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Nauss, T.

Publications and source records attributed to Nauss, T..

2 recordsLinked to original sources

Automated non-lethal moth traps can be used for robust estimates of moth abundance

O_LIRecent reports of insect decline highlight the need for extensive large-scale insect monitoring. However, obtaining abundance or species richness data at high spatial and temporal resolution is difficult due to personnel, maintenance, and post-processing costs as well as ethical considerations. Non-invasive automated insect monitoring systems could provide a solution to address these constraints. However, every new insect monitoring design needs to be evaluated with respect to reliability and bias based on comparisons with conventional methods. C_LIO_LIIn this study, we evaluate the effectiveness of an automated moth trap (AMT), built from off-the-shelf-hardware, in capturing variations in moth abundance, by comparing it to a conventional, lethal trap. Both trap types were operated five times on 16 plots from the beginning of July 2021 to the end of August 2021. C_LIO_LIMoth abundance scaled isometrically between the two trap types. Consequently, the respective seasonal patterns in abundance determined over the monitoring period were similar. C_LIO_LIThe AMT samples phenological patterns using a robust and non-lethal method. However, an initial quantitative in-field test revealed that its long-term applicability must be preceded by several adjustments to the power supply and to data transfer. Depending on the software implementation, the AMT can be used to address a broad range of research questions while also reducing both energy expenditure and the disturbance of non-target animals. C_LI

ecology↗

Classifying the activity states of small vertebrates using automated VHF telemetry

O_LIThe most basic behavioural states of animals can be described as active or passive. However, while high-resolution observations of activity patterns can provide insights into the ecology of animal species, few methods are able to measure the activity of individuals of small taxa in their natural environment. We present a novel approach in which the automated VHF radio-tracking of small vertebrates fitted with lightweight transmitters (< 0.2 g) is used to distinguish between active and passive behavioural states. C_LIO_LIA dataset containing > 3 million VHF signals was used to train and test a random forest model in the assignment of either active or passive behaviour to individuals from two forest-dwelling bat species (Myotis bechsteinii (n = 50) and Nyctalus leisleri (n = 20)). The applicability of the model to other taxonomic groups was demonstrated by recording and classifying the behaviour of a tagged bird and by simulating the effect of different types of vertebrate activity with the help of humans carrying transmitters. The random forest model successfully classified the activity states of bats as well as those of birds and humans, although the latter were not included in model training (F-score 0.96-0.98). C_LIO_LIThe utility of the model in tackling ecologically relevant questions was demonstrated in a study of the differences in the daily activity patterns of the two bat species. The analysis showed a pronounced bimodal activity distribution of N. leisleri over the course of the night while the night-time activity of M. bechsteinii was relatively constant. These results show that significant differences in the timing of species activity according to ecological preferences or seasonality can be distinguished using our method. C_LIO_LIOur approach enables the assignment of VHF signal patterns to fundamental behavioural states with high precision and is applicable to different terrestrial and flying vertebrates. To encourage the broader use of our radio-tracking method, we provide the trained random forest models together with an R-package that includes all necessary data-processing functionalities. In combination with state-of-the-art open-source automated radio-tracking, this toolset can be used by the scientific community to investigate the activity patterns of small vertebrates with high temporal resolution, even in dense vegetation. C_LI

animal behavior and cognition↗