DOI: 10.5061/dryad.6djh9w14m
title: Data for: Using radio frequency identification (RFID) technology to characterize nest site selection in wild Japanese tits (Parus minor)

description: Selecting a suitable nest site is critical to the survival and
 reproduction of birds. Prospecting allows individuals to gather
 information on the local quality of potential future breeding
 sites, which may help them make the best nest
 site selection decision. However, few studies have
 focused on the direct links between the prospecting activity of
 breeders and subsequent nest site selection. In this study, we
 investigated the prospecting pattern of Japanese tits (Parus minor) during
 the pre-breeding period of the first breeding attempt and whether nest
 site characteristics influence their nest box visiting
 behaviour and occupied nest site. We used radio frequency
 identification (RFID) to track the movements of Japanese
 tits visiting nest boxes and compared nest site characteristics
 between visited and unvisited (control) nest boxes, as well as
 between visited and occupied nest boxes. We found that Japanese
 tits started visiting nest boxes approximately 20 days before
 breeding, visited an average of 6 nest boxes and eventually chose the most
 visited nest box for breeding activities. Japanese tits were more
 likely to
 visit nest boxes that had less canopy cover and lower shrub density but a greater total number of surrounding trees and ultimately chose breeding nest boxes with a smaller entrance inclination, in nesting trees with a larger diameter at breast height (DBH) which were surrounded by trees with a larger DBH. Our results suggest that Japanese tits visit several potential breeding sites before choosing breeding nest boxes and that nest site characteristics can influence their prospecting activity and nest site selection.
description: <em>Study area and study species</em> The study area was located in the eastern
 Changbai Mountains in the Zuojia Nature Reserve (44°1′–45°0′ N,
 126°0′–126°8′ E), situated in Jilin Province, northeastern China. The
 total area of the reserve is ~5,544 ha. The area is hilly in the
 transition from the Changbai Mountain sub-region to the western plain,
 with an elevation of 200-530 m. The average temperatures in March, April,
 and May in the Zuojia Natural Reserve area from 2005 to 2021 were -1.01 ±
 0.26℃, 8.16 ±0.24℃, 15.38 ± 0.18℃, respectively. Local temperatures were
 obtained from Jilin Suburban Weather Station, 44 km from the study area
 [Chinese National Meteorological Center of CMA (http://eng.nmc.cn/),
 weather station No. 54172]. The main forest type in the study area is
 secondary broad-leaved mixed forest, and the forest age is generally 50-60
 years old. The main tree species are <em>Quercus
 mongolica</em>, <em>Sophora
 japonica</em>, <em>Ulmus japonica</em>, <em>Betula
 davurica</em>, <em>Fraxinus
 mandshurica</em>, <em>Tilia mandshurica</em>,
 <em>Pinus sylvestris</em>, <em>Pinus
 koraiensis</em>, <em>Populus davidiana</em>, and
 <em>Pinus massoniana</em> (Deng and Gao 2005, Fan et al.
 2021). The main shrub species are <em>Rosa dahurica</em>,
 <em>Spiraea salicifolia</em>, <em>Rosa d
 oreana</em>, <em>Sorbaria sorbifolia</em>, and
 <em>Lonicera maximowiczii</em> (Deng and Gao 2005). Since
 2009, we have been providing approximately 450 artificial nest boxes for
 secondary cavity-nesting birds, such as Japanese tits and Eurasian
 nuthatch (<em>Sitta europaea</em>), and have carried out many
 types of research in this area (e. g., Yu et al. 2017, Shen et al. 2021, E
 et al. 2021). The study was undertaken from February-July in 2020 and
 2021. Nest boxes had inner dimensions of 12 cm × 12 cm × 25 cm, an
 entrance hole with a diameter of 5 cm with its lower end 155 mm from the
 floor. All nest boxes were attached to the trees and were
 approximately 2-3 m above the ground, with each nest box 30-50 m apart.
