DOI: 10.5061/dryad.m63xsj4cj
title: Selective disappearance based on navigational efficiency in a long-lived seabird

description: Whilst efficient movement through space is thought to increase the fitness
 of long-distance migrants, evidence that selection acts upon such traits
 remains elusive. Here, using 228 migratory tracks collected from 102 adult
 breeding common terns (Sterna hirundo) aged 3 – 22 years, we find evidence
 that older terns navigate more efficiently than younger terns, and that
 efficient navigation leads to a reduced migration duration and earlier
 arrival at the breeding and wintering grounds.  We additionally
 find that the age-specificity of navigational efficiency in adult breeding
 birds cannot be explained by within-individual change with age (i.e.
 learning), suggesting the selective disappearance of less navigationally
 efficient individuals.  This suggests that, at least in common
 terns, learning of navigational skills may be largely absent in adulthood,
 and limited to the pre-breeding phase of life where tracking is more
 difficult. We propose that selection might explain parts of the
 age-specificity of navigational performance observed in migratory taxa
 more generally; discuss the causes and evolutionary implications of
 variation in navigational traits and the selective agents acting upon
 them; and highlight the necessity of longitudinal studies when considering
 changes in behaviour with age.
description: <strong>Study population</strong>
   The common tern is a long-lived,
 migratory seabird (Becker and Ludwigs 2004). The data we present here come
 from a long-term study population of common terns located at the Banter
 See in Wilhelmshaven on the German North Sea coast (53°30’40” N, 08°06’20”
 E). Here, chicks have been ringed since 1980, while the presence and
 reproductive performance of individually marked adults has been monitored
 following a standard protocol since 1992. Hereto, 101 adult birds were
 caught and marked with individually numbered subcutaneously injected
 transponders, and, since 1992, all locally hatched birds have been ringed
 as well as marked with such a transponder shortly prior to
 fledging.   As part of the
 standard protocol, arrival date is monitored using antennae affixed to the
 walls of the colony (Moiron et al. 2024) and the colony site is checked
 three times a week to find nests and record laying dates, clutch sizes and
 egg sizes. During incubation, which is shared between partners, antennae
 that can read the individual transponder codes are placed around each nest
 to identify breeding individuals. Combined with further nest-checks to
 establish hatching, mark offspring and assess their growth and survival,
 these methods enable the documentation of individual life-history
 trajectories (e.g. Moiron et al. 2022; Zhang et al. 2015).
  
 <strong>Tracking</strong>
   Between mid-May and early July
 2016-2021, 228 tracks were recorded from 102 birds (2016 n = 24 (22
 retrieved); 2017 n = 36 (28 retrieved); 2018 n = 50 (40 retrieved); 2019 n
 = 54 (48 retrieved); 2020 n = 65 (54 retrieved); 2021 n = 54 (36
 retrieved); total retrieval rate of geolocators with data = 80.5%) were
 equipped with an archival light-level geolocator (Intigeo-C65, Migrate
 Technology, UK) attached to the leg using a 10 mm aluminium ring. The
 total mass of the ring, glue and geolocator was 1.6 g, which equalled 1.2%
 ± 0.1 SD of the body mass of the birds at capture, and did not have a
 detectable effect on behaviour, reproductive performance or survival
 (Kürten et al. 2019). The geolocators were set to sample ambient light
 intensity every minute, with the maximum light intensity being stored
 every five minutes (‘mode 10’ on Migrate Technology devices).
   <strong>Track
 processing</strong>  
 Upon return from migration, i.e. between mid-May and early July
 2017-2022, birds were retrapped to retrieve their geolocators carrying the
 archived light-level data, from which their migratory trajectories were
 estimated using the R package ‘FLightR’ (Rakhimberdiev et al. 2017).
 Device failure mid-trip notwithstanding, we have both a spring and autumn
 trajectory from each bird (in total: 228 tracks of 102 individuals, with
 228 autumn and 184 spring trajectories).
