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"bird monitoring"
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Automated Bird Counting with Deep Learning for Regional Bird Distribution Mapping
A challenging problem in the field of avian ecology is deriving information on bird population movement trends. This necessitates the regular counting of birds which is usually not an easily-achievable task. A promising attempt towards solving the bird counting problem in a more consistent and fast way is to predict the number of birds in different regions from their photos. For this purpose, we exploit the ability of computers to learn from past data through deep learning which has been a leading sub-field of AI for image understanding. Our data source is a collection of on-ground photos taken during our long run of birding activity. We employ several state-of-the-art generic object-detection algorithms to learn to detect birds, each being a member of one of the 38 identified species, in natural scenes. The experiments revealed that computer-aided counting outperformed the manual counting with respect to both accuracy and time. As a real-world application of image-based bird counting, we prepared the spatial bird order distribution and species diversity maps of Turkey by utilizing the geographic information system (GIS) technology. Our results suggested that deep learning can assist humans in bird monitoring activities and increase citizen scientists’ participation in large-scale bird surveys.
Journal Article
Unstructured citizen science data fail to detect long-term population declines of common birds in Denmark
Aim Long-term monitoring of biodiversity is necessary to identify population declines and to develop conservation management. Because long-term monitoring is labour-intensive, resources to implement robust monitoring programmes are lacking in many countries. The increasing availability of citizen science data in online public databases can potentially fill gaps in structured monitoring programmes, but only if trends estimated from unstructured citizen science data match those estimated from structured monitoring programmes. We therefore aimed to assess the correlation between trends estimated from structured and unstructured data. Location Denmark. Methods We compared population trends for 103 bird species estimated over 28 years from a structured monitoring programme and from unstructured citizen science data to assess whether trends estimated from the two data sources were correlated. Results Trends estimated from the two data sources were generally positively correlated, but less than half the population declines identified from the structured monitoring data were recovered from the unstructured citizen science data. The mismatch persisted when we reduced the structured monitoring data from count data to occurrence data to mimic the information content of unstructured citizen science data and when we filtered the unstructured data to reduce the number of incomplete lists reported. Mismatching trends were especially prevalent for the most common species. Worryingly, more than half the species showing significant declines in the structured monitoring showed significant positive trends in the citizen science data. Main conclusions We caution that unstructured citizen science databases cannot replace structured monitoring data because the former are less sensitive to population changes. Thus, unstructured data may not fulfil one of the most critical functions of structured monitoring programmes, namely to act as an early warning system that detects population declines.
Journal Article
Birds in Europe 4: the fourth assessment of Species of European Conservation Concern
This is the fourth comprehensive assessment of the population status of all wild bird species in Europe. It identifies Species of European Conservation Concern (SPECs) so that action can be taken to improve their status. Species are categorised according to their global extinction risk, the size and trend of their European population and range, and Europe’s global responsibility for them. Of the 546 species assessed, 207 (38%) are SPECs: 74 (14%) of global concern (SPEC 1); 32 (6%) of European concern and concentrated in Europe (SPEC 2); and 101 (18%) of European concern but not concentrated in Europe (SPEC 3). The proportion of SPECs has remained similar (38–43%) across all four assessments since 1994, but the number of SPEC 1 species of global concern has trebled. The 44 species assessed as Non-SPECs in the third assessment (2017) but as SPECs here include multiple waders, raptors and passerines that breed in arctic, boreal or alpine regions, highlighting the growing importance of northern Europe and mountain ecosystems for bird conservation. Conversely, the 62 species assessed as SPECs in 2017 but as Non-SPECs here include various large waterbirds and raptors that are recovering due to conservation action. Since 1994, the number of specially protected species (listed on Annex I of the EU Birds Directive) qualifying as SPECs has fallen by 33%, while the number of huntable (Annex II) species qualifying as SPECs has risen by 56%. The broad patterns identified previously remain evident: 100 species have been classified as SPECs in all four assessments, including numerous farmland and steppe birds, ducks, waders, raptors, seabirds and long-distance migrants. Many of their populations are heavily depleted or continue to decline and/or contract in range. Europe still holds 3.4–5.4 billion breeding birds, but more action to halt and reverse losses is needed.
