DOI: 10.5061/dryad.x69p8czv9
title: Data from: Fine-scale reconstruction of pelagic fish migration by iso-logging of eye lens

description: Understanding the evolution and maintenance of partial migration, in which
 a population includes both migratory and resident individuals, is an
 important challenge in ecology. However, studies of fish partial migration
 have exclusively focused on terrestrial and coastal ecosystems, and the
 difficulty of tracking highly migratory fish has hampered studies in the
 open ocean. Here, we performed isotope-based migration tracking
 (iso-logging) of 33 adult skipjack tuna (Katsuwonus pelamis) in the
 western Pacific by measuring carbon and nitrogen stable isotope ratios
 (δ13C and δ15N) in the eye lens. We compared distribution maps of δ13C and
 δ15N (isoscapes) with the δ13C and δ15N of individual eye lens and
 estimated the most likely migration routes by using a state–space model.
 Our results showed that i) skipjack in the western Temperate North Pacific
 included migrants from the western Tropical Pacific, and ii) most
 individuals caught in the western Tropical Pacific resided in the tropics
 or subtropics for their entire lives. Thus, this study is the first to
 observe partial migration in a highly migratory pelagic marine fish.
 Iso-logging offers a simpler and more cost-effective method of migration
 tracking than conventional bio-logging, and could provide a breakthrough
 for research on marine behavioral ecology.
description: Stable isotope data for skipjack eye lens sections (Dataset 1)
 were obtained through isotope analysis of 33 skipjacks caught in the
 western Pacific. The data include the date of capture, latitude and
 longitude of the capture location, and the fork length (FL) at the time of
 catch. The eye lens from each fish was extracted, dried under mild vacuum
 conditions, and sectioned serially while measuring the lens diameter.
 Using an equation that relates the dried lens diameter to the fork length
 of skipjack, the estimated fork length (Estimated FL) at the time each
 lens section was formed was calculated. Each lens section was analyzed
 with an isotope mass spectrometer (EA-IRMS) to determine the stable carbon
 and nitrogen isotope ratios, including the analytical error and the
 carbon-to-nitrogen molar ratio (C/N). The corrected δ¹³C and δ¹⁵N values
 were used to compare the carbon and nitrogen isotope ratios in the eye
 lens sections with the isoscapes. These corrections accounted for isotope
 ratio offsets between the eye lens and muscle tissue and adjusted for
 increases in isotope ratios associated with somatic growth in skipjack
 tuna. Dataset 2 represents the fork length and eye lens diameter of
 skipjack tuna (n = 88).
description: # **Fine-scale reconstruction of pelagic fish migration by iso-logging of
 eye lens**: isotope data and custom codes
 [https://doi.org/10.5061/dryad.x69p8czv9](https://doi.org/10.5061/dryad.x69p8czv9) ## Description of the data and file structure Files are stored in Excel (.xlsx) or R (.R) format ### Files and variables #### File: SI_data.csv **Description:** Metadata and stable isotope ratios of eye lens sections dissected from adult skipjack tuna (*Katsuwonus pelamis*) captured in the western Pacific. ##### Variables * Fish_ID:  unique identifier for each skipjack tuna * Lens_ID: number of divided eye lens sections from core to surface * Date: capture date of each skipjack tuna * FL_mm: fork length of each skipjack tuna at the time of capture * lat: latitude of the capture location of each skipjack tuna * lon: longitude of the capture location of each skipjack tuna * LD_mm: lens diameter measured before each lens section was scraped * est_FL_mm: estimated fork length at the time each lens section was formed * C.N: carbon-to-nitrogen molar ratio in each lens section * d13C: measured carbon stable isotope ratios (‰) for each eye lens section * d15N : measured nitrogen stable isotope ratios (‰) for each eye lens section * sd_d13C: measurement error for carbon stable isotope ratios (‰) * sd_d15N: measurement error for nitrogen stable isotope ratios (‰) * cor_d13C: corrected carbon stable isotope ratios (‰) for comparison with the isotope ratios of isoscapes * cor_d15N: corrected nitrogen stable isotope ratios (‰) for comparison with the isotope ratios of isoscapes * sd2_d13C: uncertainty associated with the correction applied to enable comparison of eye lens carbon stable isotope ratios with isoscapes (‰) * sd2_d15N: uncertainty associated with the correction applied to enable comparison of eye lens nitrogen stable isotope ratios with isoscapes (‰) #### File: isoscape_raw.csv **Description:** Estimated isoscape data for carbon and nitrogen stable isotope ratios. ##### Variables * lon: longitude of the grid cell * lat: latitude of the grid cell * mean15N: predicted nitrogen stable isotope ratios (‰) * mean13C: predicted carbon stable isotope ratios (‰) * sd15N: prediction uncertainty of nitrogen stable isotope ratios (‰) * sd13C:  prediction uncertainty of carbon stable isotope ratios (‰) ## Code/software #### File: 0_make_isoscape.R **Description:** R code that uses isoscape predicted values and prediction errors (from **isoscape_raw.csv**) to generate a large number of random isoscapes. Before running, adjust the path in `setwd("isolog_tuna")` as appropriate, create a **data** folder in the same working directory, and place **isoscape_raw.csv** inside it. #### File: 1_isolog_tuna.R **Description:** R code that runs a state-space model to estimate the migration history of the target organism using crystalline lens isotope data (**SI_data.csv**) and isoscape data. Before running this script, first execute **0_make_isoscape.R** to generate random isoscapes. Also, before using this code, do the following: 1. Create a **data** folder in the working directory and place **SI_data.csv** inside it. 2. Create a **functions** folder in the working directory and place **model.R** and **map_utils.R** inside it. 3. Modify the path in `setwd("isolog_tuna")` as appropriate. #### File: map_utils.R **Description:** R code defining functions to plot isoscapes and visualize the results of the iso-logging. #### File: model.R **Description:** R code that defines a state-space model to estimate an organism’s movement history from eye lens isotope ratios and isoscapes.