   To analyse these trajectories, we
 extracted two daily positions using the ‘run.particle.filter’ function and
 used the ‘stationary.migration.summary’ function to detect
 individual-specific migration periods based on the dates of arrival to,
 and departure from, the breeding colony and wintering area. In essence,
 this involved using the posterior probability of movement for each day’s
 calculated position to ascertain whether the bird was likely to be moving
 (movement probability &gt; 0.4). Based on these probabilities,
 geolocator fixes were then assembled into periods of movement and
 non-movement, from which the breeding and wintering periods could be
 calculated. Spring and autumn migratory periods were defined as the
 intervening periods (for more details, see Kürten et al.
 2022).   Once the beginning
 and end of each migration period were estimated for each track, we sought
 to remove any stopovers, during which birds would not necessarily exert a
 navigational preference, by removing GLS fixes where the distance moved
 was estimated at &lt;100 km between fixes (removing n = 3451 fixes).
 This was meant to ensure that all fixes included in our dataset described
 the behaviour of birds moving in directed flight, and robustness to the
 selected cut-off value was tested via reanalysis with the cut-off value
 set to 50 km (removing n = 4575 fixes; see Table S4).
   Autumn migration occurred between the
 24th of July and 15th of October, whilst spring migration occurred between
 the 10th of February and 9th of May, with birds moving from their colony
 in Northern Germany to sites distributed between West and Southern Africa
 (see Kürten et al., 2022). Common terns took remarkably direct migratory
 routes – characterised by relatively few stopovers – with autumn and
 spring migration lasting on average 17.95 (±14.60 days standard deviation)
 and 27.56 days (±18.94 days standard deviation), respectively.
   <strong>Analysis of age-specific
 navigational performance</strong>
   Given that GLS positional estimates
 are inferred using light levels, they are inherently less accurate than
 those obtained using other positioning technologies (e.g. GPS). However,
 since heavier GPS devices have been associated with changes in at-sea
 behaviour in similar taxa (e.g. Gillies et al. 2020), we assumed that GLS
 devices were best-suited to our purpose. The error inherent to light-level
 geolocation comes from device shading and/or electrical errors in the
 device itself, and this error can manifest in one of two ways: (i) noise –
 indiscriminately affecting all devices, increasing the number of false
 negatives (e.g. noise obscuring a correlation between GLS positional
 information and a predictor), or (ii) bias – impacting devices
 non-randomly, causing false positives (e.g. weather-induced device shading
 causing variation in positional estimate, leading to a spurious
 correlation between cloud cover and distance travelled; Lisovski et al.
 2018).   To minimise the
 effect of noise, we used the FLightR algorithm to process our GLS data, as
 it is robust to equinox error and leads to substantially less overall
 error than more traditional thresholding methods (c.250 km error per fix;
 Halpin et al. 2021; Rakhimberdiev et al. 2017). In addition, our large
 sample size (228 migratory tracks from 102 individual terns) secured
 statistical power. For positional error to manifest as bias and in turn
 affect our study, the causes of positional error (i.e. device
 shading/electrical error) would have to correlate with age.  To assess
 whether such bias was present in our dataset (i.e. whether birds of a
 given age were biased in their positional estimates), we tested whether
 the positional estimates of known-location birds varied with age. This we
 did by assessing whether longitude and latitude varied with age over the
 months of June and July – when all birds tracked were known to be at the
 breeding colony – using a linear mixed effects model (see below). Given
 that this wasn’t the case (see supplementary material), we assume our
 conclusions to be unaffected by bias.  
 Characterising navigational performance without knowing a
 bird’s preferred migratory route a priori is challenging, since whilst
 many birds take a direct migratory route (e.g. Prochazka et al. 2018;
 Schmaljohann et al. 2012), some do not (e.g. Alerstam 2001; González-Solís
 et al. 2009; Guilford et al. 2009; Lisovski et al. 2021; Mellone et al.
