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Declining bird populations, especially those that breed in North American grasslands, have stimulated extensive research on factors that affect nest failure and reduced reproductive success. Until now, this research has been hampered by the difficulties inherent in observing nest activities. Video Surveillance of Nesting Birds highlights the use of miniature video cameras and recording equipment yielding new important and some unanticipated insights into breeding bird biology, including previously undocumented observations of hatching, incubation, fledging, diurnal and nocturnal activity patterns, predator identification, predator-prey interactions, and cause-specific rates of nest loss. This seminal contribution to bird reproductive biology uses tools capable of generating astonishing results with the potential for fresh insights into bird conservation, management, and theory.

Developing and validating methods to determine trends in populations of threatened species is essential for evaluating the effectiveness of conservation interventions. For cryptic species inhabiting remote environments, this can be particularly challenging. Rock wrens, Xenicus gilviventris , are small passerines endemic to the alpine zone of southern New Zealand. They are highly vulnerable to predation by introduced mammalian predators. Establishing a robust, cost-effective monitoring tool to evaluate population trends in rock wrens is a priority for conservation of both the species and, more broadly, as part of a suite of indicators for evaluating effectiveness of management in New Zealand’s alpine ecosystems. We assessed the relative accuracy and precision of three population estimation techniques (mark-resight, distance sampling and simple counts on line transects) for two populations of rock wrens in the Southern Alps over six breeding seasons (2012–2018). The performance of these population estimators was compared to known rock wren population size derived from simultaneous territory mapping. Indices of abundance derived from counts on transects were correlated with territory mapping at both study areas, and performed better than either mark-resight methods or distance sampling. Simple counts on standardised line transects are a highly cost-effective method of monitoring birds because they do not require banding a population. As such, we recommend that line transect counts using the design outlined in this paper be adopted as a standard method for long-term monitoring of rock wren populations. Although species-specific testing is required to validate use of low-cost population indices, our results may have utility for the monitoring of other cryptic passerines in relatively open habitats.

Passive acoustic monitoring (PAM) has become an important tool for surveying birds, and there is a growing demand for approaches to obtain abundance and behavioural information from PAM recordings. Changes in bird populations have been assessed by counting recorded calls and calculating the vocal activity rate (VAR, i.e. the number of calls per recording time). However, bird calls could be counted in various ways and depending on species traits, these call counts could give us different insights on bird abundance, vocal behaviour and/or habitat use. Our study had two goals: (1) to present and evaluate two new indices based on call counts, the detection rate (DR, i.e. the number of 1‐min recordings in which the presence of a target vocalization is detected) and the maximum count per minute (MAX, i.e. the maximum number of calls found in a 1‐min recording); and (2) to present a conceptual framework showing how the interpretations of VAR, DR and MAX could depend on the index and on species traits. For three Neotropical bird species with distinct traits, we calculated VAR, DR and MAX based on PAM data from 25 sites in the Yucatan Peninsula (Mexico) that varied in their degree of anthropogenic habitat disturbance. We found moderate to high correlations between the indices and higher temporal variability in VAR compared to DR and MAX. We also found different effect sizes of habitat disturbance on the three species and indices. We suggest that DR might be a more reliable index of relative abundance than VAR for species whose calling behaviour exhibits a high cue rate and that MAX may be suitable for estimating family or flock size in gregarious birds. Our findings show the potential usefulness of developing new indices based on call counts to generate ecological hypotheses and assess changes in bird abundance and behaviour. Resumen El monitoreo acústico pasivo (PAM, por sus siglas en inglés) se ha convertido en una herramienta relevante para el estudio de aves, y existe una creciente demanda de diferentes enfoques para obtener información sobre abundancia y comportamiento a partir de grabaciones por medio del PAM. Hasta ahora, los cambios en las poblaciones de aves se han evaluado mediante el conteo de vocalizaciones grabadas y calculando la tasa de actividad vocal (VAR, por sus siglas en inglés, i.e. el número de vocalizaciones por tiempo de grabación). Sin embargo, las vocalizaciones de aves podrían contarse de varias maneras, y dependiendo de las características de las especies, estos conteos podrían brindarnos diferente información sobre la abundancia de aves, el comportamiento vocal y/o el uso del hábitat. Nuestro estudio tuvo dos metas: (1) presentar y evaluar dos nuevos índices basados en conteos de vocalizaciones, la tasa de detección (DR, por sus siglas en inglés, i.e. el número de grabaciones de 1 minuto en las que se detecta la presencia de una vocalización focal) y el conteo máximo por minuto (MAX, i.e. el número máximo de vocalizaciones encontradas en una grabación de 1 minuto); y (2) presentar un marco conceptual que muestra cómo las interpretaciones de VAR, DR y MAX podrían depender del índice y de las características de las especies. Para tres especies de aves neotropicales con diferentes características, calculamos VAR, DR y MAX con base en datos de PAM de 25 sitios en la Península de Yucatán (México) que variaban en el grado de perturbación antropogénica del hábitat. Encontramos correlaciones moderadas a altas entre los índices, y una mayor variabilidad temporal en VAR en comparación con DR y MAX. También encontramos diferentes tamaños de efecto de la perturbación del hábitat para las tres especies e índices. Sugerimos que DR podría ser un índice más confiable de la abundancia relativa que VAR para especies cuyo comportamiento vocal exhibe una alta tasa de señales, y que MAX podría ser adecuado para estimar el tamaño de grupo (familia o bandada) en aves gregarias. Nuestros hallazgos muestran la utilidad potencial de desarrollar nuevos índices basados en el conteo de vocalizaciones para generar hipótesis ecológicas y evaluar cambios en la abundancia y el comportamiento de las aves.

