Skip to main content

Local timing of rainfall predicts the timing of moult within a single locality and the progress of moult among localities that vary in the onset of the wet season in a year-round breeding tropical songbird

Cite this dataset

Nwaogu, Chima; Cresswell, Will (2022). Local timing of rainfall predicts the timing of moult within a single locality and the progress of moult among localities that vary in the onset of the wet season in a year-round breeding tropical songbird [Dataset]. Dryad.


Rainfall seasonality is likely an important cue for timing key annual cycle events like moult in birds living in seasonally arid environments, but its precise effect is difficult to establish because seasonal rainfall may affect other covarying annual events such as breeding in the same way. In central Nigeria, however, Common Bulbuls Pycnonotus barbatus moult in the wet season but only show weak breeding seasonality. This suggests that moult is more sensitive to rainfall than breeding, but a similar outcome is possible if moult is simply periodic. We tested the relationship between rainfall and moult in Common Bulbuls at a single location over 18 years: on average moult started 5th May (± 41 days: 25th March–15th June), being on average later than the onset of the rains which is usually mid-April. The likelihood of finding a moulting Common bulbul was best predicted by rainfall 9–15 weeks before moult was scored. We then tested the generality of this across populations: the progress of moult should, therefore, correlate with the average timing of the wet season along a spatial environmental gradient where the rains start at different times each year south-to-north of Nigeria. To test this, we modelled moult progress just before the rains across 15 localities 6°–13° N as a function of the onset of the wet season among localities. As predicted, moult progressed further in localities with earlier wet seasons, confirming that the onset of moult is timed to the onset of the wet season in each locality despite weak breeding seasonality in the Common Bulbul. This strategy may evolve to maintain optimal annual cycle routine in seasonal environments where breeding is prone to unpredictable local perturbations like nest predation. It may, however, be less obvious in temperate systems where all annual cycle stages are seasonally constrained, but it may help with explaining the high frequency of breeding–moult overlaps in tropical birds.


Data collection

To determine the timing of moult over the annual cycle and the time window within which rainfall predicts the occurrence of moult in a population, we obtained 1701 moult records from Common Bulbuls collected between 2001 and 2018 at the A. P. Leventis Ornithological Research Institute in Jos (09°52′N, 08°58′E). For most birds, moult of primary feathers was scored as ‘pre-moult (not started moult)’, ‘in moult (moulting)’ or ‘moult completed (completed moult)’, and these scores were converted to a binary variable indicating whether a Common Bulbul was moulting or not.

Daily rainfall data between 2000 and 2018 were made available from the Nigerian Meteorological Agency at the Jos airport, located 26 km from APLORI. In Jos, the wet season lasts for approximately six months, usually between mid-April and mid-October, with annual peaks between July and August (Figure S1 and S2). However, the duration of the wet and dry season may vary slightly between years depending on the onset and termination of the rains. April is the first month of the wet season in Jos. There is hardly any rainfall from November to March but there was always rainfall in April between 2001 and 2018 (Figure S2). Between 2001 and 2018, April received 8.73% of the total amount of rainfall recorded in Jos, while November–March together had 1.27%.

Within three months prior to the wet season in Jos, we travelled across Nigeria and mist-netted 308 Common Bulbuls across 15 locations between latitude 6 and 13° N (Fig. 1). Mist netting was carried out between the 17th of January and the 8th of April 2017. All sampling locations were visited before the wet season in each location. We sampled from the southernmost location (Benin) and advanced northward (but not necessarily always consistent with latitude increase; see Fig. 1 for sampling order), apart from Jos which was sampled on three occasions. The pattern of sampling was aimed at preventing the temporal sampling bias from affecting our conclusions, because we predicted that moult will commence later in locations where the wet season was later, and these were more likely in the north. The precipitation in the driest quarter of the year, i.e., the quarter before the wet season, which was when we sampled in each location, correlates negatively with latitude. Hence by sampling south to north, we sample localities with earlier rainfall first, allowing us to interpret any positive correlation between the progress of moult and the precipitation of the driest quarter as an effect of rainfall rather than simply a bias due to sampling date. Note that the sampling order south–north is likely to weaken the predicted positive correlation between moult progress and precipitation because moult should continue to progress in southern localities as we move northwards. So, the actual correlation between the progress of moult and the onset of the wet season among localities should be stronger than represented by our data.

For each bird captured along the gradient, we assessed moult status by scoring primary feathers on an ordinal scale of 0–5: fully grown new feathers were scored 5, while un-moulted old feathers were scored 0, and feathers at different stages of growth were scored 1–4 depending on their size (Ginn and Melville 1983). We also recorded wing length (± 1 mm), brood patch score and body mass (± 0.1 g, Ohaus Scout). We used the function “ms2pfmg” provided with the package ‘Moult’ in R (Erni et al. 2013) to convert moult scores to the proportion of feather material regrown, using methods described by Underhill and Zucchini (1988): each moulted feather was converted to feather mass based on reference masses of individual fully grown primary feathers of the Common Bulbul obtained from Museum specimens at the A. P. Leventis Ornithological Research Institute in Nigeria.

We extracted bioclimatic variables from, based on the GPS coordinates of locations where birds were caught with the aid of the ‘maptools’ and ‘raster’ packages in R. We obtained the precipitation of the driest quarter of the year in each location from the list of 19 variables provided from bioclim (see also Nwaogu et al. 2018).

Usage notes

Microsoft office excel, R and QGIS.


University of St Andrews