 2013). We chose to characterise navigational performance for each GLS fix
 of each tern as the instantaneous deflection between the instantaneous
 migratory trajectory (i.e. the direction the bird is currently going in)
 and the goal (Padget et al. 2018; Wynn et al. 2020a). As such,
 instantaneous deflection was calculated twice-daily for each bird, and did
 not assume that the bird in question was following the Great Circle route
 (“beeline”). Whilst it is true that ‘improvements’ in navigational
 performance with age might reflect differing motivations to navigate
 efficiently towards the goal, we reasoned this was the less likely
 explanation of any change in performance since all birds were established
 breeders and, at least in spring, under selection to arrive and breed
 early (Dobson et al. 2017; Moiron et al. 2024).
   For autumn migration the destination
 was considered to be the highly repeatable individual-specific wintering
 site (Kürten et al., 2022), whilst in spring the destination was
 considered to be the breeding site. Deflection angles were expressed as an
 absolute deflection between 0 and 180o, with 0o representing no difference
 between the bird’s trajectory and the beeline between the observed
 position and the destination, and 180o meaning complete reversal (Figure
 2). Whilst the response variable was angular, it was not circular, since
 the beginning and the end of the scale were not the same value. As such,
 we used linear rather than circular statistics to analyse our
 data.   To assess the effect
 of age on navigational performance, we used the R-package ‘lme4’ (Bates et
 al. 2015) to run a linear mixed effects model with the GLS-fix-specific
 absolute instantaneous deflection as a response variable assuming a
 Gaussian error distribution, and with track identity, nested within
 individual identity, as random effects to account for pseudoreplication
 (Padget et al. 2018). Because multiple birds of different ages (expressed
 in years since hatching) were tracked per season – and no birds were kept
 in the study for its entire duration – year-on-year changes in migration
 caused by the environment were unlikely to confound with age in any
 analysis.   Age was
 partitioned into an ‘average age’ and ‘delta age’ component (van de Pol
 and Wright 2009), whereby an individual's average age was defined as
 the average of all ages at which it was tracked, while delta age was
 defined as the difference between an individual's age for a given
 track and its average age (i.e. delta age = age − average age). When both
 were added as a covariate to the model, average age reflected whether
 birds tracked at different ages differed in their navigational performance
 (i.e. an among-individual pattern), whilst delta age represented any
 within-individual change with age (e.g. learning; van de Pol and Wright
 2009). A significant effect of delta age would suggest that learning (or
 senescence, depending on the direction of the effect) explained changes in
 navigational performance with age, whilst a significant difference between
 the effects of average age and delta age would indicate selective
 disappearance. A graphical illustration of this principle can be found in
 Figure S1. We also included the interaction between average age and delta
 age to test for non-linear effects of age, because learning could
 decelerate as birds grow older. Finally, we added season as a 2-level
 class variable (autumn and spring), both as a main effect and in
 interaction with all age components, in order to test whether
 (age-specific) navigational performance differed between autumn and spring
 migration. Since both average age and delta age were on the same scale,
 and there were no other predictors, we did not mean-centre or normalise
 the variable.   Significance
 was assessed using likelihood ratio tests, comparing the hypothesis model
 to a null model that was identical to the hypothesis model save for the
 exclusion of the interaction/term under investigation. Effect sizes and
 bootstrapped 95% confidence intervals, which were estimated using the
 R-package ‘arm’, are reported in Tables 1 and S1. The full model was
 simplified, allowing for the creation of a minimally adequate model with
 more immediately interpretable effect sizes. Since the three-way
 interaction between season, average age and delta age; the two-way
 interaction between average age and delta age; the two-way interaction
 between season and average age; and the two-way interaction between season
 and delta age were non-significant, these terms were excluded during a
 step-wise backwards elimination process (Table 1). The resulting minimally
 adequate model included the main effects of season, average age, and delta
 age. Delta age was included irrespective of significance so as the
 difference between the effects of the delta and average age components
 could be assessed. This we did by estimating the difference between both
 effects and assessing whether the associated bootstrapped 95% Confidence
 Intervals overlapped zero (van de Pol & Wright 2009).
