Breeding season weather determines long‐tailed tit reproductive success through impacts on recruitment |
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| Authors: | Philippa R Gullett Ben J Hatchwell Robert A Robinson Karl L Evans |
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| Affiliation: | 1. Dept of Animal and Plant Sciences, Univ. of Sheffield, Sheffield, UK;2. British Trust for Ornithology, The Nunnery, Thetford, Norfolk, UK |
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| Abstract: | Productivity is a key demographic trait that can be influenced by climate change, but there are substantial gaps in our understanding of the impact of weather on productivity and recruitment in birds. Weather is known to influence reproductive success in numerous species, although such effects have not been reported in all studies, perhaps because they are masked by high nest predation rates or buffered by density dependence. Here, we use a 19‐yr study of a population of individually marked long‐tailed tits Aegithalos caudatus to quantify the impacts of weather on productivity in the nest (from eggs to fledging) and subsequent recruitment, while taking nest predation rates and density dependence into account. We find that weather has negligible effects on clutch size, hatching success, brood size, probability of fledging and number of fledglings. Annual variation in nest predation rates is a strong predictor of productivity, but we find no evidence that the magnitude of nest predation is determined by weather. Recruitment was strongly associated with breeding season weather, even when taking density dependence effects into account. This contrasts with the conventional view that first year survival of temperate passerines is primarily determined by winter weather. Recruitment was reduced when March temperatures were high, perhaps caused by earlier peaks in caterpillar abundance and thus reduced food availability at the time of fledging. Recruitment increased following high May temperatures, perhaps due to an improved thermo‐regulatory environment for young fledglings. These opposing effects of warm March and May temperatures highlight the importance of considering asymmetrical rates of warming in different months when predicting climate change impacts. |
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