The use of a drone to count the flock sizes of 33 species of waterbirds during the breeding and non‐breeding periods was investigated. In 96% of 343 cases, drone counting was successful. 18.8% of non‐breeding birds and 3.6% of breeding birds exhibited adverse reactions: the former birds were flushed, whereas the latter attempted to attack the drone. The automatic counting of birds was best done with ImageJ/Fiji microbiology software – the average counting rate was 100 birds in 64 s. Machine learning using neural network algorithms proved to be an effective and quick way of counting birds – 100 birds in 7 s. However, the preparation of images and machine learning time is time‐consuming, so this method is recommended only for large data sets and large bird assemblages. The responsible study of wildlife using a drone should only be carried out by persons experienced in the biology and behavior of the target animals. The experiment carried out on 33 species of waterbirds shows the effectiveness of the use of the drone in population censusing, 96% of 343 cases, drone counting was successful. The best automatic counting tool was microbiology software ImageJ/Fiji and Machine learning using neural network algorithms – DenoiSeg.

In 2020, over 10,000 bird strikes were reported in the USA, with average repair costs exceeding$200 million annually, rising to $ 1.2 billion worldwide. These collisions of avifauna with airplanes pose a significant threat to human safety and wildlife. This article presents a system dedicated to monitoring the space over an airport and is used to localize and identify moving objects. The solution is a stereovision based real-time bird protection system, which uses IoT and distributed computing concepts together with advanced HMI to provide the setup’s flexibility and usability. To create a high degree of customization, a modified stereovision system with freely oriented optical axes is proposed. To provide a market tailored solution affordable for small and medium size airports, a user-driven design methodology is used. The mathematical model is implemented and optimized in MATLAB. The implemented system prototype is verified in a real environment. The quantitative validation of the system performance is carried out using fixed-wing drones with GPS recorders. The results obtained prove the system’s high efficiency for detection and size classification in real-time, as well as a high degree of localization certainty.

Without robust and unbiased systems for monitoring, changes in natural systems will remain enigmatic for policy makers, leaving them without a clear idea of the consequences of any environmental policies they might adopt. Generally, biodiversity-monitoring activities are not integrated or evaluated across any large geographic region. The EuMon project conducted the first large-scale evaluation of monitoring practices in Europe through an on-line questionnaire and is reporting on the results of this survey. In September 2007 the EuMon project had documented 395 monitoring schemes for species, which represents a total annual cost of about [Euro Sign]4 million, involving more than 46,000 persons devoting over 148,000 person-days/year to biodiversity-monitoring activities. Here we focused on the analysis of variations of monitoring practices across a set of taxonomic groups (birds, amphibians and reptiles, mammals, butterflies, plants, and other insects) and across 5 European countries (France, Germany, Hungary, Lithuania, and Poland). Our results suggest that the overall sampling effort of a scheme is linked with the proportion of volunteers involved in that scheme. Because precision is a function of the number of monitored sites and the number of sites is maximized by volunteer involvement, our results do not support the common belief that volunteer-based schemes are too noisy to be informative. Just the opposite, we believe volunteer-based schemes provide relatively reliable data, with state-of-the-art survey designs or data-analysis methods, and consequently can yield unbiased results. Quality of data collected by volunteers is more likely determined by survey design, analytical methodology, and communication skills within the schemes rather than by volunteer involvement per se.

