Procesos oceanográficos asociados al desarrollo y dispersión de florecimientos de Gymnodinium catenatum en el norte del Golfo de California

Oceanographic processes associated with harmful algal blooms of Gymnodinium catenatum development and dispersal in the northern Gulf of California

GABRIELA RESENDIZ COLORADO (2023, [Tesis de doctorado])

Durante los últimos años, durante la temporada de invierno y primavera, se ha detectado la presencia de florecimientos algales nocivos (FAN) causados por el dinoflagelado Gymnodinium catenatum en el norte del golfo de California (NGC). Estos eventos tienen impactos ecológicos, económicos y sociales negativos porque G. catenatum es una especie productora de saxitoxina, la cual, está asociada al envenenamiento paralítico por consumo de mariscos, lo que origina que la autoridad sanitaria se vea en la necesidad de implementar vedas en áreas de extracción de almeja generosa en el NGC al detectar producto contaminado. Por lo tanto, es necesario conocer los procesos físicos que provocan la ocurrencia y recurrencia de estos eventos, así como contar con un sistema de monitoreo y alerta temprana que permita tomar decisiones y acciones oportunas de mitigación contra los efectos perjudiciales de estos fenómenos. En este trabajo se abordaron estas necesidades de investigación por medio de la implementación de un método de detección remota de estos FAN, utilizando la clasificación de máxima verosimilitud basada en los datos de dos eventos sucedidos en 2015 y 2017. Los resultados de este enfoque fueron satisfactorios al reproducir la temporalidad de la presencia de la especie documentada por medio de muestreos semanales en la bahía de San Felipe, así como de la detección geográfica en las áreas que se conocen que son afectadas recurrentemente. Para estudiar los procesos físicos asociados a estos FAN, se implementó un modelo hidrodinámico usando el Sistema de Modelación Oceánica Regional (ROMS) para el norte del golfo de California. A partir de este modelo se obtuvieron datos de variables como energía cinética turbulenta, temperatura, corrientes y se complementaron con datos de esfuerzo de fondo producido por oleaje estimados a partir de datos de la quinta generación del reanálisis atmosférico del clima global del ECMWF (ERA-5), los análisis de estas variables y la abundancia semanal de G. catenatum permitieron identificar que los principales procesos asociados a la formación de estos florecimientos es el incremento del esfuerzo de fondo asociado al oleaje y la turbulencia. Estos, a su vez, tienen una relación con el cambio en el patrón del viento que se caracteriza por ser del noroeste durante las temporadas de invierno y primavera. Respecto a la dispersión de los florecimientos algales, con base en los datos obtenidos del modelo hidrodinámico, se realizaron experimentos

During the recent winter and spring seasons, harmful algal blooms (HABs) caused by the dinoflagellate Gymnodinium catenatum have been detected in the northern Gulf of California (NGC). These events have negative ecological, economic, and social impacts because G. catenatum is a species producer of saxitoxin, which is associated with paralytic shellfish poisoning, which causes the need to implement bans by the health authority in extraction areas of generous clam in the NGC when detecting contaminated products. Therefore, it is necessary to identify the physical processes that cause the occurrence and recurrence of these events and have a monitoring and early warning system that allows timely decisions and mitigation actions to be taken against the harmful effects of these henomena. In this work, these research gaps are approached by implementing a remote detection method for these HABs, using maximum likelihood classification based on data from two events in 2015 and 2017. The results of this approach were satisfactory by reproducing the temporality presence of Gymnodinium catenatum documented through weekly sampling in San Felipe Bay, as well as geographic detection in areas known to be recurrently affected. To study the physical processes associated with these HABs, a hydrodynamic model was implemented using the Regional Ocean Modeling System (ROMS) for the northern Gulf of California. From this model, variables such as turbulent kinetic energy, temperature, and currents were obtained and were complemented with data on bottom stress produced by waves estimated from the fifth generation ECMWF atmospheric reanalysis of the global climate (ERA-5) data. Analyzing these variables and the weekly abundance of G. catenatum, it was possible that the processes associated with forming these blooms are the increase in bottom stress related to waves and turbulence. These processes are related to the change in the wind pattern characterized by northwest winds during the winter and spring seasons. Regarding the dispersion of algal blooms, based on the data obtained from the hydrodynamic model, lagrangian experiments were carried out to estimate the transport of the HAB. The results showed that the modeled dispersion corresponds with the detection carried out with the remote sensing method mplemented in this work for the 2017 event. The results obtained from this work are essential knowledge for the operational implementation of monitoring and early warning systems ..

