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The snake pipefish, Entelurus aequoreus (Linnaeus, 1758), is a slender, up to 60 cm long, northern Atlantic fish that dwells in open seagrass habitats and has recently expanded its distribution range. The snake pipefish is part of the family Syngnathidae (seahorses and pipefish) that has undergone several characteristic morphological changes, such as loss of pelvic fins and elongated snout. Here, we present a highly contiguous, near chromosome-scale genome of the snake pipefish assembled as part of a university master’s course. The final assembly has a length of 1.6 Gbp in 7,391 scaffolds, a scaffold and contig N50 of 62.3 Mbp and 45.0 Mbp and L50 of 12 and 14, respectively. The largest 28 scaffolds (>21 Mbp) span 89.7% of the assembly length. A BUSCO completeness score of 94.1% and a mapping rate above 98% suggest a high assembly completeness. Repetitive elements cover 74.93% of the genome, one of the highest proportions so far identified in vertebrate genomes. Demographic modeling using the PSMC framework indicates a peak in effective population size (50 – 100 kya) during the last interglacial period and suggests that the species might largely benefit from warmer water conditions, as seen today. Our updated snake pipefish assembly forms an important foundation for further analysis of the morphological and molecular changes unique to the family Syngnathidae.
The 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.
A 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).
The 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.
Our 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.