Variability in stream water chemistry and brown trout (Salmo trutta L.) parr otolith microchemistry on different spatial scales

Abstract

Mixed‐stock fishery of sea trout (Salmo trutta L.) in the Baltic Sea targets wild and stocked individuals, which could lead to over‐exploitation of wild stocks. Studying the contribution of different spawning streams and hatcheries to the fishery would aid in more effective management and conservation of S. trutta. The aim of the present study was to explore whether and to what extent parr from different S. trutta spawning streams and hatcheries can be differentiated, using water chemistry and otolith microchemistry. Water chemistry samples demonstrated significant spatial variation between different study regions (Estonia and Finland); Sr, Mg, Fe, Zn, Ca and Cu contributed most to the separation. Parr collected from different sites showed a significant difference in otolith element:Ca and 87Sr:86Sr ratios on a smaller scale, including only Estonian samples (n = 361), and on a larger scale incorporating parr from Estonia, Latvia and Finland (n = 564). The reclassification rate of S. trutta parr was on average 73%, being highest in Finnish streams and hatcheries, followed by Latvia and Estonia. Otolith 87Sr:86Sr and Sr:Ca ratios were the most important in discriminating fish from different sampling sites. The present study demonstrates the potential of separating S. trutta on various spatial scales including different countries and neighbouring streams, making this the most extensive otolith microchemistry study to date in terms of streams and regions studied. The established reclassification rules form a basis for future work investigating the natal origin of adult S. trutta caught in the mixed‐stock fishery in the sea.

Assessment of heavy metal and organic pollution of a salmonid ecosystem: Case of the Oued Guigou River, Middle Atlas (Morocco)

“…Average calcium (Ca) and magnesium (Mg) concentrations were 46.16 mg/L and 14.74 mg/L respectively, with the maximum values observed at site S5 (62.9 mg/L for Ca and 15.7 mg/L for Mg). These concentrations are attributed to the retention of these ions in soil particles subsequently mobilized by the existing vegetation, and which can slow the rate of brown trout migration in the water (Matetski et al, 2022). Calcium and magnesium are found in all natural waters, with calcium contributing significantly to their hardness (Abba et al, 2012).…”

Otolith microchemistry combined with genetics reveal patterns of straying and population connectivity in anadromous brown trout (Salmo trutta)

“…The classification accuracy of the Random Forest classifier, which included 87 Sr/ 86 Sr, Sr/Ca, Ba/Ca, Mg/Ca and Mn/Ca, was 80%, which is a high accuracy for classifying individuals to their river of origin, resembling results from previous studies. For example, in otolith microchemistry studies among Pacific salmonids, a classification accuracy between 89 and 100% (Maguffee et al., 2019; Turcotte & Shrimpton, 2020) has been documented, while studies on brown trout have reported accuracies between 73% and 93% (Matetski et al., 2022; Mikheev et al., 2021). Our study therefore provides further evidence that otolith microchemistry can be used to differentiate juvenile trout originating from different rivers.…”

“…Among all the elements included in the classifier, 87 Sr/ 86 Sr and Sr/ Ca contributed the most to classification accuracy, while Ba/Ca, Mn/ Ca and Mg/Ca contributed less. The importance of 87 Sr/ 86 Sr and Sr/ Ca has been documented in similar studies among different taxa originating from various systems (Heidemann et al, 2012;Matetski et al, 2022;Mikheev et al, 2021). During the analytical process, additional elements were measured from the otoliths (Pb and Zn), but did not improve, and in some instances negatively affected, the ability of the Random Forest classifier to assign individuals to their natal river, so they were excluded from the analysis.…”

“…Individuals originating from the rivers situated on northern side of the fjord, Villestrup and Valsgaard, had a higher likelihood of being correctly classified to their natal river than individuals from rivers on the southern side, Kastbjerg and Maren Møllebaek. While overall high classification accuracy has been reported for different systems (Mikheev et al, 2021;Turcotte & Shrimpton, 2020), it has also been documented to be accompanied with high variability in classification accuracy for nearby rivers (Matetski et al, 2022).…”

“…Otolith microchemistry and genetics have proven to be effective tools for investigating population structure and connectivity in fishes (Bekkevold et al., 2020; Collins et al., 2013; Heidemann et al., 2012). Otolith microchemistry provides the possibility to infer information on migratory patterns (Brennan et al., 2015; Sturrock, Wikert, et al., 2015; Taal et al., 2014;), habitat use (Ciepiela & Walters, 2019; Phillis et al., 2018; Volk et al., 2010) and the origin of fish among marine, freshwater and diadromous populations (Chen et al., 2020; Heidemann et al., 2012; Matetski et al., 2022). Otoliths are particularly well suited to track migrations among aquatic ecosystems, as they grow continuously and incorporate chemical differences among water sources over the lifetime of an individual (Campana, 1999; Svedäng et al., 2010; Tabouret et al., 2010).…”