   We proceeded to re-run all analyses
 using only the 177 tracks (i.e. 78% of all tracks). We did so because
 birds migrating further south seemingly followed different, less efficient
 routes (see Figure 1), such that any difference in navigational
 performance might represent differences in destination rather than
 performance (see Tables S1 and S2).    
 <strong>Analysis of the effect of navigational efficiency
 on migratory timing and duration</strong>
   To test whether the duration of
 migration, or the resulting arrival date (known to be under selection, at
 least in spring; see Moiron et al. 2024), varied with instantaneous
 deflection, for each route we regressed the overall time spent migrating
 (i.e. the time of the end of migration minus the time of migration onset,
 in days; assuming a Gaussian error distribution) or the Julian date of
 arrival against the average instantaneous deflection observed over the
 route, including individual identity as a random effect and season as a
 fixed effect.   As above,
 significance was assessed using likelihood ratio tests comparing the
 hypothesis model to a null model that was identical to the hypothesis
 model save for the exclusion of the interaction/term under investigation.
 All effect sizes and bootstrapped 95% confidence intervals are reported in
 Tables 2 and S2.
description: # Selective disappearance based on navigational efficiency in a long-lived
 seabird
 [https://doi.org/10.5061/dryad.m63xsj4cj](https://doi.org/10.5061/dryad.m63xsj4cj) ## Description of the data and file structure The common tern is a long-lived, migratory seabird (Becker and Ludwigs 2004). The data we present here come from a long-term study population of common terns located at the Banter See in Wilhelmshaven on the German North Sea coast (53°30’40” N, 08°06’20” E). Here, chicks have been ringed since 1980, while the presence and reproductive performance of individually marked adults has been monitored following a standard protocol since 1992. Hereto, 101 adult birds were caught and marked with individually numbered subcutaneously injected transponders, and, since 1992, all locally hatched birds have been ringed as well as marked with such a transponder shortly prior to fledging. As part of the standard protocol, arrival date is monitored using antennae affixed to the walls of the colony (Moiron et al. 2024) and the colony site is checked three times a week to find nests and record laying dates, clutch sizes and egg sizes. During incubation, which is shared between partners, antennae that can read the individual transponder codes are placed around each nest to identify breeding individuals. Combined with further nest-checks to establish hatching, mark offspring and assess their growth and survival, these methods enable the documentation of individual life-history trajectories (e.g. Moiron et al. 2022; Zhang et al. 2015). ### Files and variables #### File: comics\_all\_birds\_seasonally\_flexible\_load\_script.R **Description:** This R script will load in the associated R data file, and is set up to faithfully replicate the data processing used in this analysis. #### File: terns\_with\_2022.rdata **Description:** This rdata file contains all tracks used in this analysis, and is designed to work with the attached script. ##### Variables * *position_date_time* is variable containing the timestamp associated with each GLS fix in POSIXCt format * *position_lat* is latitude in degrees * *position_lon* is longitude in degrees * *individual* is the individual ID of a tern  * *track_id* is the ID of a track (i.e. a given individual in a given year) * *year* is the year of tracking * *age* is the age at deployment in years * *sex* is the sex of the tracked bird (male/female) * *departure_colony* is when the bird left the colony in Wilhelmshaven in POSIXCt format * *arrival_wa_final* is when the bird left the wintering site in POSIXCt format * *departure_wa_final* is when the bird left the wintering site in POSIXCt format * *arrival_date_geo* is when the bird arrived back in Wilhelmshaven in POSIXCt format ## Code/software Code is included as a separate file.