Machine Learning Approaches for Classifying and Characterizing Coral Diseases DOI
Emily W. Van Buren, Kelsey M. Beavers,

Mariah N. Cornelio

и другие.

Research Square (Research Square), Год журнала: 2024, Номер unknown

Опубликована: Сен. 19, 2024

Abstract Background: Anthropogenic climate change has had devastating effects on the Florida and Caribbean reef systems, primarily due to increased disease outbreaks. Climate contributes rising frequency of marine diseases by expanding pathogen ranges heightening host susceptibility environmental stress. Specifically, there been a stark rise in events targeting multiple coral species, resulting high mortality rates declining biodiversity. Although many these present similar visual symptoms, they exhibit varying require distinct treatment protocols. Advances transcriptomics research have enhanced our understanding responses different diseases, but more sophisticated methods are required classify that appear visually similar. Results: This study provides first machine learning algorithm can two common diseases: stony tissue loss (SCTLD) white plague (WP). This also identifies 463 biomarkers, with 275 unique SCTLD 167 WP. These biomarkers highlight differences immune algorithms were tested validated samples collected in situ, supporting biomarker efficacy identified for classification. The final model was built partial least squares discriminant analysis highly predictive an AUC 0.9895 low error rates. Conclusion: study provides diagnostic tool reliably distinguishes between phenotypically provide characterizations

Язык: Английский

The coral microbiome in sickness, in health and in a changing world DOI
Christian R. Voolstra, Jean‐Baptiste Raina, Melanie Dörr

и другие.

Nature Reviews Microbiology, Год журнала: 2024, Номер 22(8), С. 460 - 475

Опубликована: Март 4, 2024

Язык: Английский

Процитировано

45
When Microbial Interactions Go Wrong: Coral Bleaching, Disease, and Dysbiosis DOI
Julie L. Meyer, Michael Sweet, Blake Ushijima

и другие.

Coral reefs of the world, Год журнала: 2025, Номер unknown, С. 169 - 180

Опубликована: Янв. 1, 2025

Язык: Английский

Процитировано

0
Building Coral Reef Resilience Through Assisted Restoration DOI
Raquel S. Peixoto, Christian R. Voolstra, Sebastian Staab

и другие.

Coral reefs of the world, Год журнала: 2025, Номер unknown, С. 235 - 243

Опубликована: Янв. 1, 2025

Язык: Английский

Процитировано

0
Synergistic effects of the antibiotic ciprofloxacin and a simulated heatwave on the Baltic Sea dinoflagellate Apocalathium malmogiense DOI Creative Commons
Sabrina K. Roth,

Catharina Uth,

Iris Orizar

и другие.

Marine Environmental Research, Год журнала: 2025, Номер 208, С. 107155 - 107155

Опубликована: Апрель 19, 2025

Язык: Английский

Процитировано

0
Phototrophic bacteria as potential probiotics for corals DOI Creative Commons
Eslam O. Osman, Neus Garcías-Bonet, Pedro Cardoso

и другие.

npj Biodiversity, Год журнала: 2025, Номер 4(1)

Опубликована: Апрель 29, 2025

Coral-associated microorganisms provide crucial nutritional, protective, and developmental benefits, yet many functional traits remain unexplored. Phototrophic bacteria may enhance coral nutrition reduce oxidative stress during bleaching via photosynthesis antioxidant production. Despite this potential, their role in the holobiont's energy budget heat resilience is understudied. This review explores potential of phototrophic to health under environmental stress.

Язык: Английский

Процитировано

0
Understanding the role of micro-organisms in the settlement of coral larvae through community ecology DOI Creative Commons
Abigail Turnlund, Paul A. O’Brien, Laura Rix

и другие.

Marine Biology, Год журнала: 2025, Номер 172(3)

Опубликована: Фев. 7, 2025

Abstract Successful larval recruitment is essential to the growth of coral reefs and therefore plays a key role in recovery degraded worldwide. The rising intensity frequency environmental disturbance events their effect on establishment new corals outpacing natural capacity recover. To counter this, restoration programmes are increasingly turning interventionist approaches enhance recruitment, including mass-breeding aquaria for subsequent deployment field. Coral sexual propagation has potential generate large numbers genetically diverse recruits, but widespread application still limited by ability reliably guarantee successful settlement larvae. Identifying origins biochemical cues that prerequisite improving locations substrates. Microbial biofilms microbes associated with crustose coralline algae have been shown induce settlement, yet specific taxa mechanisms involved poorly understood. In this review we synthes current literature microbial challenges untaizengling origin individual originating within complex communities. Furthermore, call attention importance interrogating interactions holistic community approach further our knowledge both inducers inhibitors. Obtaining better understanding will lead more effective restoration, from engineering inductive communities synthesising can support aquaculture reef recovery.

Язык: Английский

Процитировано

0
Machine Learning Approaches for Classifying and Characterizing Coral Diseases DOI
Emily W. Van Buren, Kelsey M. Beavers,

Mariah N. Cornelio

и другие.

Research Square (Research Square), Год журнала: 2024, Номер unknown

Опубликована: Сен. 19, 2024

Abstract Background: Anthropogenic climate change has had devastating effects on the Florida and Caribbean reef systems, primarily due to increased disease outbreaks. Climate contributes rising frequency of marine diseases by expanding pathogen ranges heightening host susceptibility environmental stress. Specifically, there been a stark rise in events targeting multiple coral species, resulting high mortality rates declining biodiversity. Although many these present similar visual symptoms, they exhibit varying require distinct treatment protocols. Advances transcriptomics research have enhanced our understanding responses different diseases, but more sophisticated methods are required classify that appear visually similar. Results: This study provides first machine learning algorithm can two common diseases: stony tissue loss (SCTLD) white plague (WP). This also identifies 463 biomarkers, with 275 unique SCTLD 167 WP. These biomarkers highlight differences immune algorithms were tested validated samples collected in situ, supporting biomarker efficacy identified for classification. The final model was built partial least squares discriminant analysis highly predictive an AUC 0.9895 low error rates. Conclusion: study provides diagnostic tool reliably distinguishes between phenotypically provide characterizations

Язык: Английский

Процитировано

0