Relationship between Ln (: oxygen consumption rate) and Ln (: carbon dioxide production rate) in incubating ancient murrlets at Reef Island in 2010 (<i>N</i> = 18 birds, <i>N</i> = 58 measurements).

<p>Relationship between Ln (: oxygen consumption rate) and Ln (: carbon dioxide production rate) in incubating ancient murrlets at Reef Island in 2010 (<i>N</i> = 18 birds, <i>N</i> = 58 measurements).</p

Baseline CORT levels (ng mL⁻¹) of 38 ancient murrelets by number of chicks that departed to sea.

<p>All samples were taken at the end of incubation period at Reef Island in 2008.</p

Schematic representation of a nest-box modified into a metabolic chamber (A) and airflow from a bird chamber to FOXBOX (B).

<p>Air pulled from a nest chamber into FOXBOX (A). Concentrations of CO₂ and O₂ inside a nest box were measured through FOXBOX (B).</p

The relationships between RQ and the length of fasting endurance, and CORT levels and reproductive success in ancient murrelets.

<p>The relationships between RQ and the length of fasting endurance, and CORT levels and reproductive success in ancient murrelets.</p

Frequency of fasting duration at the time of measurements of incubation metabolic rate in ancient murrlets at Reef Island in 2010.

<p>Frequency of fasting duration at the time of measurements of incubation metabolic rate in ancient murrlets at Reef Island in 2010.</p

Extending the Functionality of Behavioural Change-Point Analysis with <i>k</i>-Means Clustering: A Case Study with the Little Penguin (<i>Eudyptula minor</i>)

<div><p>We present a simple framework for classifying mutually exclusive behavioural states within the geospatial lifelines of animals. This method involves use of three sequentially applied statistical procedures: (1) <i>behavioural change point analysis</i> to partition movement trajectories into discrete bouts of same-state behaviours, based on abrupt changes in the spatio-temporal autocorrelation structure of movement parameters; (2) <i>hierarchical multivariate cluster analysis</i> to determine the number of different behavioural states; and (3) <i>k-means clustering</i> to classify inferred bouts of same-state location observations into behavioural modes. We demonstrate application of the method by analysing synthetic trajectories of known ‘artificial behaviours’ comprised of different correlated random walks, as well as real foraging trajectories of little penguins (<i>Eudyptula minor</i>) obtained by global-positioning-system telemetry. Our results show that the modelling procedure correctly classified 92.5% of all individual location observations in the synthetic trajectories, demonstrating reasonable ability to successfully discriminate behavioural modes. Most individual little penguins were found to exhibit three unique behavioural states (resting, commuting/active searching, area-restricted foraging), with variation in the timing and locations of observations apparently related to ambient light, bathymetry, and proximity to coastlines and river mouths. Addition of <i>k</i>-means clustering extends the utility of behavioural change point analysis, by providing a simple means through which the behaviours inferred for the location observations comprising individual movement trajectories can be objectively classified.</p></div

Time series of behavioural states inferred during foraging trips.

<p>Colours of vertical bars denote the different states—see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0122811#pone.0122811.g004" target="_blank">Fig 4</a> for interpretation; black bars denote missing data.</p

Kernel-density surface of inferred behavioural states superimposed on a map of the study area.

<p>Vertical height represents the relative areal density of locations classified as particular behavioural modes. The colours of the behavioural states are the same as those in Figs <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0122811#pone.0122811.g003" target="_blank">3</a> and <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0122811#pone.0122811.g004" target="_blank">4</a>.</p

Foraging trajectories of eight little penguins (<i>Eudyptula minor</i>) as recorded by GPS data-loggers.

<p>The nominal sampling interval of the loggers was 1 fix per min^-1. Colours represent tracks of different individuals. The location of the island study colony is indicated by red/yellow hatching. Sources of the background satellite images: Esri, DigitalGlobe, Earthstar Geographics, CNES/Airbus DS, GeoEye, USDA FSA, USGS, Getmapping, Aerogrid, IGN, IGP, swisstopo, and the GIS User Community.</p

Statistical definitions of behavioural states inferred for the location observations comprising eight penguin foraging trajectories.

<p>Behaviours were classified through sequential use of behavioural-change-point and <i>k</i>-means cluster analyses, based on combinations of inter-fix speeds (open circles, black lines) and relative turning angles (grey triangles and lines). Circles and triangles represent grand median values of all observations of all penguins, and the vertical bars represent the corresponding inter-quartile ranges.</p