Results 161 to 170 of about 599,621 (222)

Coral Restoration in the Omics Era: Development of Point-of-Care Tools for Monitoring Disease, Reproduction, and Thermal Stress. [PDF]

Bioessays
Chille EE, Stephens TG, Nandi S, Jiang H, Gerdes MJ, Williamson OM, Neufeld A, Montoya-Maya P, Bhattacharya D.   +8 more
europepmc   +1 more source

Deep learning–assisted coral morphology cover across 742 sites on the Great Barrier Reef (2022–2025): A Great Reef Census citizen science dataset

Limnology and Oceanography Letters, Volume 11, Issue 6, November 2026.
Abstract Citizen science can expand the spatial extent of coral reef surveys, but its value is often limited by inconsistent data quality and slow manual analysis. Here we combine volunteer‐collected imagery with deep learning to overcome these constraints, delivering validated coral cover data at scale.
Christopher L. Lawson, Kathryn M. Chartrand, Peter J. Mumby, Andy Ridley, Benjamin Vozzo, Seán Daly, Chandru Ganesan, Aruna Kolluru, Chris M. Roelfsema   +8 more
wiley   +1 more source

Frag-n-Fly gardening method for coral restoration - Pitching to the industry level the methods for coral fragmentation and outplanting in coral reef restoration. [PDF]

MethodsX
Giraldes BW, Donahue C, Mello ES, Al-Mohannadi HS, Mustafa SF, Abdulla M, Range P.   +6 more
europepmc   +1 more source

Melanoma and Other Melanistic Lesions in Brown Bullhead Ameiurus nebulosus From Waterbodies in the Northeastern United States and Canada: Identification of Risk Factors

Journal of Fish Diseases, Volume 49, Issue 11, November 2026.
ABSTRACT Melanistic lesions, including non‐raised black areas due to proliferations of melanocytes and melanomacrophages in the dermis and epidermis, as well as raised black areas consistent with melanoma, are described in brown bullhead (BBH) Ameiurus nebulosus from three water bodies in the northeastern United States and Quebec, Canada.
Vicki S. Blazer, Peter Emerson, Matthew Bodnar, Thomas Jones, David R. Russell, Matthew Pehrson, Cheyenne R. Smith, Danielle Cleveland, Mark J. Henderson, Patricia M. Mazik   +9 more
wiley   +1 more source

Coral restoration in a stressful environment: Disease, bleaching, and dysbiosis in <i>Acropora aspera</i> in Guam, Micronesia. [PDF]

iScience
Raymundo LJ, Andersen MD, Rouzé H.
europepmc   +1 more source

Quantifying Land–Sea Connectivity Through Remotely Sensed River Plumes

Geophysical Research Letters, Volume 53, Issue 19, 16 October 2026.
Abstract Coastal ecosystems are highly influenced by riverine freshwater inputs, but the nature of these influences is dynamic and hard to characterize. Here we present a method that leverages remote sensing data of river plumes to quantify land–sea connectivity.
Kelly L. Hondula, Albert Ruhí, Gregory P. Asner, Rachel R. Carlson   +3 more
wiley   +1 more source

Coral Reef [PDF]

Coral Reef
core   +1 more source

An Interpretable, Data‐Driven, Hierarchical Multi‐Domain Fusion Framework for Classification and Motor Function Scoring in Chronic Ankle Instability

Advanced Science, Volume 13, Issue 55, 5 October 2026.
An AI‐enabled digital twin framework integrates wearable EMG sensing with hierarchical multi‐domain fusion to classify chronic ankle instability, distinguish clinically relevant subtypes, and generate continuous motor function scores. Clinically interpretable functional stratification and SHAP‐based biomarker analysis provide transparent decision ...
Tianle Jie, Datao Xu, Zhifeng Zhou, Bas Van Hooren, Huiyu Zhou, Yi Yuan, Xiangli Gao, Monèm Jemni, Liangliang Xiang, Meizi Wang, Justin Fernandez, Tibor J. Goda, Yaodong Gu   +12 more
wiley   +1 more source

A Genetic Method for Distinguishing Cryptic Pocillopora Species in French Polynesia Without Sequencing

Ecology and Evolution, Volume 16, Issue 10, October 2026.
This study presents restriction fragment length polymorphism (RFLP) digests that identify the six species of Pocillopora (P. acuta, P. cf. effusa, P. grandis, P. meandrina, P. tuahiniensis, and P. verrucosa) found in French Polynesia, without sequencing.
Francesca M. Cohn, Erika C. Johnston, Scott C. Burgess, Jordan A. Sims, Krithika Layagala, Pierrick Harnay, Hollie M. Putnam, Adrienne M. S. Correa   +7 more
wiley   +1 more source

Using Deep Learning AI to Detect Riparian Vegetation Recovery Following a Major Flood in the Brisbane River

River Research and Applications, Volume 42, Issue 8, Page 1720-1735, October 2026.
ABSTRACT Post‐flood riparian vegetation recovery demands significant attention; however, the complexity of traditional remote sensing methods often hinders environmental managers from implementing rapid vegetation monitoring. This paper developed a model using a Deep Learning Model within ArcGIS to classify and detect recovery of vegetation with high ...
Sydney O'Hare, Jinghan Li, Yongping Wei
wiley   +1 more source