Tag Archives: Hidden Markov Models

Movement Ecology: Stepping into the Mainstream

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Post provided by Theoni Photopoulou

“Movement is the glue that ties ecological processes together”
from Francesca Cagnacci et al. 2010

CTD-SRDL telemetry tags being primed for deployment. ©Theoni Photopoulou

CTD-SRDL telemetry tags being primed for deployment. ©Theoni Photopoulou

Movement ecology is a cross-disciplinary field. Its main aim is to quantitatively describe and understand how movement relates to individual and population-level processes for resource acquisition and, ultimately, survival. Today the study of movement ecology hinges on two 21st century advances:

  1. Animal-borne devices/tags (biologging science, Hooker et al., 2007) and/or remote sensing technology to quantify movement and collect data from remote or otherwise challenging environments
  2. Computational power sufficient to manipulate, process and analyse substantial volumes of data

Although datasets often involve small numbers of individuals, each individual can have thousands – sometimes even millions – of data points associated with it. Study species have tended to be large birds and mammals, due to the ease of tag attachment. However, the trend for miniaturisation of tags and the development of remote detection technologies (such as radar, e.g. Capaldi et al., 2000), have allowed researchers to track and study ever smaller animals. Continue reading →

Soaring with Eagles, Swimming with Sharks: Measuring Animal Behaviour with Hidden Markov Models

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Post provided by THEONI PHOTOPOULOU, MEGAN MURGATROYD, VIANEY LEOS-BARAJAS

Around the world there are concerns over the impacts of land use change and the developments (such as wind farms). These concerns have led to the implementation of tracking studies to better understand movement patterns of animals. Such studies have provided a wealth of high-resolution data and opportunities to explore sophisticated statistical methods for analysis of animal behaviour.

We use accelerometer data from aerial (Verreaux’s eagle in South Africa) and marine (blacktip reef shark in Hawai’i) systems to demonstrate the use of hidden Markov models (HMMs) in providing quantitative measures of behaviour. HMMs work really well for analysing animal accelerometer data because they account for serial autocorrelation in data. They allow for inferences to be made about relative activity and behaviour when animals cannot be directly observed too, which is very important.

In addition to this, 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 that we provide in ‘Analysis of animal accelerometer data using hidden Markov models‘ can be applied to a wide range of activity data. It opens up exciting opportunities for understanding drivers of individual animal behaviour.

The following images provide an inside view into the ecosystems in which the Verreaux’s eagle and blacktip reef shark reside.

Soaring with Veraux’s Eagles

Megan Murgatroyd equipping an eagle with a UvA-BiTS tag used to collect the data presented in our paper. These tags collect high-resolution GPS locations, altitude and three-axis acceleration data. They are able to collect large volumes of data because information is downloaded to a ground station rather than being transmitted via satellite. (© Gareth Tate)
Verreaux’s eagles intersperse their flights with periods of perching, which can be detected by low values of acceleration. Eagles like to perch on rocky outcrops and are usually found together. Both perching and flying behaviours persist for periods of time, e.g. birds can perch for 10min between flights of 45min, and this is explicitly taken into account by virtue of the structure of HMMs. (© Megan Murgatroyd)
Large stick nests are built on cliffs or rocky outcrops and are used for successive years. Especially during nest building and incubation, birds can spend quite a bit of time walking around on the nest, this is the type of behaviour that is likely captured by the low activity state described in the paper. (© Megan Murgatroyd)
A Verreaux’s eagle seen here soaring in its typical habitat. Updrafts on cliff faces likely aid soaring flight. Away from cliffs eagles rely on thermal lift to achieve low cost flights. We found that birds were more likely to remain in an active state at higher wind speeds, which are known to aid soaring flight especially over ridges. (© Megan Murgatroyd)
Verreaux’s eagles lay one to two eggs but only raise one chick. These are normally hatch during mid-winter and stay on the nest for around 90 days. The parents provision the chick during this time, delivering prey to the nest. Rock hyrax is a frequent item on the menu, as seen here. Having concurred data from accelerometers and nest cameras for the same bird could allow for activity level to be linked to successful prey acquisition or prey type. (Photo taken by a nest camera installed as part of the Black Eagle Project, Megan Murgatroyd)

Swimming with Blacktip Reef Sharks

How many sharks can we spot in this photo? The waves breaking in the background while the sharks cruise around the reef. Due to bans on fishing, large numbers of predators (sharks, snapper, and trevally) are found at the atoll, which makes it a perfect site to study blacktip reef sharks. (© Yannis P Papastamatiou)
A blacktip reef shark swims around ourresearch boat at Palmyra atoll. In total, we received accelerometer data from four blacktip reef sharks. We calculated overall dynamic body acceleration and used this metric as a proxy for general activity levels. The hidden Markov model was able to distinguish between high level and low level activity, allowing us to understand when the shark was more active. (© Yannis P Papastamatiou)
A blacktip reef shark cruising solo at the border between the backreef and forereef, where we see the waves breaking in the background. A few sharks had cameras attached, as well as the accelerometers. However, even with cameras it’s hard to know everything the shark is doing! Acceleration data provides a picture of what the shark is doing without having to spend all day underwater swimming after it. (© Yannis P Papastamatiou)
A view from the top of a blacktip reef shark. Applying a hidden Markov model to the observed overall dynamic body acceleration data, we discovered that the sharks use deeper water during the day when they were least active. (© Yuuki Y Watanabe)
Here comes the blacktip reef shark! Photographed during the day, when these sharks tend to be less active. They ‘rest’ during these hours in low energy environments, though, naturally, they never stop swimming! (© Yannis P Papastamatiou)

To find out more, read our Methods in Ecology and Evolution article ‘Analysis of animal accelerometer data using hidden Markov models’.

Issue 8.2

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Issue 8.2 is now online!

The February issue of Methods 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.

Continue reading →

Issue 7.11

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Issue 7.11 is now online!

The November issue of Methods is now online!

This month’s issue contains four Applications articles and two Open Access articles, all of which are freely available.

– moveHMM: This R package allows ecologists to process GPS tracking data into series of step lengths and turning angles, and to fit a hidden Markov model to these data, allowing, in particular, for the incorporation of environmental covariates.

– BORIS: An open-source and multiplatform standalone program that allows a user-specific coding environment to be set for a computer-based review of previously recorded videos or live observations. Being open to user-specific settings, the program allows a project-based ethogram to be defined that can then be shared with collaborators, or can be imported or modified.

– inbreedR: An R package that provides functions to measure variance in inbreeding – through the strength of correlation in heterozygosity across marker loci – based on microsatellite and SNP markers with associated P-values and confidence intervals. Within the framework of Heterozygosity–fitness correlation theory, inbreedR also estimates the impact of inbreeding on marker heterozygosity and fitness.

– Terrestrial Precipitation Analysis: This package is comprised of the Precipitation Trends (P-Trend), Precipitation Attributes (P-Att) and Precipitation Manipulation (P-Man) tools. Combined, these web tools allow researchers to easily calculate fundamental precipitation statistics for past, present and projected future precipitation regimes for any terrestrial location in the world.

Continue reading →

moveHMM: An Interview with Théo Michelot

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David Warton (University of New South Wales) interviews Théo Michelot (University of Sheffield) about an article on his recent R package moveHMM in Methods in Ecology and Evolution. David and Théo also discuss the case study in the paper – on the understudied wild haggis – and what advances could be made to the package in future.

Continue reading →