Only a handful of bird species are known to use foraging tools in the wild. Amongst them, the New Caledonian crow (Corvus moneduloides) stands out with its sophisticated tool-making skills. Despite considerable speculation, the evolutionary origins of this species' remarkable tool behaviour remain largely unknown, not least because no naturally tool-using congeners have yet been identified that would enable informative comparisons. Here we show that another tropical corvid, the 'Alalā (C. hawaiiensis; Hawaiian crow), is a highly dexterous tool user. Although the 'Alalā became extinct in the wild in the early 2000s, and currently survives only in captivity, at least two lines of evidence suggest that tool use is part of the species' natural behavioural repertoire: juveniles develop functional tool use without training, or social input from adults; and proficient tool use is a species-wide capacity. 'Alalā and New Caledonian crows evolved in similar environments on remote tropical islands, yet are only distantly related, suggesting that their technical abilities arose convergently. This supports the idea that avian foraging tool use is facilitated by ecological conditions typical of islands, such as reduced competition for embedded prey and low predation risk. Our discovery creates exciting opportunities for comparative research on multiple tool-using and non-tool-using corvid species. Such work will in turn pave the way for replicated cross-taxonomic comparisons with the primate lineage, enabling valuable insights into the evolutionary origins of tool-using behaviour.

Passive acoustic monitoring is increasingly being used as a cost‐effective way to study wildlife populations, especially those that are difficult to census using conventional methods. Burrow‐nesting seabirds are among the most threatened birds globally, but they are also one of the most challenging taxa to census, making them prime candidates for research into such automated monitoring platforms. Passive acoustic monitoring has the potential to determine presence/absence or quantify burrow‐nesting populations, but its effectiveness remains unclear. We compared passive acoustic monitoring, tape‐playbacks and GPS tracking data to investigate the ability of passive acoustic monitoring to capture unbiased estimates of within‐colony variation in nest density for the Manx Shearwater Puffinus puffinus. Variation in acoustic activity across 12 study plots on an island colony was examined in relation to burrow density and environmental factors across 2 years. As predicted fewer calls were recorded when wind speed was high, and on moon‐lit nights, but there was no correlation between acoustic activity and the density of breeding birds within the plots as determined by tape‐playback surveys. Instead, acoustic indices correlated positively with spatial variation in the in‐colony flight activity of breeding individuals detected by GPS. Although passive acoustic monitoring has enormous potential in avian conservation, our results highlight the importance of understanding behaviour when using passive acoustic monitoring to estimate density and distribution.

The use of bio‐logging devices to track animal movement continues to grow as technological advances and device miniaturisation allow researchers to study animal behaviour in unprecedented detail. Balanced against the remarkable data that bio‐loggers can provide is a need to understand the impact of devices on animal behaviour and welfare. Recent meta‐analyses have demonstrated the impacts of device attachment on animal behaviour, but there is a concern about the frequency and clarity with which device effects are reported. One aspect lacking in many studies is assessment of the statistical power of tests of device effects, yet such information would assist the interpretation of results. We address this issue by providing an overview of the statistical power, as well as the Type M (magnitude) and Type S (sign) error rate, of tests of device effects within the avian tracking literature across a range of assumed effect sizes. The median power of statistical tests ranged from 9% to 65% across a range of assumed effect sizes corresponding to benchmark values for small, moderate and large effects (d = 0.2, 0.5, 0.8, respectively). Moreover, when using effect sizes derived from previous a meta‐analysis (d = 0.1), median power was only 6%. When assuming smaller effect sizes, statistical tests were characterised by high Type M and Type S error rates, suggesting that statistically significant results of device effects will tend to exaggerate the size of such effects and may estimate the sign of an effect in the wrong direction. Well‐designed tracking studies will reduce device effects to low levels and consequently issues associated with low power will be commonplace. Nevertheless, assessment of device effects remains important, particularly when embarking on novel tracking studies. We recommend that statistical tests of device effects are reported clearly and are routinely accompanied by assessment of statistical power, including Type M and Type S errors, based upon realistic external estimates of effect size. Reporting the statistical power can help avoid the pitfalls of overstating results from individual studies, shift the emphasis to accurate reporting of effect sizes and guide decisions about the ethical impacts of device attachment.

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