 The tree species for hanging the boxes and the orientation of them were
 randomly selected. We routinely cleaned out old nest materials from
 artificial nest boxes before the initiation of the breeding season. The
 Japanese tit population in our area began occupying the territory in early
 March, adding nesting materials to the nest box in early April, and laying
 eggs in mid-to-late April. We checked the nest boxes at least once a week
 to record breeding attempts and reproductive data (e.g., egg-laying date,
 clutch size, hatching date, brood size, number of fledglings) of Japanese
 tits (Yu et al. 2017). <em>Tagging and monitoring
 birds</em> In the
 pre-breeding period (from mid-March to early April), all nest box
 entrances were fixed with a circular antenna (approximately 4.2 cm in
 diameter), which was connected to a custom-made RFID reader (125-kHz,
 Guangzhou Yongjin Intelligent Technology Co., Ltd., Guangzhou, China). The
 reader recorded every time a bird equipped with a passive integrated
 transponder (PIT) tag passed through the nest box entrance. The unique
 10-digit identification code and the current date and time to the seconds
 were recorded in a log file. PIT tags do not have their own power source
 and, therefore, can only be detected within the range of the antenna (~2–3
 cm). The readers were powered by a lithium battery (3.7 v, 10,000 mAh) and
 solar panels and could also be powered via a USB connection
 charger. We trapped the
 adults in a cage using song playbacks of local males in the pre-breeding
 period. The cage was designed to include two levels, the upper level with
 plenty of food, water, and a small wooden stick and the lower level with a
 loudspeaker (Royqueen M300, Shenzhen, China) continuously playing the song
 of the Japanese tits to attract conspecifics. The wooden stick held the
 lid partially open; when the birds were attracted to the food and stepped
 on the stick, they were locked in the cage. This method of capture does
 not harm the birds. We continuously monitored the trapping equipment, and
 as soon as birds were trapped, we immediately removed them, and they were
 sexed and tagged by unique PIT tags. Handling time averaged 2 min per
 individual after which they were released at the location of capture. We
 did not attempt to capture birds during inclement weather. The PIT tags
 were attached to especially made plastic blue rings with an internal
 diameter of 2.6 mm. The weight of the tag (0.45 g) was approximately 3% of
 the bird's body weight, which is well below the recommended
 5%-of-body-weight criterion for flying animals (López-López 2016). The PIT
 tag number was read and recorded before and after the placement using a
 commercial handheld reader. No birds were injured or suffered long-term
 effects in the course of PIT tagging or RFID monitoring. <em>Nest site
 characteristics</em> We
 measured 13 nest site characteristics of the nest boxes visited (including
 those eventually occupied) by Japanese tits and corresponding randomly
 selected control nest boxes (not visited or occupied by any birds). X. L.
 was responsible for the measurement of nest site characteristics. In
 addition, two researchers, L. W. and Y. Y. assisted with the nest site
 characteristics after being trained by X. L. to standardize the
 measurements to reduce human error. The detailed measurements for all
 variables are described in Table 1. We located the longitude and
 latitude of nest boxes using handheld GPS (Garmin Intl. Inc., Olathe,
 KS). <em>Statistical
 analysis</em> To assess
 the influence of the 13 nest site characteristics on nest box
 visitation rate, we used a generalized linear mixed model (GLMM) with a
 logit-link and binomial error distribution (control nest box = 0, visited
 nest box = 1; Betts et al. (2008), Olah et al. (2014)). Since the data
 were collected during two years (2020 and 2021), “year” was added as a
 random variable. To avoid pseudo-replication, we also added the identity
 of the visited/control nest box as a random variable. To determine the
 influence of the 13 nest site characteristics on the nest box selection of
 Japanese tits, we used conditional logistic regression (visited but
 rejected nest box = 0, occupied nest box = 1), where the identity of the
 Japanese tit was the grouping variable (Thomas and Taylor 2006, Hartman et
 al. 2016, Dyson et al. 2019). Before model development, we screened
 explanatory variables for strong correlations (Menard 2002), and no
 strongly correlated explanatory variables were found (all explanatory
 variables displayed Pearson’s correlation coefficient &lt; 0.65 and
 variance inflated factor (VIF) &lt; 2) (O’brien 2007, Dyson et al.
 2019). Among the 13 nest site characteristics, only the orientation and
 tree species were categorical variables, while the other 11 were
 continuous variables. Then, we standardized all continuous variables prior
 to analysis (subtracted the mean and divided by the standard deviation),
 resulting in the mean for each predictor equalling 0 and the standard
 deviation equalling 1. In all
 analyses, we used Akaike’s information criterion (AIC) to perform model
 selection. We ranked competing models using ΔAICc and selected the
 top-ranked model with the ∆AICc ≤ 2 (Burnham and Anderson 2002). Then,
 among the selected models, we choose the model with the fewest variables
 as the best model. If there was a significant effect of the categorical
 variables (orientation and tree species), we further performed Tukey’s
 honest significant difference (HSD) post hoc pairwise comparisons using
 the ‘lsmeans’ package to determine differences between categorical
 variables (Lenth 2016) and used the Bonferroni correction to correct the
 P-values. We evaluated model performance by calculating the area under the
 curve (AUC) of the receiver operating characteristic (ROC) for the best
 model (Fielding and Bell 1997). We considered a model with an AUC score of
 0.7–0.9 to be a good predictor.
description: We performed all analyses in the program R (version 4.2.1). The
 GLMMs were implemented using the ‘lme4’ package (Bates et al.
 2015), conditional logistic regression was performed by the ‘survival’
 package (Therneau 2015), and the multi-model inference package ‘MuMIn’ was
 used for model selection. The results were visualized using
 ggplot2 (Wickham and Chang 2022).