Two-year spawning migration as a life history strategy of sea trout

Description

Abstract In this study we used radio-telemetry derived data of riverine movements and spawning migration behaviour of wild sea trout tagged in a large, northern Baltic Sea river system, the Tornionjoki River. Tracking results indicate that both immature and mature trout utilize the freshwater environments for overwintering, and that previous spawning experience and age of tagged individuals were important for the maturation likelihood and subsequent direction of migration (mature spawners migrated upstream, immature fish returned to sea). The results indicate that the population has adopted a two-year spawning migration cycle, where individuals overwinter in-river both pre- and post-spawning. The prolonged freshwater stay of sea trout migrants needs to be taken into consideration in management to ensure adequate protection of both immature and mature sea trout. General information Title of Dataset: Two-year spawning migration as a life history strategy of sea trout (Salmo trutta L.) DOI: doi: 10.17632/rjxmmmvbvs.1 Contact Information: Principal Investigator Contact Information Name: Linus Lähteenmäki Institution: Åbo Akademi University Address: Aurum, Henriksgatan 2–20520 Åbo, Finland, Email: linus.lahteenmaki@abo.fi, ORCID:https://orcid.org/0000-0002-3931-3595 Date of data collection (approximate date): 01-04-2018 - 31-12-2021 Geographic location of data collection: Tornionjoki River system (65°46 N, 24°07 E) Information about funding sources that supported the collection of the data: This study was funded by the Lapland´s Centre for Economic Development, Transport and the Environment, the Finnish–Swedish Transboundary River Commission, the Swedish Agency for Marine and Water Management, Natural Resources Institute Finland, Kempestiftelserna, Swedish University of Agricultural Science, Åbo Akademi University and Svenska Kulturfonden. Recommended citation for this dataset: Lähteenmäki, L., Huusko, R., Hellström, G., Snickars, M., & Romakkaniemi, A. (in prep.) Data from: Two-year spawning migration as a life history strategy of sea trout (Salmo trutta L.) in large, high latitude river systems. Mendeley Data, V1, doi: 10.17632/rjxmmmvbvs.1. All datasets used in the data analysis and generation of the visualizations for this study are provided in this repository. Please view the "READ ME" file For more detailed information on dataset contents

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Radio-telemetry data of sea trout movements in the Tornionjoki River system was collected from 2018-2021. 113 sea trout were caught and tagged in the river system. Individual characteristics (length, weight, body condition) were measured, and scale samples were taken for age determination and estimate of previous spawning experience. Caught individuals were tagged with coded radio transmitters (model: MCFT2-3A, Lotek Wireless Inc. Canada). Movement data of tagged fish was collected by automated listening stations (ALS) consisting of automated radio recievers (Model: SRX-DL or SRX800, Lotek Wireless Inc.) connceted to Yagi-antennae. Additionally, tracking by car using mobile recievers (SRX-400, Lotek Wireless Inc.) was done weekly, while tracking by boat and airplane was done twice, respectively. Position of each observation was determined with a 50-100m accuracy during the migration and with a 10-50m accuracy during spawning periods. All telemetry data was pooled into a joint dataset which was used to filter individuals based on movements in the river system (processed data). Spawners were separated based on observations of movements upstream into potential spawning habitats. Overwintering individuals were separated by observations of trout moving back to sea post-overwintering in-river. Individuals that did not fall into either category were deemed lost, presumably dead. Migration characteristics (migration speed, location of spawning habitat, length of riverine stay etc.) was calculated using the processed dataset. Furthermore, all individuals which had all measured individual characteristics (body size, ages etc.) were included in the generation of a support vector machine model (SVM) to determine importance of the characteristics on maturation likelihood.

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