Methods
Study site
The study took place over three consecutive non-breeding periods, hereby
referred to as year1 (November 2017 – April 2018), year2 (September
2018 – April 2019), and year3 (November 2019 – March 2020) at a guinea
savannah site on the Jos Plateau, Nigeria in West Africa (09°52’N,
08°58’E). This region experiences single pronounced wet and dry seasons
lasting six months each, from May to October and November to April,
respectively. Sites were primarily open scrubland with different and
varying degrees of anthropogenic activities, e .g . farming,
livestock grazing, tin mining, and fires (Hulme & Cresswell, 2012).
These sites represent typical African dynamic habitats, where
anthropogenic activities are constant and continuously changing
throughout the year.
Mist-netting and
resightings
Birds were captured using 9m, 12m, and 18m x 2.5m 5-shelf (16 x 16mm
mesh) mist nets and conspecific playback. During year1, nets were set up
in the morning between mid-November 2017 and mid-February 2018 (mean of
four nets per day, open for 2h 50m), totalling 70 visits. In year2 nets
were set up in the morning and/or evening from late October 2018 to
mid-April 2019 (mean of 4.5 nets per day for 3h 24m), totalling 69
visits. Few additional birds were caught in year3 between mid-November
2019 and mid-February 2020 but were excluded from return rates and
between-years site fidelity analyses. All individuals were sexed as
either female, male or unknown, and aged as either first-year, adult, or
unknown (Svensson, 1992). Each individual was given a unique combination
of coloured leg rings (three colour rings and a metal ring). In total,
212 individuals were colour-ringed in year1, 115 individuals in year2,
and 10 in year3. This work was conducted under the ethical guidelines of
the AP Leventis Ornithological Research Institute Scientific Committee
and all methods were approved by the School of Biology Ethics Committee
of the University of St. Andrews (SEC17028).
Resightings were carried out at least once a week between sunrise and
~1030 hrs and/or between ~1500 hrs and
sunset throughout the fieldwork period. Two observers undertook all
observations. We interspersed starting points to avoid biases as a
product of the time of day and air temperature. Resightings were not
carried out during days of heavy rain. Once an individual was detected
we proceeded to identify its complete colour combination using 10 x 40
binoculars. GPS points were recorded with a Garmin eTrex10™ GPS where
individuals were first detected and/or captured. Due to the skittish and
shy behaviour of Whitethroats, conspecific playback was used. In some
cases, individuals were first detected and playback was then used to
help reveal the complete colour-combination. In most cases, however,
when there were no signs of activity, playback was used before
detection. This did not seem to induce any significant movement in
individuals, and we believe that most recorded GPS points reflect
unbiased locations where the individuals would be without any
interaction with observers. We tried to spend the same effort resighting
all individuals, but we acknowledge that this may not have been always
the case. 135 individuals were seen at least once after capture. Because
of the high resighting effort, we are confident that departure months
and site persistence were determined accurately.
Radio tag deployment
Between 25 October 2019 and 28 November 2018, 11 individuals were fitted
with “LifeTags™”, a 0.45 g solar-powered and battery-free radio
transmitter from Cellular Tracking Technologies™. Tags were attached to
birds’ backs using an elastic leg-loop harness (Rappole & Tipton,
1991). Devices weighed approximately 0.51 g with the harness,
corresponding to 3.4% (3.2 – 3.8%) of an individual’s body mass. As
individuals were fitted with radio tags, an effort was made to seek them
at least twice a week after tag deployment until 8 December 2019. All
birds were observed for at least three days after tag deployment. When
individuals were detected, efforts were made to observe and corroborate
its ring combination. GPS coordinates were recorded where individuals
were first seen or heard or when detection was strong. To determine
whether radio tags had any negative effect on individuals, the residency
period (number of days between when an individual was caught and the
last time it was detected) and return rates (proportion of individuals
that returned the following non-breeding period) was compared between 11
radio-tagged individuals and 11 randomly selected control birds, ringed
during the same period. No significant differences were found regarding
residency periods (F(1,20) = 0.05, p = .82) or
return rates (χ² = 0.26, df = 1, p = .61) between radio-tagged
individuals and controls.
Detection probabilities
The probability of detecting an individual directly affects how we
calculate and categorise site persistence. Therefore, to estimate
detection probability we used multiple datasets and methods.
- Manually : Detection probabilities were calculated by dividing
the number of times a bird was detected (number of encounters) by the
total visits to its home range between its first detection (excluding
the date it was ringed) until its last detection for each year. We
used data obtained from individuals that we knew were present at the
study site during each visit (i .e . obvious long-term
winter residents, see below) to be certain that their non-detection
was due to detectability factors and not due to absence or death. This
assumes that birds did not leave their home range at any time and that
all birds, if present, had the same probability of detection. We used
information collected from 20, 16, and 15 individuals during years 1,
2, and 3, respectively. All data were analysed separately by year and
returning individuals were included in every year they were detected:
excluding them would otherwise bias estimates by preferentially
sampling first winter birds.