Gymnodinium catenatum, florecimientos algales nocivos, percepción remota, modelación hidrodinámica, norte del golfo de California : Gymnodinium catenatum, harmful algal bloom, remote sensing, hydrodynamic modeling, northern Gulf of California BIOLOGÍA Y QUÍMICA CIENCIAS DE LA VIDA OTRAS ESPECIALIDADES DE LA BIOLOGÍA OTRAS OTRAS

High-throughput plot-level quantitative phenotyping using convolutional neural networks on very high-resolution satellite images

Francisco Pinto Matthew Paul Reynolds Robert Furbank (2024, [Artículo])

Deep Learning Object-Based Image Analysis Optical Imagery CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA AGRICULTURE IMAGE ANALYSIS PLANT BREEDING REMOTE SENSING MACHINE LEARNING

Remote sensing of quality traits in cereal and arable production systems: A review

Zhenhai  Li xiuliang jin Gerald Blasch James Taylor (2024, [Artículo])

Cereal is an essential source of calories and protein for the global population. Accurately predicting cereal quality before harvest is highly desirable in order to optimise management for farmers, grading harvest and categorised storage for enterprises, future trading prices, and policy planning. The use of remote sensing data with extensive spatial coverage demonstrates some potential in predicting crop quality traits. Many studies have also proposed models and methods for predicting such traits based on multi-platform remote sensing data. In this paper, the key quality traits that are of interest to producers and consumers are introduced. The literature related to grain quality prediction was analyzed in detail, and a review was conducted on remote sensing platforms, commonly used methods, potential gaps, and future trends in crop quality prediction. This review recommends new research directions that go beyond the traditional methods and discusses grain quality retrieval and the associated challenges from the perspective of remote sensing data.

Quality Traits Grain Protein CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA REMOTE SENSING QUALITY GRAIN PROTEINS CEREALS PRODUCTION SYSTEMS

Tallo: A global tree allometry and crown architecture database

Tommaso Jucker Jörg Fischer Jerome Chave David Coomes John Caspersen Arshad Ali Grace Jopaul Loubota Panzou Ted R. Feldpausch Daniel Falster Vladimir Andreevich Usoltsev Stephen Adu-Bredu Luciana Alves Mohammad Aminpour Bhely ANGOBOY Ilondea Niels Anten Cécile Antin yousef askari Rodrigo Muñoz Ayyappan Narayanan Patricia Balvanera Lindsay Banin Nicolas Barbier John J. Battles Hans Beeckman Yannick Enock Bocko Benjamin Bond_Lamberty Frans Bongers Samuel Bowers THOMAS BRADE Michiel van Breugel ARTHUR CHANTRAIN Rajeev Chaudhary JINGYU DAI Michele Dalponte Kangbéni Dimobe jean-christophe domec Jean-Louis Doucet Remko Duursma Moisés Enriquez KARIN Y. VAN EWIJK WILLIAM FARFAN_RIOS Adeline FAYOLLE ERIC FORNI David Forrester Hammad Gilani John Godlee Sylvie Gourlet-Fleury Matthias Haeni Jefferson Hall Jie He Andreas Hemp JOSE LUIS HERNANDEZ STEFANONI Steven Higgins ROBERT J. HOLDAWAY Kiramat Hussain Lindsay Hutley Tomoaki Ichie Yoshiko Iida Hai Jiang Puspa Raj Joshi Seyed Hasan Kaboli Maryam Kazempour Larsary Tanaka Kenzo Brian Kloeppel Takashi Kohyama Suwash Kunwar Shem Kuyah Jakub Kvasnica Siliang Lin Emily Lines Hongyan Liu CRAIG LORIMER Joel Loumeto Yadvinder Malhi Peter Marshall Eskil Mattsson Radim Matula Jorge Arturo Meave del Castillo Sylvanus Mensah XIANGCHENG MI Stephane MOMO Takoudjou Glenn Moncrieff Francisco Mora Sarath Nissanka Kevin O'Hara steven pearce Raphaël Pélissier Pablo Luis Peri Pierre Ploton Lourens Poorter mohsen javanmiri pour Hassan pourbabaei JUAN MANUEL DUPUY RADA Sabina Ribeiro Ryan Casey ANVAR SANAEI Jennifer Sanger Michael Schlund Giacomo Sellan Alexander Shenkin Bonaventure Sonké Frank Sterck Martin Svatek Kentaro Takagi Anna Trugman Farman Ullah Matthew Vadeboncoeur Ahmad Valipour Mark Vanderwel Alejandra Vovides Weiwei WANG Li Qiu Christian Wirth MURRAY WOODS Wenhua Xiang Fabiano de Aquino Ximenes Yaozhan Xu TOSHIHIRO YAMADA Miguel A. Zavala (2022, [Artículo])

