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Context To assess the real impact of human-made structures on bird and bat communities, a significant number of carcass-removal trials has been performed worldwide in recent decades. Recently, researchers have started to use camera traps to record carcasses exact removal time and better understand the factors that influence this event. Aims In our study, we endeavoured to identify the factors that significantly affect carcass-persistence time, such as (1) season, (2) scavenger guild, (3) type of carcass, (4) habitat and (5) weather conditions. Additionally, we aimed to assess the performance of camera-trapping technology in comparison to the conventional method typically used in carcass-removal trials. Methods We conducted two trials in two wind farms during early spring and during summer season. In each trial, we used 30 bird carcasses and 30 mice carcasses as surrogates for bats. Digital infrared camera traps were used to monitor each carcass. Chi-squared test was used to investigate differences between wind farms regarding the scavenger guild. A log-rank test was used to compare carcass-persistence times for both wind farms. Carcass-persistence times were analysed using both non-parametric and parametric survival models. Finally, we evaluated the percentage of carcasses removed during the day time and night time. Key results In our study area, carcass-persistence times were influenced by the scavenger guild present and by the exposure to rain. Camera traps allowed to record the exact removal time for the majority of the carcasses, reducing the number of visits to the study site about five times. However, there were also cases wherein loss of data occurred as a result of equipment flaws or camera theft. Conclusions Results demonstrated the importance of undertaking site-specific carcass-removal trials. Use of camera-trap methodology is a valid option, reducing displacement costs. Costs related to equipment purchase and the risk of camera theft should be taken into consideration. Implications When choosing camera-trapping, the main aspect to evaluate is the balance between the investment in equipment purchase and the cost savings through reduced displacement costs. Further studies are required concerning the real effects of the data collected on the accuracy of carcass-removal correction factor obtained.

Radar systems have been increasingly used to monitor birds. To take full advantage of the large datasets provided by radars, researchers have implemented machine learning (ML) techniques that automatically read and attempt to classify targets. Here we used data collected from two locations in Portugal with two marine radar antennas (VSR and HSR) to apply and compare the performance of six ML algorithms that are widely used in the literature: random forests (RF), support vector machine (SVM), artificial neural networks (NN), linear discriminant analysis (LDA), quadratic discriminant analysis (QDA) and decision trees (DT), all trained with several dataset configurations. We found that all algorithms performed well (area under the receiver operating characteristic (AUC) and accuracy > 0.80, P < 0.001) when discriminating birds from non-biological targets such as vehicles, rain or wind turbines, but greater variance in the performance among algorithms was apparent when separating different bird functional groups or bird species (e.g. herons vs. gulls). In our case study, only RF was able to hold an accuracy > 0.80 for all classification tasks, although SVM and DT also performed well. Further, all algorithms correctly classified 86% and 66% (VSR and HSR) of the target points, and only 2% and 4% of these points were misclassified by all algorithms. Our results suggest that ML algorithms are suitable for classifying radar targets as birds, and thereby separating them from other non-biological targets. The ability of these algorithms to correctly identify among bird species functional groups was found to be much weaker, but if properly trained and supported by a good ground truthing dataset, targeted to the relevant species groups, some of these algorithms are still able to achieve high accuracies in classification tasks. Such results indicate that ML algorithms are suitable for use in near real-time monitoring of bird movements, and may help to mitigate collision of birds with, for example, wind turbines or airplanes.