DOI: 10.5061/dryad.ns1rn8pzx
title: Elevational differences in migration phenology of Lazuli Buntings do not support selection-based hypotheses for protandry

description: Documenting and understanding sex-specific variation in migratory
 phenology is important for predicting avian population dynamics. In
 spring, males often arrive on the breeding grounds before females
 (protandry), though whether these patterns result from fitness benefits
 versus sex-specific constraints on arrival timing remains poorly
 understood. Sex-specific variation in the timing of fall migration is less
 well-documented than in spring, in part because documenting fall
 departures is often limited by cryptic behaviors, lower vocalization
 rates, and shifting territory boundaries during this time of year. We used
 two years of high-resolution encounter data from radio-frequency
 identification (RFID)-equipped bird feeders to monitor the daily presence
 of male and female Lazuli Buntings (Passerina amoena) throughout the
 breeding season at a high and a low elevation site in Cache County, Utah,
 USA. These encounter data were used to estimate daily arrival and
 departure probabilities and to investigate possible differences in
 migration timing in relation to sex and elevation. At low elevation, male
 arrival (n=15) preceded female arrival (n=16) by approximately one week,
 consistent with previous research that has documented protandry in other
 migratory songbirds. At high elevation, however, no significant
 differences were found between male (n=19) and female arrival (n=6). In
 fall, we found little difference in departure dates between elevation or
 sex, or between years.  Our observations are most consistent with
 constraint-based hypotheses explaining protandry, possibly relating to
 sex-specific constraints operating during the non-breeding period. We
 additionally emphasize the need for quantifying uncertainty in
 phenological estimates and importance of addressing potential differences
 across demographic groups. 
description: Study Site and Species
 We monitored breeding Lazuli Buntings
 (<em>Passerina amoena</em>) within the Bear River Range of
 Cache County, UT, USA (41.8° N, -111.7° W). The Lazuli Bunting is a
 sexually dimorphic, generalist songbird that breeds throughout the western
 United States. This species is a moderate-distance migrant, travelling to
 west-coastal Mexico via the Sonoran region, where it is thought to
 complete its annual pre-basic molt (Greene et al. 2020). Though they are
 highly omnivorous, Lazuli Buntings readily visit bird feeders when stocked
 with white millet (<em>Panicum miliaceum</em>), making them an
 excellent candidate species for use of RFID resighting. We collected data
 from April to September, during 2019 and 2020 on two study plots
 established at low (1450 m) and high (1930 m) elevation, located 24.5 km
 apart within the same canyon. Both study sites provide suitable habitat
 for Lazuli Buntings, with no observable differences in sex ratios or
 breeding densities. The sites did vary in their vegetative composition,
 with the low-elevation site being dominated by non-native grasses and
 shrubs, while native grasses, sagebrushes, and quaking aspen
 (<em>Populus tremuloides</em>) characterize the high-elevation
 site. Field
 Methods At each site, we
 established six RFID-enabled bird feeders in a 2 x 3 grid, spaced 75 m
 apart. Each feeder assembly was mounted on a stationary pole and consisted
 of a feeder body, two antennas serving as perches, an electronics box
 housing a battery and circuit board, and a solar panel for power. All
 feeders were maintained with white millet from before spring arrival
 (mid-April) through fall departure (mid-September).  We used a combined RFID reader and
 data-logger similar to that described by Bridge et al. (2019). Each
 printed circuit board contained an RFID module (UB22270, Atmel
 Corporation, San Jose, CA, USA), a microprocessor (PIC16F688, Microchip,
 Chandler, AZ, USA), a memory module (24LC512, Microchip, Chandler, AZ,
 USA), a real-time clock (DS1307, Maxim Integrated Products, Sunnyvale, CA,
 USA), and an SD card slot for data storage. The circuit board’s
 microprocessor emits a carrier wave signal via two loop antennas mounted
 as perches on the feeder. When passive RFID tags are within range (~3–5
 cm), the carrier wave energizes copper coils within the tag, inducing
 emittance of a unique 10-ASCII character code. This code is then received
 and transmitted by the antenna to the RFID module, where it is interpreted
 and stored to the onboard SD card with the unique ID and time
 stamp. Loop antennas were
 custom-built from coiled wire to produce a target inductance of 1.350 mH,
 the optimal inductance for RFID detection using this system. Each antenna
 coil was then wrapped in electrical tape to protect it from moisture and
 UV exposure. To ensure antennas and RFID components remained operational,
 we tested each feeder with a designated RFID tag two to three times per
 week throughout the season.  We captured and subsequently recorded the
 presence of  Lazuli Buntings using non-invasive RFID detections from our
 feeders at both study sites from mid-April through late-September in 2019
 and 2020. Buntings were captured using a combination of mist nets and
 feeder traps. Upon capture each bird was aged and sexed based on criteria
 from Pyle (1997) and fitted with a U.S. Fish & Wildlife Service
 band and a colored 2.6mm diameter plastic RFID band containing a passive
 integrated transponder (PIT) tag (Eccel Technology, Leicestershire, U.K.).
