Mitchell, B. Greg
    Kriegman, David
    [J]. PLOS ONE, 2015, 10 (07):
  • [4] The meaning of the term 'function' in ecology: A coral reef perspective
    Bellwood, David R.
    Streit, Robert P.
    Brandl, Simon J.
    Tebbett, Sterling B.
    [J]. FUNCTIONAL ECOLOGY, 2019, 33 (06) : 948 - 961
  • [5] Bland J.M., 1999, Measuring agreement in method comparison studies, DOI DOI 10.1177/096228029900800204
  • [6] Coral reef ecosystem functioning: eight core processes and the role of biodiversity
    Brandl, Simon J.
    Rasher, Douglas B.
    Cote, Isabelle M.
    Casey, Jordan M.
    Darling, Emily S.
    Lefchecku, Jonathan S.
    Duffy, J. Emmett
    [J]. FRONTIERS IN ECOLOGY AND THE ENVIRONMENT, 2019, 17 (08) : 445 - 453
  • [7] PREDICTING RESPONSES OF GEO-ECOLOGICAL CARBONATE REEF SYSTEMS TO CLIMATE CHANGE: A CONCEPTUAL MODEL AND REVIEW
    Browne, Nicola K.
    Cuttler, Michael
    Moon, Katie
    Morgan, Kyle
    Ross, Claire L.
    Castro-Sanguino, Carolina
    Kennedy, Emma
    Harris, Dan
    Barnes, Peter
    Bauman, Andrew
    Beetham, Eddie
    Bonesso, Joshua
    Bozec, Yves-Marie
    Cornwall, Christopher
    Dee, Shannon
    Decarlo, Thomas
    D'Olivo, Juan P.
    Doropoulos, Christopher
    Evans, Richard D.
    Eyre, Bradley
    Gatenby, Peter
    Gonzalez, Manuel
    Hamylton, Sarah
    Hansen, Jeff
    Lowe, Ryan
    Mallela, Jennie
    O'Leary, Michael
    Roff, George
    Saunders, Benjamin J.
    Zweilfer, Adi
    [J]. OCEANOGRAPHY AND MARINE BIOLOGY: AN ANNUAL REVIEW, VOL 59, 2021, 59 : 229 - 370
  • [8] Comparison of two photographic methodologies for collecting and analyzing the condition of coral reef ecosystems
    Bryant, D. E. P.
    Rodriguez-Ramirez, A.
    Phinn, S.
    Gonzalez-Rivero, M.
    Brown, K. T.
    Neal, B. P.
    Hoegh-Guldberg, O.
    Dove, S.
    [J]. ECOSPHERE, 2017, 8 (10):
  • [9] Machine-Learning for Mapping and Monitoring Shallow Coral Reef Habitats
    Burns, Christopher
    Bollard, Barbara
    Narayanan, Ajit
    [J]. REMOTE SENSING, 2022, 14 (11)
  • [10] Caldwell A.R., 2022, J Open Source Software, V7, P4148, DOI [10.21105/joss.04148, DOI 10.21105/JOSS.04148]