Zooplankton provide the key link between primary production and higher levels of the marine food web and they play an important role in mediating carbon sequestration in the ocean. All commercially harvested fish species depend on zooplankton populations. However, spatio-temporal distributions of zooplankton are notoriously difficult to quantify from ships. We know that zooplankton can form large aggregations that visibly change the color of the sea, but the scale and mechanisms producing these features are poorly known. Here we show that large surface patches (>1000 km) of the red colored copepod Calanus finmarchicus can be identified from satellite observations of ocean color. Such observations provide the most comprehensive view of the distribution of a zooplankton species to date, and alter our understanding of the behavior of this key zooplankton species. Moreover, our findings suggest that high concentrations of astaxanthin-rich zooplankton can degrade the performance of standard blue-green reflectance ratio algorithms in operational use for retrieving chlorophyll concentrations from ocean color remote sensing.
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http://dx.doi.org/10.1038/s41598-018-37129-x | DOI Listing |
Sci Adv
January 2025
Institute of Physics, Faculty of Physics, Astronomy and Informatics, Nicolaus Copernicus University in Toruń, Grudziadzka 5, 87-100 Torun, Poland.
Measuring low light absorption with combined uncertainty <1 per mil (‰) is crucial for many applications. Popular cavity ring-down spectroscopy can provide ultrahigh precision, below 0.01‰, but its accuracy is often worse than 5‰ due to inaccuracies in light intensity measurements.
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January 2025
Faculty of Fine Arts, Design and Architecture Department of Landscape Architecture, Tekirdağ Namık Kemal University, Tekirdağ, Türkiye.
Wetlands provide necessary ecosystem services, such as climate regulation and contribution to biodiversity at global and local scales, and they face spatial changes due to natural and anthropogenic factors. The degradation of the characteristic structure signals potential severe threats to biodiversity. This study aimed to monitor the long-term spatial changes of the Göksu Delta, a critical Ramsar site, using remote sensing techniques.
View Article and Find Full Text PDFIntegr Environ Assess Manag
January 2025
Department of Environmental Science, GITAM Deemed to be University, Visakhapatnam, India.
The rising frequency and severity of landslides in the vulnerable Himalayan region of India threaten human settlements and critical infrastructure. This growing issue demands urgent action and innovative strategies to mitigate risks and bolster the resilience of affected communities and infrastructure in this fragile area. The research explores the use of Alnus nepalensis for slope stabilization, illustrated by a case study near Ukhimath, Uttarakhand, India, and elucidates the potential ecological niche of Alnus in the temperate region of Uttarakhand using well-dispersed species occurrence records along with environment.
View Article and Find Full Text PDFIntegr Environ Assess Manag
January 2025
Federal University of the Agreste of Pernambuco, Garanhuns, Brazil.
The proliferation of cyanobacteria has become a significant water management challenge due to the increasing eutrophication of water supply reservoirs. Cyanobacterial blooms thrive on elevated nutrient concentrations and form extensive green mats, disrupting the local ecosystem. Furthermore, many cyanobacterial species can produce toxins that are lethal to vertebrates called cyanotoxins.
View Article and Find Full Text PDFFront Neurorobot
January 2025
College of Artificial Intelligence, Taiyuan University of Technology, Jinzhong, Shanxi, China.
Accurate building segmentation has become critical in various fields such as urban management, urban planning, mapping, and navigation. With the increasing diversity in the number, size, and shape of buildings, convolutional neural networks have been used to segment and extract buildings from such images, resulting in increased efficiency and utilization of image features. We propose a building semantic segmentation method to improve the traditional Unet convolutional neural network by integrating attention mechanism and boundary detection.
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