 Like conventional color bands, the RFID bands are lightweight (0.092g) and
 are not expected to influence bird behavior or performance. For each bird,
 we recorded standard morphological measurements, mass, fat, breeding
 characteristics, and molt status. Once banded, each RFID-marked bird was
 passively recorded each time it used any feeder at our sites, producing a
 nearly continuous-time encounter record throughout the breeding
 season. Analysis We used the
 package <em>feedr</em> (LaZerte 2020) in R (4.0.3; R Core Team
 2019) to organize, clean, and isolate individual feeder visits from the
 raw RFID data. Single tag reads or reads of the same tag within 30 s of
 each other were considered a single visit. This threshold was selected
 based on field observations of 1) birds displaying weak aggression at
 feeders, 2) displaced feeding individuals being only displaced temporarily
 by others without leaving the immediate feeder location (i.e. perching on
 top or adjacent to the feeder), and 3) multiple feeders were closely
 spaced, allowing for most marked bird to select from multiple feeders if
 exclusion were to happen from territorial individuals. We then used
 the <em>feedr</em> output to create a daily detection history
 of number of observed feeder visits by each individual across the study
 sites.  We divided the
 birds into four groups for the purpose of exploring migratory phenology:
 males and females at high and low elevations. To isolate locally breeding
 birds and ensure transient individuals did not influence our phenology
 estimates, we only included individuals for departure analysis if they
 were of known sex, were marked prior to 15 July of a sampling year, and
 were recorded using a feeder ≥10 days during the core breeding period (15
 June–15 July). For the arrival model, individuals were included in the
 analysis if they were marked the prior season and were detected ≥10 days
 between 15 May and 15 July of the current season. We used two seasons of
 data in the departure model and a single season of data in the arrival
 model. We modelled arrival and departure separately because we did not
 have sufficient data to estimate survival probabilities between
 seasons. Observation
 Model We modeled the daily
 number of visits recorded for each individual,
 denoted <em>y<sub>i,t</sub></em>, using a Poisson
 point process model: <em>y<sub>i,t</sub></em>  ~ <em>Poisson </em>( λ<em><sub>i,t</sub></em>  <em>z<sub>i,t</sub></em> )                 (1) where λ<em><sub>i,t</sub></em> is the expected number of visits for individual <em>i</em> on day <em>t</em> and <em>z<sub>i,t</sub></em> is the true status (present or absent) of individual <em>i</em> on day <em>t</em>. We incorporated daily and individual-level random effects to account for temporal variation and individual heterogeneity in visitation rates: <em>log </em>( λ<em><sub>i,t </sub></em>) =  µ<sub>λ</sub>  +  γ<em><sub>t</sub></em>  +  ε<em><sub>i</sub></em>               (2) γ<em><sub>t  </sub></em>~ <em>normal </em>(0, <em>σ<sub>day</sub></em>) Ε<em><sub>i</sub></em> ~ <em>normal </em>(0, <em>σ<sub>ind</sub></em>) State Process Models Each individual <em>i</em> has a true status (present or absent) on each day <em>t</em> that is only partially observable, as individuals can be present at the study site but not using a feeder. To account for this form of imperfect detection, we modeled the latent state variable <em>z</em> as a Bernoulli trial. We modeled arrival as:   <em>z<sub>i,t</sub></em>  ~ <em>Bernoulli</em> ([1 -  <em>z<sub>i,t-1</sub></em>] α<sub>g[i],t-1</sub> +  <em>z<sub>i,t-1</sub></em>)                               (3) where α<em><sub>g</sub></em><sub>[<em>i</em>],<em>t</em>-1</sub> is the cumulative daily probability that an individual in group <em>g</em> arrives between days <em>t</em> and <em>t</em> - 1. This formulation ensures that once an individual arrives (z<em><sub>i,t</sub></em><sub> </sub>= 1), it must remain present for the remainder of the study period. We modeled departure similarly: <em>z<sub>i,t</sub></em>  ~ <em>Bernoulli</em> ([1 -  δ<em><sub>g</sub></em><sub>[<em>i</em>],<em>t</em>-1</sub>]   <em>z<sub>i,t-1</sub></em>)                        (4) where δ<em><sub>g</sub></em><sub>[<em>i</em>],<em>t</em>-1 </sub>is the cumulative daily probability that an individual in group <em>g</em> departs between days <em>t</em> and <em>t</em> - 1. In this formulation, once an individual departs (z<em><sub>i,t</sub></em><sub> </sub>= 0), it cannot be detected at the feeders for the remainder of the season. We modeled daily arrival and departure probability using survivorship retention curves. Following Pledger et al. (2009), we used a Weibull distribution to model the cumulative daily arrival and departure probability for each group: α/δ<sub>g,t</sub>  =  1 - <em>e^</em>( -[(t+1)/γ<sub>g</sub>]^κ<sub>g</sub>  +  [<em>t</em>/γ<sub>g</sub>]^κ<sub>g</sub> )                                         (5) where ?