DOI: 10.5061/dryad.p2ngf1vq9
title: Smolt outmigration timing in Norway

description: 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.
description: <strong>Data Collection</strong>
 <em>Smolt migration data</em> 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 published scientific articles, (2) Norwegian reports, and (3) unpublished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. <em>River morphology
 data</em> 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. <em>Annual environmental data</em> 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 <em>predict.gam</em> 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.