Data capturing multiple axes of tree size and shape, such as a tree's stem diameter, height and crown size, underpin a wide range of ecological research—from developing and testing theory on forest structure and dynamics, to estimating forest carbon stocks and their uncertainties, and integrating remote sensing imagery into forest monitoring programmes. However, these data can be surprisingly hard to come by, particularly for certain regions of the world and for specific taxonomic groups, posing a real barrier to progress in these fields. To overcome this challenge, we developed the Tallo database, a collection of 498,838 georeferenced and taxonomically standardized records of individual trees for which stem diameter, height and/or crown radius have been measured. These data were collected at 61,856 globally distributed sites, spanning all major forested and non-forested biomes. The majority of trees in the database are identified to species (88%), and collectively Tallo includes data for 5163 species distributed across 1453 genera and 187 plant families. The database is publicly archived under a CC-BY 4.0 licence and can be access from: https://doi.org/10.5281/zenodo.6637599. To demonstrate its value, here we present three case studies that highlight how the Tallo database can be used to address a range of theoretical and applied questions in ecology—from testing the predictions of metabolic scaling theory, to exploring the limits of tree allometric plasticity along environmental gradients and modelling global variation in maximum attainable tree height. In doing so, we provide a key resource for field ecologists, remote sensing researchers and the modelling community working together to better understand the role that trees play in regulating the terrestrial carbon cycle. © 2022 The Authors. Global Change Biology published by John Wiley & Sons Ltd.

ALLOMETRIC SCALING CROWN RADIUS FOREST BIOMASS STOCKS FOREST ECOLOGY REMOTE SENSING STEM DIAMETER TREE HEIGHT BIOLOGÍA Y QUÍMICA CIENCIAS DE LA VIDA BIOLOGÍA VEGETAL (BOTÁNICA) ECOLOGÍA VEGETAL ECOLOGÍA VEGETAL

Modeling spatiotemporal patterns of land use/land cover change in Central Malawi using a neural network model

Leah Mungai Joseph Messina Leo Zulu Jiaguo Qi Sieglinde Snapp (2022, [Artículo])

Multilayer Perceptrons CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA AGRICULTURE LAND USE POPULATION SATELLITE IMAGERY TEXTURE LAND COVER NEURAL NETWORKS REMOTE SENSING

2022 Advanced wheat improvement course: Global wheat rust surveillance

&

monitoring

David Hodson (2022, [Objeto de congreso])

CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA WHEAT RUSTS DISEASE SURVEILLANCE NEW TECHNOLOGY REMOTE SENSING

Global rust monitoring

David Hodson (2022, [Objeto de congreso])

CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA WHEAT RUSTS DISEASE SURVEILLANCE REMOTE SENSING NEW TECHNOLOGY

Use of remote sensing for linkage mapping and genomic prediction for common rust resistance in maize

Alexander Loladze Francelino Rodrigues Cesar Petroli Felix San Vicente Garcia Bruno Gerard Osval Antonio Montesinos-Lopez Jose Crossa Johannes Martini (2024, [Artículo])

Common Rust Rp1 Locus CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA RUSTS REMOTE SENSING VEGETATION INDEX MAIZE CHROMOSOME MAPPING

KARC 2020 Experiment – The potential of UAV and very high-resolution satellite imagery for yellow and stem rust detection and phenotyping in Ethiopia

Gerald Blasch (2023, [Objeto de congreso])

CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA REMOTE SENSING WHEAT CROPS DISEASES

Wheat rust early warning systems in Ethiopia: implementation of remote sensing technologies to combat crop disease

Gerald Blasch (2020, [Objeto de congreso])

CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA RUSTS MONITORING DISEASE SURVEILLANCE EARLY WARNING SYSTEMS REMOTE SENSING