SummaryThe ‘Vulnerable’ Swinhoe’s Rail Coturnicops exquisitus is believed to occur in only two regions in Russia’s Far East and China’s Heilongjiang province, separated by more than 1,000 km. Recent observations suggest that the Amur region, situated between the two known populations, might be inhabited by this secretive species as well. As the species is rather similar in appearance and field characteristics to its Nearctic sister taxon, the Yellow Rail C. noveboracensis, and almost all field records relate to flushed individuals in flight, we aimed to complement the field observations by genetic evidence. Samples were obtained from four individuals and one eggshell and their mitochondrial cytochrome b genes were amplified and sequenced. The genetic analyses unequivocally confirmed that swab samples and eggshell were attributable to Swinhoe’s Rail, thus constituting the first known breeding record of this species for 110 years. It is therefore likely that the individuals observed in the field also belonged to this species. It seems possible that Swinhoe’s Rail is more widely distributed in the Amur region and was overlooked in the past, possibly due to a misleading description of its calls in the literature.

Information on habitat preferences is critical for the successful conservation of endangered species. For many species, especially those living in remote areas, we currently lack this information. Time and financial resources to analyze habitat use are limited. We aimed to develop a method to describe habitat preferences based on a combination of bird surveys with remotely sensed fine-scale land cover maps. We created a blended multiband remote sensing product from SPOT 6 and Landsat 8 data with a high spatial resolution. We surveyed populations of three bird species (Yellow-breasted Bunting Emberiza aureola, Ochre-rumped Bunting Emberiza yessoensis, and Black-faced Bunting Emberiza spodocephala) at a study site in the Russian Far East using hierarchical distance sampling, a survey method that allows to correct for varying detection probability. Combining the bird survey data and land cover variables from the remote sensing product allowed us to model population density as a function of environmental variables. We found that even small-scale land cover characteristics were predictable using remote sensing data with sufficient accuracy. The overall classification accuracy with pansharpened SPOT 6 data alone amounted to 71.3%. Higher accuracies were reached via the additional integration of SWIR bands (overall accuracy = 73.21%), especially for complex small-scale land cover types such as shrubby areas. This helped to reach a high accuracy in the habitat models. Abundances of the three studied bird species were closely linked to the proportion of wetland, willow shrubs, and habitat heterogeneity. Habitat requirements and population sizes of species of interest are valuable information for stakeholders and decision-makers to maximize the potential success of habitat management measures.the effectiveness of conservation interventions [1,3]. Yet, despite the rapid global decline in species richness and abundance over the past decades [4,5], a major challenge in conservation ecology is our limited knowledge on the population sizes and habitat preferences of animal species in understudied regions [6][7][8]. Here, the use of remote sensing data to predict species distributions and abundances is vital to improve large-scale biodiversity monitoring and species conservation [2,3,9].Optical remote sensing is often suggested as a powerful tool to assist with the mapping of protected habitats and vegetation. Moreover, spaceborne remote sensing offers the opportunity to map larger areas in a systematic, repeatable, and spatially exhaustive manner [10,11]. Apart from the spatial extent of the data, its grain or spatial resolution is another relevant issue of spatial scale. Ideally, the pixel resolution of remotely sensed and considered environmental data (e.g., information on habitat features) should match to model their relationships [12]. For example, the classical multispectral configuration of Landsat-4 to Landsat-8 provides a pixel size of 30 × 30 m and an image extent of 185 × 185 km. This allows linking Landsat data to en...

The selection of a nest site is crucial for successful reproduction of birds. Animals which re‐use or occupy nest sites constructed by other species often have limited choice. Little is known about the criteria of nest‐stealing species to choose suitable nesting sites and habitats. Here, we analyze breeding‐site selection of an obligatory “nest‐cleptoparasite”, the Amur Falcon Falco amurensis. We collected data on nest sites at Muraviovka Park in the Russian Far East, where the species breeds exclusively in nests of the Eurasian Magpie Pica pica. We sampled 117 Eurasian Magpie nests, 38 of which were occupied by Amur Falcons. Nest‐specific variables were assessed, and a recently developed habitat classification map was used to derive landscape metrics. We found that Amur Falcons chose a wide range of nesting sites, but significantly preferred nests with a domed roof. Breeding pairs of Eurasian Hobby Falco subbuteo and Eurasian Magpie were often found to breed near the nest in about the same distance as neighboring Amur Falcon pairs. Additionally, the occurrence of the species was positively associated with bare soil cover, forest cover, and shrub patches within their home range and negatively with the distance to wetlands. Areas of wetlands and fallow land might be used for foraging since Amur Falcons mostly depend on an insect diet. Additionally, we found that rarely burned habitats were preferred. Overall, the effect of landscape variables on the choice of actual nest sites appeared to be rather small. We used different classification methods to predict the probability of occurrence, of which the Random forest method showed the highest accuracy. The areas determined as suitable habitat showed a high concordance with the actual nest locations. We conclude that Amur Falcons prefer to occupy newly built (domed) nests to ensure high nest quality, as well as nests surrounded by available feeding habitats.