<em><sub>g</sub></em> is the scale parameter and ?<em><sub>g</sub></em> is the shape parameter for each demographic group. The Weibull distribution provides a flexible approach for modeling migration timing, allowing for a wide range of curves among groups depending on the values of the shape and scale parameters. In particular, ?<em><sub>g</sub></em> &gt; 1 results in accelerating daily arrival or departures rates (i.e., arrival/departure probability are initially low and then increase rapidly towards the end of the study period), whereas ?<em><sub>g</sub></em> = 1 implies constant arrival/departure rates and ?<em><sub>g</sub></em> &lt; 1 indicates decelerating arrival/departure rates (Pledger et al. 2009). The shape and scale parameters can be further used to quantify relevant summary statistics for each group, including daily arrival/departure probability densities and the mean, median, and variance of arrival/departure dates, based on the properties of the Weibull distribution. We modeled group-level variation in migration timing by treating the log of the Weibull parameters (<em>log</em>(?<em><sub>g</sub></em>), <em>log</em>(?<em><sub>g</sub></em>)) as a normally-distributed random effect with means µ<sub>?</sub>/µ<sub>?</sub> and standard deviations µ<sub>?</sub>/µ<sub>?</sub>. We estimated posterior distributions for each parameter using Markov chain Monte Carlo (MCMC) methods implemented in R using NIMBLE (NIMBLE Development Team 2020). We used zero-centered normal priors with standard deviation = 1.75 for all mean parameters (µ<sub>?</sub>, µ<sub>?</sub>, and µ<sub>λ</sub>) and half-normal priors with standard deviation = 1.75 for all standard deviation parameters (σ<sub>?</sub>, σ<sub>?</sub>, σ<sub>day</sub>, and σ<sub>ind</sub>). In our arrival model, we selected a more diffuse <em>normal</em>(0, 5) prior for µ<sub>?</sub>, because initial inspection of the data indicated that timing of first detections differed significantly across the elevational gradient, indicating a potentially wider spread in arrival phenology than departure. For each model, we ran three MCMC chains for 30,000 iterations with 5,000 sample burn-in and thinning by four. For the departure model, we initially estimated separate 2019 and 2020 parameters for each group to assess annual variation in departure phenology. However, no significant differences were observed between years (Table S1), so we pooled data from both years in our final analysis. Methods References: Bridge, E. S., J. Wilhelm, M. M. Pandit, A. Moreno, C. M. Curry, T. D. Pearson, D. S. Proppe, C. Holwerda, J. M. Eadie, T. F. Stair, A. C. Olson, et al. (2019). An Arduino-Based RFID Platform for Animal Research. Frontiers in Ecology and Evolution 7:257. Greene, E., V. R. Muehter, and W. Davison (2020). Lazuli Bunting (Passerina amoena). In Birds of the World (A. F. Poole and F. B. Gill, Editors). Cornell Lab of Ornithology, Ithaca, NY, USA. LaZerte, S. E. (2020). feedr: Transforming Raw RFID Data. NIMBLE Development Team (2020). NIMBLE: MCMC, Particle Filtering, and Programmable Hierarchical Modeling. Zenodo. Pledger, S., M. Efford, K. Pollock, J. Collazo, and J. Lyons (2009). Stopover Duration Analysis with Departure Probability Dependent on Unknown Time Since Arrival. In Modeling Demographic Processes In Marked Populations (D. L. Thomson, E. G. Cooch and M. J. Conroy, Editors). Springer US, Boston, MA, pp. 349–363. Pyle, P. (1997). Identification Guide to North American Birds, Part I: Columbidae to Ploceidae. Slate Creek Press, Point Reyes Station, CA, USA. R Core Team (2019). R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria.
description: R statistical
 environment. See the methods above for packages and usage.