A recent paper by Beretta-Blanco and Carrasco-Letelier (2021) claims that agricultural eutrophication is not one of the main causes for cyanobacterial blooms in rivers and artificial reservoirs. By combining rivers of markedly different hydrological characteristics e.g., presence/absence and number of dams, river discharge and geological setting, the study speculates about the role of nutrients for modulating phytoplankton chlorophyll-a. Here, we identified serious flaws, from erratic and inaccurate data manipulation. The study did not define how erroneous original dataset values were treated, how the variables below the detection/quantification limit were numerically introduced, lack of mandatory variables for river studies such as flow and rainfall, arbitrary removal of pH > 7.5 values (which were not outliers), and finally how extreme values of other environmental variables were included. In addition, we identified conceptual and procedural mistakes such as biased construction/evaluation of model prediction capability. The study trained the model using pooled data from a short restricted lotic section of the (large) Uruguay River and from both lotic and reservoir domains of the Negro River, but then tested predictability within the (small) Cuareim River. Besides these methodological considerations, the article shows misinterpretations of the statistical correlation of cause and effect neglecting basic limnological knowledge of the ecology of harmful algal blooms (HABs) and international research on land use effects on freshwater quality. The argument that pH is a predictor variable for HABs neglects overwhelming basic paradigms of carbon fluxes and change in pH because of primary productivity. As a result, the article introduces the notion that HABs formation are not related to agricultural land use and water residence time and generate a great risk for the management of surface waterbodies. This reply also emphasizes the need for good practices of open data management, especially for public databases in view of external reproducibility.
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http://dx.doi.org/10.1016/j.scitotenv.2021.151854 | DOI Listing |
J Helminthol
December 2024
Escuela de Tecnología Médica y Centro Integrativo de Biología y Química Aplicada (CIBQA). Universidad Bernardo O' Higgins, Santiago de Chile, Chile.
is a genus within the family Haploporidae and is distributed throughout the Americas. The recent application of molecular techniques has facilitated the reorganization of this genus and the description of new species, resulting in a current total of 28 species. In Argentina, 11 species have been identified; however, the validity of and remains controversial.
View Article and Find Full Text PDFZootaxa
April 2024
Sección Entomología; Facultad de Ciencias; Universidad de la República; Iguá 4225; PC 11400; Montevideo; Uruguay.
Glob Chang Biol
October 2024
Department of Fisheries, Wildlife, and Conservation Sciences, Oregon State University, Corvallis, Oregon, USA.
Recent decades have witnessed substantial changes in freshwater biodiversity worldwide. Although research has shown that freshwater biodiversity can be shaped by changes in habitat diversity and human-induced pressure, the potentials for interaction between these drivers and freshwater biodiversity at large spatial extents remain unclear. To address these issues, we employed a spatially extensive multitrophic fish and insect database from 3323 stream sites across the United States, to investigate the ability of habitat diversity to modulate the effect of human pressure on the richness and abundance of fish and insects.
View Article and Find Full Text PDFEnviron Monit Assess
October 2024
Aquaculture Department, Federal University of Santa Catarina, Florianópolis, Santa Catarina, Brazil.
The golden mussel (Limnoperna fortunei) is an invasive bivalve that has established itself in several South American river systems, impacting ecosystem functioning. Reservoir cascades provide their larvae with the means of rapid dispersal, but the relationship between environmental variables and larval stage structure remains unclear. In this study, the density of three L.
View Article and Find Full Text PDFSci Total Environ
December 2024
Departamento Modelización Estadística de Datos e Inteligencia Artificial (MEDIA), CURE-Rocha, Universidad de la República, Ruta Nacional N°9 intersección Ruta N°15, Rocha 27000, Uruguay.
Faecal contamination is a widespread environmental and public health problem on recreational beaches around the world. The implementation of predictive models has been recommended by the World Health Organization as a complement to traditional monitoring to assist decision-makers and reduce health risks. Despite several advances that have been made in the modeling of faecal coliforms, tools and algorithms from machine learning are still scarcely used in the field and their implementation in nowcast systems is delayed.
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