Millions of bird observations have been entered on online portals in the past 20 years either as checklists or arbitrary individual entries. While several hundred publications have been written on a variety of topics based on bird checklists worldwide, unstructured non-checklist observations have received little attention and praise by academia. In the present study we tested the suitability of non-checklist data to estimate key figures of large-scale migration phenology in four zones covering the whole of Finland. For that purpose, we analysed 10 years of ornithological non-checklist data including over 400 million. individuals of 115 bird species. We discuss bird- and human-induced effects to be considered in handling non-checklist data in this context and describe applied methodologies to address these effects. We calculated 5%, 50% and 95% percentile dates of spring and autumn migration period for all species in all four zones. For validation purposes we compared the temporal distributions of 43 bird species with migration phenology from standardized long-term ringing data in autumn of which 24 species (56%) showed similar medians. In a model approach, non-checklist data successfully revealed latitudinal migration progression in spring and autumn. Overall, non-checklist data proved to be well suited to determine descriptors of migration phenology in Northern Europe which are challenging to attain by any other currently available means. The effort-to-yield ratio of data processing was commensurate to the outcomes. The unprecedented spatiotemporal coverage makes non-checklist data a valuable complement to current migration databases from bird observatories. The basic concept of the present methodology is applicable to data from other bird portals, if combined with local field ornithological knowledge and literature. Species-specific descriptors of migration phenology can be potentially used in climate change studies and to support echo interpretation in radar ornithology.

Background Ornithobacterium ( O. ) rhinotracheale is an emerging bacterial pathogen in poultry and not fully understood to date. Because of its importance particularly for the global turkey meat industry, reliable diagnostic and characterization methods are needed for early treatment and in future for better vaccine production. The host range of birds infected by O. rhinotracheale or carrying the bacterium in their respiratory tract has constantly increased raising important epidemiological and taxonomic questions for a better understanding of its diversity, ecology and transmission cycles. The purpose of this study was to introduce partial rpoB gene sequencing for O. rhinotracheale into routine diagnostics to differentiate strains isolated from poultry and more diverse avian hosts (i.e., birds of prey, corvids and pigeons) and to compare phylogenetic relationships with results from 16S rRNA gene analysis and multilocus sequence typing (MLST). Results Partial 16S rRNA gene analysis revealed a high level of homogeneity among the 65 investigated O. rhinotracheale sequences with similarity values ranging from 98.6 to 100% between sequences from non-galliform and poultry species. The corresponding rpoB gene sequences were heterogeneous and ranged in their similarity values from 85.1 to 100%. The structure of the rpoB tree was in strong correlation with previous MLST results revealing three main clusters A (poultry and birds of prey), B (poultry, birds of prey and corvids) and C (pigeons), which were clearly separated from each other. Conclusions By using partial sequences from a single gene, the rpoB gene analysis is in good agreement with MLST results with a slight decrease in resolution to distinguish more similar strains. The present results provide strong evidence that traditional phenotypic and genetic methods may not properly represent the heterogeneous group of bacteria classified as O. rhinotracheale . From housekeeping gene analyses, it is very likely that the genus Ornithobacterium includes additional species and partial rpoB gene sequencing can be recommended as fast, cost-effective and readily available method to identify strains and differentiate between O. rhinotracheale and Ornithobacterium -like bacteria.

Obtaining unbiased survey data for vocal bird species is inherently challenging due to observer biases, habitat coverage biases, and logistical constraints. We propose that combining bioacoustic monitoring with unmanned aerial vehicle (UAV) technology could reduce some of these biases and allow bird surveys to be conducted in less accessible areas. We tested the feasibility of the UAV approach to songbird surveys using a low-cost quadcopter with a simple, lightweight recorder suspended 8 m below the vehicle. In a field experiment using playback of bird recordings, we found that small variations in UAV altitude (it hovered at 28, 48, and 68 m) didn't have a significant effect on detections by the recorder attached to the UAV, and we found that the detection radius of our equipment was comparable with detection radii of standard point counts. We then field tested our equipment, comparing songbird detections from our UAV-mounted recorder with standard point-count data from 51 count stations. We found that the number of birds per point on UAV counts was comparable with standard counts for most species, but there were significant underestimates for some—specifically, issues of song masking for a species with a low-frequency song, the Mourning Dove (Zenaida macroura); and underestimation of the abundance of a species that was found in very high densities, the Gray Catbird (Dumetella carolinensis). Species richness was lower on UAV counts (mean = 5.6 species point−1) than on standard counts (8.3 species point−1), but only slightly lower than on standard counts if nonaudible detections are omitted (6.5 species point−1). Excessive UAV noise is a major hurdle to using UAVs for bioacoustic monitoring, but we are optimistic that technological innovations to reduce motor and rotor noise will significantly reduce this issue. We conclude that UAV-based bioacoustic monitoring holds great promise, and we urge other researchers to consider further experimentation to refine techniques.