Journal Article
Tracking progress toward EU biodiversity strategy targets: EU policy effects in preserving its common farmland birds
PECBMS is supported financially by the RSPB and the European Commission. TT was supported by Institutional Research Plan (RVO: 68081766), SH and LB were supported by EUBON project (308454; FP7‐ENV‐2012 European Commission) and the TRUSTEE project (RURAGRI ERA‐NET 235175), and AL received financial support from the Academy of Finland (project 275606).
Journal Article
Improving national-scale breeding bird surveys with integrated distance sampling
Bird population estimation over broad spatial and temporal scales is a key objective in ornithology. To date, bird ecologists mainly relied on standard point counts where the number of detected individuals is interpreted as either the true abundance or proportionally related to it. However, providing accurate estimates of species abundance requires modelling the observation process with temporally replicated data, which is not always possible with the increasing use of ever-bigger datasets from citizen science programs. Data integration methods allow combining temporally replicated sampling at coarser spatial grains with data collected over larger spatial extents. Here, we developed an Integrated distance sampling (IDS) to combine national structured and semi-structured citizen-based bird surveys in France to estimate species abundances using observation distances and accounting for availability, i.e. the probability of individuals being detectable during a given sampling visit. While our simulation study showed an overall increase in the accuracy of estimated parameters for both ecological and observation processes, without significant biases, our case study suggests that such model improvements will depend on specific sampling scenarios. Integrated models represent a promising tool for ecological science, permitting the joint use of large unstructured datasets with scale-restricted structured surveys.
Journal Article
A Runway Safety System Based on Vertically Oriented Stereovision
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.
Journal Article
Comprehensive Bird Preservation at Wind Farms
Wind as a clean and renewable energy source has been used by humans for centuries. However, in recent years with the increase in the number and size of wind turbines, their impact on avifauna has become worrisome. Researchers estimated that in the U.S. up to 500,000 birds die annually due to collisions with wind turbines. This article proposes a system for mitigating bird mortality around wind farms. The solution is based on a stereo-vision system embedded in distributed computing and IoT paradigms. After a bird’s detection in a defined zone, the decision-making system activates a collision avoidance routine composed of light and sound deterrents and the turbine stopping procedure. The development process applies a User-Driven Design approach along with the process of component selection and heuristic adjustment. This proposal includes a bird detection method and localization procedure. The bird identification is carried out using artificial intelligence algorithms. Validation tests with a fixed-wing drone and verifying observations by ornithologists proved the system’s desired reliability of detecting a bird with wingspan over 1.5 m from at least 300 m. Moreover, the suitability of the system to classify the size of the detected bird into one of three wingspan categories, small, medium and large, was confirmed.
Journal Article
Developing biodiversity indicators for African birds
Biodiversity indicators are essential for monitoring the impacts of pressures on the state of nature, determining the effectiveness of policy responses, and tracking progress towards biodiversity targets and sustainable development goals. Indicators based on trends in the abundance of birds are widely used for these purposes in Europe and have been identified as priorities for development elsewhere. To facilitate this we established bird population monitoring schemes in three African countries, based on citizen science approaches used in Europe, aiming to monitor population trends in common and widespread species. We recorded > 500 bird species from c. 450 2-km transects in Botswana, > 750 species from c. 120 transects in Uganda, and > 630 species from c. 90 transects in Kenya. Provisional Wild Bird Indices indicate a strong increase in bird populations in Botswana and a small decrease in Uganda. We also provide comparisons between trends of habitat generalists and specialists, of birds within and outside protected areas, and between Afro-Palearctic migrants and resident birds. Challenges encountered included recruiting, training and retaining volunteer surveyors, and securing long-term funding. However, we show that with technical support and modest investment (c. USD 30,000 per scheme per year), meaningful biodiversity indicators can be generated and used in African countries. Sustained resourcing for the existing schemes, and replication elsewhere, would be a cost-effective way to improve our understanding of biodiversity trends globally, and measure progress towards environmental goals.
Journal Article
Drones, automatic counting tools, and artificial neural networks in wildlife population censusing
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.
Journal Article