Conservationists are increasingly using autonomous acoustic recorders to determine the presence/absence and the abundance of bird species. Unlike humans, these recorders can be left in the field for extensive periods of time in any habitat. Although data acquisition is automated, manual processing of recordings is labour intensive, tedious, and prone to bias due to observer variations. Hence automated birdsong recognition is an efficient alternative. However, only few ecologists and conservationists utilise the existing birdsong recognisers to process unattended field recordings because the software calibration time is exceptionally high and requires considerable knowledge in signal processing and underlying systems, making the tools less user‐friendly. Even allowing for these difficulties, getting accurate results is exceedingly hard. In this review we examine the state‐of‐the‐art, summarising and discussing the methods currently available for each of the essential parts of a birdsong recogniser, and also available software. The key reasons behind poor automated recognition are that field recordings are very noisy, calls from birds that are a long way from the recorder can be faint or corrupted, and there are overlapping calls from many different birds. In addition, there can be large numbers of different species calling in one recording, and therefore the method has to scale to large numbers of species, or at least avoid misclassifying another species as one of particular interest. We found that these areas of importance, particularly the question of noise reduction, are amongst the least researched. In cases where accurate recognition of individual species is essential, such as in conservation work, we suggest that specialised (species‐specific) methods of passive acoustic monitoring are required. We also believe that it is important that comparable measures, and datasets, are used to enable methods to be compared.

Summary 1.Adaptive management involves the development of predictive models, strategic manipulation of management actions to gain information, and subsequent updating of the models and management. The paradigm has several characteristics that make it an effective approach for determining requirements of re-introduced populations. 2. Adaptive management was applied to the re-introduction of hihi Notiomystis cincta , a New Zealand forest bird that had been reduced to a single island population. Following three previous failed re-introductions, we initiated an 8-year series of management manipulations when hihi were re-introduced to Mokoia Island in 1994. 3.We developed a population model for projecting outcomes under potential management scenarios, and updated it on an annual basis. The population model combined submodels for survival and reproduction that were selected from sets of candidate models using an information-theoretic approach. All projections incorporated demographic stochasticity, and later projections incorporated uncertainty associated with model selection and parameter estimation. 4. The programme showed that some actions (e.g. the provision of sugar water during breeding season and mite control) substantially increased the population's growth rate, but that persistence was uncertain under any management scenario. The population growth rate was shown to be constrained by a low adult survival rate that was unaffected by supplementary feeding, and was associated with a feature of the island (high density of Aspergillus fumigatus spores) that could not be remedied by management. Hihi were therefore removed from Mokoia. However, the management actions shown to be effective on Mokoia have now been used to produce sustained growth in three other re-introduced hihi populations. 6. Synthesis and applications . The results illustrate how adaptive management can facilitate successful species recovery. Without manipulation of management treatments, the Mokoia hihi re-introduction would have just been another failure that provided no useful information. Instead, our manipulations allowed us to identify effective management actions that were successfully applied to other re-introduced populations, and allowed us to identify a limiting factor that had not been previously considered. We have illustrated how other characteristics of the adaptive management approach (flexible treatments, ongoing monitoring, early model development, quantitative projections and incorporation of uncertainty) were essential to the programme.

Widespread in the subtropics and tropics of the Southern Hemisphere, savannas are highly heterogeneous and seasonal natural vegetation types, which makes change detection (natural vs. anthropogenic) a challenging task. The Brazilian Cerrado represents the largest savanna in South America, and the most threatened biome in Brazil owing to agricultural expansion. To assess the native Cerrado vegetation (NV) areas most susceptible to natural and anthropogenic change over time, we classified 33 years (1985–2017) of Landsat imagery available in the Google Earth Engine (GEE) platform. The classification strategy used combined empirical and statistical decision trees to generate reference maps for machine learning classification and a novel annual dataset of the predominant Cerrado NV types (forest, savanna, and grassland). We obtained annual NV maps with an average overall accuracy ranging from 87% (at level 1 NV classification) to 71% over the time series, distinguishing the three main NV types. This time series was then used to generate probability maps for each NV class. The native vegetation in the Cerrado biome declined at an average rate of 0.5% per year (748,687 ha yr−1), mostly affecting forests and savannas. From 1985 to 2017, 24.7 million hectares of NV were lost, and now only 55% of the NV original distribution remains. Of the remnant NV in 2017 (112.6 million hectares), 65% has been stable over the years, while 12% changed among NV types, and 23% was converted to other land uses but is now in some level of secondary NV. Our results were fundamental in indicating areas with higher rates of change in a long time series in the Brazilian Cerrado and to highlight the challenges of mapping distinct NV types in a highly seasonal and heterogeneous savanna biome.

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.

Avian malaria is caused by intracellular mosquito-transmitted protist parasites in the order Haemosporida, genus Plasmodium. Although Plasmodium species have been diagnosed as causing death in several threatened species in New Zealand, little is known about their ecology and epidemiology. In this study, we examined the presence, microscopic characterization and sequence homology of Plasmodium spp. isolates collected from a small number of New Zealand introduced, native and endemic bird species. We identified 14 Plasmodium spp. isolates from 90 blood or tissue samples. The host range included four species of passerines (two endemic, one native, one introduced), one species of endemic pigeon and two species of endemic kiwi. The isolates were associated into at least four distinct clusters including Plasmodium (Huffia) elongatum, a subgroup of Plasmodium elongatum, Plasmodium relictum and Plasmodium (Noyvella) spp. The infected birds presented a low level of peripheral parasitemia consistent with chronic infection (11/15 blood smears examined). In addition, we report death due to overwhelming parasitemia in a blackbird, a great spotted kiwi and a hihi. These deaths were attributed to infections with either Plasmodium spp. lineage LINN1 or P. relictum lineage GRW4. To the authors’ knowledge, this is the first published report of Plasmodium spp. infection in great spotted and brown kiwi, kereru and kokako. Currently, we are only able to speculate on the origin of these 14 isolates but consideration must be made as to the impact they may have on threatened endemic species, particularly due to the examples of mortality.