- MARK : With the same data, we proceeded to calculate detection
probabilities using Cormack–Jolly–Seber (CJS) models in MARK
software (White & Burnham, 2009). CJS models estimate both apparent
survival (φ ) and detection probability (p ), where the
former is the probability that an individual survives from one
sampling occasion to the next, and the latter is the probability that,
given that the individual is alive and in the sample, it is
encountered (Hammond, 2018). Given that we used capture histories from
individuals which we knew were present and alive (φ = 1), we
were only interested in obtaining the detection probability for each
year. We assumed that detection was constant throughout all encounters
(φ (.)p (.)).
- Radio tags : Detection probabilities were calculated for three
radio-tagged individuals that were detected at least during three
visits in year2. Every time a radio-tagged individual was detected
with the antenna, we proceeded to find it in the same manner that we
would normally do during resightings. We then estimated detection
probabilities by dividing the number of visits during which an
individual was detected in ‘resighting’ conditions by the total number
of visits that same individual was detected with the radio tag
antenna.
The final overall detection probability was obtained by averaging all
seven estimates: detections obtained manually and in MARK for all three
seasons (total of six detections), and a detection obtained through
radio-tagged individuals.
Site persistence
Once established that individuals undertook different residency
strategies (see Appendix 1), we estimated the number of days individuals
spent in the study area (days between when individuals were first and
last detected). To facilitate further comparisons, however, individuals
were grouped into residency categories as seen in Table 1. Individuals
detected across more than one year were categorised independently each
year.
Between-years site
fidelity
Return rates were estimated by dividing the number of individuals that
were seen in year i +1 by the total number of individuals ringed
in year i . To determine the degree of between-years site fidelity
of individuals that returned for at least two non-breeding seasons –
how far an individual moved from year i to year i +1 – we
calculated the centroid coordinate for each individual in each year and
estimated the distance between centroids using the
“distHaversine ” function from the “geosphere ” package
version 1.5.10 in R (Hijmans, 2019; Fig. 1). Individuals were grouped
into group A, individuals detected in years 1 and 2, group B,
individuals detected in years 2 and 3, and group C, individuals detected
in years 1 and 3 but not in year2. Individuals that were seen during all
three seasons were not excluded from the analysis and were added to
groups A and B.
Departure dates
We tested departure date repeatability of individuals seen for at least
two non-breeding periods. Year3 birds were excluded from this analysis
because resightings that year ended earlier, and final resightings were
not likely to reflect true departure dates. We excluded records of all
birds that were seen after 25 February (three weeks before the end of
observations) to exclude birds that were highly likely to have not left
before our last resighting effort of that year. We estimated
repeatability using the “rpt” function in the “rptR ”
package (Stoffel et al., 2017). This uses a linear mixed model framework
where the groups compared for repeatability are specified by a random
effect (i .e . individuals). Confidence intervals
were estimated by running 1000 bootstraps. We calculated repeatability
for adults and first-year birds, as well as for each residency category
(i .e . long-term, short-term, and passage birds).
Statistical analyses
All data were analysed using R version 3.6.3 and RStudio version 1.1.456
(R Core Team, 2020) and a statistical significance level of p< .05 was chosen to reject the null hypotheses.
Detection probabilities
To compare whether detection probabilities were constant between
non-breeding periods and methods, General Linear Models (GLMs) were
performed.
Site persistence
We performed GLMs to understand whether site persistence, defined as the
number of days an individual was present and detected in the area,
varied across years, age, and sex. Birds that could not be aged or sexed
were excluded from models that included these variables as predictors.
First-year Whitethroats are difficult to sex accurately (Waldenström &
Ottosson, 2000), so models using sex as an independent variable only
include adults. Because of this, modelling for the effects of age and
sex in residency periods was undertaken separately. Data from year3 were
excluded from these analyses as well as those individuals whose age and
sex were unknown.
We used a model averaging approach for models that had the same sample
size using the “dredge ” and “model.avg ” function from
the “MuMin ” package in R (Barton, 2020). This procedure entails
carrying out all possible models from a base model (i .e .
‘days ~ age + year’ and ‘days ~ sex +
year’), and calculating a weighted average of parameter estimates, such
that parameter estimates from models that contribute little information
about the variance in the response variable are given little weight
(Grueber et al., 2011).
Between-years site
fidelity
Chi-squared tests (χ2) were performed to detetermine
the effects of year, age at year i (‘previous age’), sex, and
residency at year i (‘previous residency’) on return rates. A
model averaging approach was also undertaken to explore whether the
distance moved from one year to another was dependent previous age, sex,
and previous residency (base model: ‘dist ~ preage + sex
+ group + preres + preage*preres + preage*group’). All birds that could
not be aged were excluded from models that included age as a predictor.
Residency repeatability
To explore whether individuals remained for similar periods across
different years, or whether they repeated residency categories the
following years, we estimated the percentage of individuals that
remained (or changed) in each residency category. We carried out a
linear model and estimated the correlation between the number of days
spent in year i with the number of days spent in year i +1.
Departure dates
To describe population variation regarding departure dates, we pooled
all observed dates across the first two years from individuals that left
after January in a respective year. We then calculated the difference
between each date and the date of earliest sighting and calculated the
mean, standard error (se), and range. To describe intra-individual
variation, we used data from individuals that were detected for at least
two years. We calculated the difference between the two values for each
individual observed in two years and calculated the mean, se, minimum
and maximum values across all individuals. GLMs were performed to test
for differences between individuals categorised by previous residency
and previous age.