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Smolt outmigration timing in Norway

Citation

Lennox, Robert et al. (2021), Smolt outmigration timing in Norway, Dryad, Dataset, https://doi.org/10.5061/dryad.p2ngf1vq9

Abstract

Aim - Accurate predictions about transition timing of salmon smolts between freshwater and marine environments are key to effective management. We aimed to use available data on Atlantic salmon smolt migration to predict the emigration timing in rivers throughout Norway.

Location - In this study, we used data outmigration timing data of Atlantic salmon (Salmo salar) smolts from 41 rivers collected from 1984-2018 to make a predictive model for the timing of out-migrating salmon smolts along 12 degrees latitude.

Methods - Smolt migration data were collated from rivers where smolts are monitored with traps, video, and tagging and matched to river-specific metadata. Using a mixed effect generalized additive model, we tested for effects of spatial location, air temperature, river flow, and counting methods on the timing of 25% smolt emigration from rivers.

Results - After accounting for spatial effects and repeated measurements (across years and among rivers), air temperature and counting method were significant drivers of the estimated timing of smolt emigration. In-sample predictions yielded strong correlation with observed values, as did 10-fold cross-validation. Out-of-sample predictions suggested that the previous national estimates underestimated the migration timing in southern populations (linear model intercept = 39.73 days).

Conclusion - Model-derived estimates of run timing can be used to more accurately predict the timing of outmigration in order to better calibrate environmental flows and regulate management of marine industries such as aquaculture that may affect migration success at this particularly sensitive life stage.

Methods

Data Collection

Smolt migration data

The goal of our study was to collate available smolt migration data from Norway, in an attempt to make a predictive model of the timing of smolt migration in this area. Data were extracted from three sources. (1) published scientific articles, (2) Norwegian reports, and (3) unpublished data available from the authors’ research institutions. Data were updated from previous compilations using the same methodology (Ugedal et al. 2014). The database does not contain daily counts but summarizes the timing of smolt emigration by percentiles, recording the dates of 25% passage, 50% passage, and 75% passage (Table 1).

Smolt migration was monitored in 348 river years, comprising 47 rivers between 58.02 and 70.50 degrees latitude from 1984-2018 (Figure 1). Monitoring was conducted using different methods of observation: traps (N = 252 river years), video counting (N = 84 river years), and tagging (N = 12 river years). Note Eio and Vigda are the only two rivers that had multiple counting methods, so they are counted multiple times. The placement of these monitoring tools was not consistent among rivers nor was the timing of deployment standardized.

River morphology data

Morphological data from the river catchments was downloaded from NEVINA (http://nevina.nve.no/). This includes elevation data from the catchment, land composition (e.g. percent of catchment covered by agriculture, forest, lake, and urban areas), air temperature throughout the year (summer, winter, July, August temperatures). In addition, modelled average discharge, average rainfall, and average air temperature was extracted from each of the catchments from the same database.

Annual environmental data

Seasonal water temperature measurements data were not available for the majority of rivers. However, air temperature data were collected by monitoring stations throughout Norway, for which historical data are freely available through an API using the esd package in R (Benestad et al. 2009). We downloaded temperature records from all available stations. A generalized additive model was fit to each individual year to explain the air temperature recorded by the longitude, latitude, and altitude of the station using the gam function in the mgcv package (Wood 2017). The gam models were then carried forward to predict the air temperatures for each river in each year using coordinates of the rivermouth, and average grade of the river using the predict.gam function. Air temperature for each river in each year was summarized as the average between January 1 and March 31.

Modelled water discharge data were available for each river from the NorKyst800 model.

The Norwegian river discharges were modelled by the NVE (Norwegian Water Resources and Energy Directorate) using a distributed version of the HBV-model with 1 km horizontal resolution (Beldring et al. 2003 and Huang et al., 2019). We summarised water discharge for each river in each year by extracting the first day of the year when the flow first hit 10 and 25 % of the maximum flow from March 1 to July 18, which was considered to be the maximal likely window for onset of smolt migration. We used different temporal windows for temperature and flow because temperature should control physiological readiness (proximate cause) and flow should drive the exact timing of migration (ultimate cause). Both extractions gave very similar model outputs.