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In wild bird populations, the structure of vegetation around nest‐sites can influence the risk of predation of dependent offspring, generating selection for nest‐sites with vegetation characteristics associated with lower predation rates. However, vegetation structure can be difficult to quantify objectively in the field, which might explain why there remains a general lack of understanding of which characteristics are most important in determining predation rates. Airborne laser scanning (ALS) offers a powerful means of measuring vegetation structure at unprecedented resolution. Here, we combined ALS with 11 years of breeding data from a wild population of superb fairy‐wrens Malurus cyaneus in southeastern Australia, a species which nests relatively close to the ground and has high rates of nest and fledgling predation. We derived structural measurements of understorey (0–8 m) vegetation from a contiguous grid of 30 × 30 m resolution cells across our c. 65 hectares study area. We found that cells with nests (nest‐cells) differed in their understorey vegetation structure characteristics compared to unused cells, primarily in having denser vegetation in the lowest layer of the understorey (0–2 m; the 'groundstorey' layer). The average height of understorey vegetation was also lower in cells with nests than in those without nests. However, relationships between understorey vegetation structure characteristics and breeding performance were mixed. Nest success rates decreased with higher volumes of groundstorey vegetation, as did fledgling survival rates, though only in nest‐cells with lower height vegetation. Our results indicate that ALS can identify vegetation characteristics relevant for superb fairy‐wren nest‐site selection, but that nesting preferences are not beneficial under current predation pressures. The study illustrates the potential for using ALS to investigate how ecological conditions affect behaviour and life‐histories in wild animal populations. [ABSTRACT FROM AUTHOR]

Climate warming has been shown to affect the timing of the onset of breeding of many bird species across the world. However, for multi‐brooded species, climate may also affect the timing of the end of the breeding season, and hence also its duration, and these effects may have consequences for fitness. We used 28 years of field data to investigate the links between climate, timing of breeding, and breeding success in a cooperatively breeding passerine, the superb fairy‐wren (Malurus cyaneus). This multi‐brooded species from southeastern Australia has a long breeding season and high variation in phenology between individuals. By applying a "sliding window" approach, we found that higher minimum temperatures in early spring resulted in an earlier start and a longer duration of breeding, whereas less rainfall and more heatwaves (days > 29°C) in late summer resulted in an earlier end and a shorter duration of breeding. Using a hurdle model analysis, we found that earlier start dates did not predict whether or not females produced any young in a season. However, for successful females who produced at least one young, earlier start dates were associated with higher numbers of young produced in a season. Earlier end dates were associated with a higher probability of producing at least one young, presumably because unsuccessful females kept trying when others had ceased. Despite larger scale trends in climate, climate variables in the windows relevant to this species' phenology did not change across years, and there were no temporal trends in phenology during our study period. Our results illustrate a scenario in which higher temperatures advanced both start and end dates of individuals' breeding seasons, but did not generate an overall temporal shift in breeding times. They also suggest that the complexity of selection pressures on breeding phenology in multi‐brooded species may have been underestimated. [ABSTRACT FROM AUTHOR]

The article presents a study which investigated climate effects on adult mortality in Australian superb fairy-wrens (Malurus cyaneus). Topics discussed include nonclimatic causes of variation in mortality, climate effects on the seasonal mortality hazard rate, and climate effects on the weekly mortality hazard rate. Temporal trends in climate indices and mortality rate are also mentioned.