Issue 8.2 is now online!
This issue contains four(!) Applications articles and two Open Access articles. These six papers are freely available to everyone – no subscription required.
– Earth Mover’s Distance: The Earth Mover’s Distance (or EMD) is a method commonly used in image retrieval applications. The authors of this paper propose its use to calculate similarity in space use in the framework of movement ecology. This will be helpful for many questions regarding behavioural ecology, wildlife management and conservation.
– warbleR: The R package warbleR is a new package for the analysis of animal acoustic signal structure. It offers functions for downloading avian vocalisations from the open-access online repository Xeno-Canto, displaying the geographic extent of the recordings, manipulating sound files, detecting acoustic signals or importing detected signals from other software, and much more.
– meteR: The open-source R package, meteR directly calculates all of Maximum entropy theory of ecology’s (METE’s) predictions from a variety of data formats; automatically handles approximations and other technical details; and provides high-level plotting and model comparison functions to explore and interrogate models.
– Noise Egg: The Noise Egg is a device that can produce a low-frequency sound, which can be used as an experimental source of noise both in aquaria and in the field. It was developed to study the effects of noise on communication and behaviour in small aquatic animals; however, it could be used for other purposes, such as testing the propagation of certain frequencies in shallow-water habitats.
‘Mechanistic description of population dynamics using dynamic energy budget theory incorporated into integral projection models‘ by Isabel Smallegange et al. is the first Open Access article in this issue. In the article the authors use dynamic energy budget (DEB) theory to construct DEB-IPMs (Integral Projection Models) based on a mechanistic representation of individual life-history trajectories. They apply the DEB-IPM to two contrasting systems: the small, fast-reproducing bulb mite Rhizoglyphus robini and the large, slow-reproducing reef manta ray Manta alfredi. Their results show that DEB-IPMs provide a mechanistic platform to investigate the biological processes that determine joint change in phenotypic characters, life-history traits, population size and community structure.
Julie McInnes et al. provide this month’s second Open Access article: ‘Optimised scat collection protocols for dietary DNA metabarcoding in vertebrates‘. In this article, they developed scat collection protocols to optimise the detection of food DNA in vertebrate scat samples. The authors show that show that both the amount and type of non-target DNA vary with sample freshness, the collection substrate, fasting period and developmental stage of the consumer. A set of procedures for field scat collections to ensure high-quality samples for dietary DNA metabarcoding studies are also recommended.
This month’s cover image looks into the eye of a Verreaux’s eagle (Aquila verreauxii), a species is found in mountainous regions of sub-Saharan Africa, where cliffs provide suitable nesting habitat. The eagle pictured is equipped with a tracking device from the University of Amsterdam Bird Tracking System. In South Africa, concerns over the impacts of land use change and the development of wind farms have led to the implementation of tracking studies to better understand movement patterns of this majestic bird. Such studies have provided a wealth of high-resolution data and opportunities to explore sophisticated statistical methods for analysis of animal behaviour.
Leos-Barajas et al. use accelerometer data from aerial (Verreaux’s eagle) and marine (blacktip reef shark) systems to demonstrate the use of hidden Markov models (HMMs) in providing quantitative measures of behaviour. HMMs are well suited to analysing animal accelerometer data because they account for serial autocorrelation in data and importantly they allow for inferences to be made about relative activity and behaviour when animals cannot be directly observed. In addition, HMMs provide data-driven estimates of the underlying distributions of the acceleration metrics, and the probability of switching between states, possibly as a function of covariates. The framework provided in the author’s paper ‘Analysis of animal accelerometer data using hidden Markov models‘ can be applied to a wide range of activity data, thereby providing exciting opportunities for understanding drivers of individual animal behaviour.
Photo © Andrew Jenkins