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Sci. Total Environ. · Jan 2019
Modelling the effect of season, sex, and body size on the three-spined stickleback, Gasterosteus aculeatus, cellular innate immunomarkers: A proposition of laboratory reference ranges.
- Adrien Marchand, Cleo Tebby, Rémy Beaudouin, Younes M I Hani, Jean-Marc Porcher, Cyril Turies, and Anne Bado-Nilles.
- Institut National de l'Environnement Industriel et des Risques (INERIS), UMR-I 02 SEBIO, Parc Technologique Alata, BP 2, 60550 Verneuil-en-Halatte, France; Université de Reims Champagne-Ardenne (URCA), UMR-I 02 SEBIO, Moulin de la Housse, B.P. 1039, 51687 Reims, France.
- Sci. Total Environ. 2019 Jan 15; 648: 337-349.
AbstractInnate immunomarkers reflect both environmental contamination and fish health status, providing useful information in environmental risk assessment studies. Nevertheless, the lack of knowledge about the effect of confounding factors can lead to data misinterpretation and false diagnoses. The aim of this study was to evaluate the impact of three confounding factors (season, sex and body size) on three-spined stickleback innate immunomarkers in laboratory conditions. Results shown strong seasonal variations in stickleback innate immunomarkers, with higher immune capacities in late winter-early spring and a disturbance during the spawning period in late spring-summer. Sex and body size had a season dependant effect on almost all tested immunomarkers. Reference ranges were established in laboratory-controlled conditions (i.e. laboratory reference ranges) and compared with data obtained from in vivo chemical expositions. The predictive power of the statistical model depended on the immunomarker, but the control data of the in vivo experiments, realized in same laboratory conditions, were globally well include in the laboratory reference ranges. Moreover, some statistical effects of the in vivo exposures were correlated with an augmentation of values outside the reference ranges, indicating a possible harmful effect for the organisms. As confounding factors influence is a major limit to integrate immunomarkers in biomonitoring programs, modelling their influence on studied parameter may help to better evaluated environmental contaminations.Copyright © 2018 Elsevier B.V. All rights reserved.
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