BackgroundIn vision, there is a trade-off between sensitivity and resolution, and any eye which maximises information gain at low light levels needs to be large. This imposes exacting constraints upon vision in nocturnal flying birds. Eyes are essentially heavy, fluid-filled chambers, and in flying birds their increased size is countered by selection for both reduced body mass and the distribution of mass towards the body core. Freed from these mass constraints, it would be predicted that in flightless birds nocturnality should favour the evolution of large eyes and reliance upon visual cues for the guidance of activity.Methodology/Principal FindingsWe show that in Kiwi (Apterygidae), flightlessness and nocturnality have, in fact, resulted in the opposite outcome. Kiwi show minimal reliance upon vision indicated by eye structure, visual field topography, and brain structures, and increased reliance upon tactile and olfactory information.Conclusions/SignificanceThis lack of reliance upon vision and increased reliance upon tactile and olfactory information in Kiwi is markedly similar to the situation in nocturnal mammals that exploit the forest floor. That Kiwi and mammals evolved to exploit these habitats quite independently provides evidence for convergent evolution in their sensory capacities that are tuned to a common set of perceptual challenges found in forest floor habitats at night and which cannot be met by the vertebrate visual system. We propose that the Kiwi visual system has undergone adaptive regressive evolution driven by the trade-off between the relatively low rate of gain of visual information that is possible at low light levels, and the metabolic costs of extracting that information.

Summary1. Acoustic methods are used increasingly to survey and monitor bat populations. However, the use of acoustic methods at continental scales can be hampered by the lack of standardized and objective methods to identify all species recorded. This makes comparable continent-wide monitoring difficult, impeding progress towards developing biodiversity indicators, transboundary conservation programmes and monitoring species distribution changes. 2. Here we developed a continental-scale classifier for acoustic identification of bats, which can be used throughout Europe to ensure objective, consistent and comparable species identifications. We selected 1350 full-spectrum reference calls from a set of 15 858 calls of 34 European species, from EchoBank, a global echolocation call library. We assessed 24 call parameters to evaluate how well they distinguish between species and used the 12 most useful to train a hierarchy of ensembles of artificial neural networks to distinguish the echolocation calls of these bat species. 3. Calls are first classified to one of five call-type groups, with a median accuracy of 97Á6%. The median species-level classification accuracy is 83Á7%, providing robust classification for most European species, and an estimate of classification error for each species. 4. These classifiers were packaged into an online tool, iBatsID, which is freely available, enabling anyone to classify European calls in an objective and consistent way, allowing standardized acoustic identification across the continent. 5. Synthesis and applications. iBatsID is the first freely available and easily accessible continental-scale bat call classifier, providing the basis for standardized, continental acoustic bat monitoring in Europe. This method can provide key information to managers and conservation planners on distribution changes and changes in bat species activity through time.

Passive acoustic sensing has emerged as a powerful tool for quantifying anthropogenic impacts on biodiversity, especially for echolocating bat species. To better assess bat population trends there is a critical need for accurate, reliable, and open source tools that allow the detection and classification of bat calls in large collections of audio recordings. The majority of existing tools are commercial or have focused on the species classification task, neglecting the important problem of first localizing echolocation calls in audio which is particularly problematic in noisy recordings. We developed a convolutional neural network based open-source pipeline for detecting ultrasonic, full-spectrum, search-phase calls produced by echolocating bats. Our deep learning algorithms were trained on full-spectrum ultrasonic audio collected along road-transects across Europe and labelled by citizen scientists from www.batdetective.org. When compared to other existing algorithms and commercial systems, we show significantly higher detection performance of search-phase echolocation calls with our test sets. As an example application, we ran our detection pipeline on bat monitoring data collected over five years from Jersey (UK), and compared results to a widely-used commercial system. Our detection pipeline can be used for the automatic detection and monitoring of bat populations, and further facilitates their use as indicator species on a large scale. Our proposed pipeline makes only a small number of bat specific design decisions, and with appropriate training data it could be applied to detecting other species in audio. A crucial novelty of our work is showing that with careful, non-trivial, design and implementation considerations, state-of-the-art deep learning methods can be used for accurate and efficient monitoring in audio.

Three families of probe-foraging birds, Scolopacidae (sandpipers and snipes), Apterygidae (kiwi), and Threskiornithidae (ibises, including spoonbills) have independently evolved long, narrow bills containing clusters of vibration-sensitive mechanoreceptors (Herbst corpuscles) within pits in the bill-tip. These ‘bill-tip organs’ allow birds to detect buried or submerged prey via substrate-borne vibrations and/or interstitial pressure gradients. Shorebirds, kiwi and ibises are only distantly related, with the phylogenetic divide between kiwi and the other two taxa being particularly deep. We compared the bill-tip structure and associated somatosensory regions in the brains of kiwi and shorebirds to understand the degree of convergence of these systems between the two taxa. For comparison, we also included data from other taxa including waterfowl (Anatidae) and parrots (Psittaculidae and Cacatuidae), non-apterygid ratites, and other probe-foraging and non probe-foraging birds including non-scolopacid shorebirds (Charadriidae, Haematopodidae, Recurvirostridae and Sternidae). We show that the bill-tip organ structure was broadly similar between the Apterygidae and Scolopacidae, however some inter-specific variation was found in the number, shape and orientation of sensory pits between the two groups. Kiwi, scolopacid shorebirds, waterfowl and parrots all shared hypertrophy or near-hypertrophy of the principal sensory trigeminal nucleus. Hypertrophy of the nucleus basorostralis, however, occurred only in waterfowl, kiwi, three of the scolopacid species examined and a species of oystercatcher (Charadriiformes: Haematopodidae). Hypertrophy of the principal sensory trigeminal nucleus in kiwi, Scolopacidae, and other tactile specialists appears to have co-evolved alongside bill-tip specializations, whereas hypertrophy of nucleus basorostralis may be influenced to a greater extent by other sensory inputs. We suggest that similarities between kiwi and scolopacid bill-tip organs and associated somatosensory brain regions are likely a result of similar ecological selective pressures, with inter-specific variations reflecting finer-scale niche differentiation.

Aerial hawking bats use intense echolocation calls to search for insect prey. Their calls have evolved into the most intense airborne animal vocalisations. Yet our knowledge about call intensities in the field is restricted to a small number of species. We describe a novel stereo videogrammetry method used to study flight and echolocation behaviour, and to measure call source levels of the aerial hawking bat Eptesicus bottae (Vespertilionidae). Bats flew close to their predicted minimum power speed. Source level increased with call duration; the loudest call of E. bottae was at 133·dB·peSPL. The calculated maximum detection distance for large flying objects (e.g. large prey, conspecifics) was up to 21·m. The corresponding maximum echo delay is almost exactly the duration of one wing beat in E. bottae and this also is its preferred pulse interval. These results, obtained by using videogrammetry to track bats in the field, corroborate earlier findings from other species from acoustic tracking methods.