Robust hierarchical state–space models reveal diel variation in travel rates of migrating leatherback turtles

Abstract

Summary1. Biological and statistical complexity are features common to most ecological data that hinder our ability to extract meaningful patterns using conventional tools. Recent work on implementing modern statistical methods for analysis of such ecological data has focused primarily on population dynamics but other types of data, such as animal movement pathways obtained from satellite telemetry, can also benefit from the application of modern statistical tools. 2. We develop a robust hierarchical state-space approach for analysis of multiple satellite telemetry pathways obtained via the Argos system. State-space models are time-series methods that allow unobserved states and biological parameters to be estimated from data observed with error. We show that the approach can reveal important patterns in complex, noisy data where conventional methods cannot. 3. Using the largest Atlantic satellite telemetry data set for critically endangered leatherback turtles, we show that the diel pattern in travel rates of these turtles changes over different phases of their migratory cycle. While foraging in northern waters the turtles show similar travel rates during day and night, but on their southward migration to tropical waters travel rates are markedly faster during the day. These patterns are generally consistent with diving data, and may be related to changes in foraging behaviour. Interestingly, individuals that migrate southward to breed generally show higher daytime travel rates than individuals that migrate southward in a non-breeding year. 4. Our approach is extremely flexible and can be applied to many ecological analyses that use complex, sequential data.

Bahamas connection: residence areas selected by breeding female loggerheads tagged in Dry Tortugas National Park, USA

“…Location data obtained through satellite transmitters are often received at irregular time intervals and sometimes involve large gaps and positional errors. Ad hoc filtering of location data based on location quality is not sufficient to remove erroneous locations and also results in loss of information [42]. Switching SSM estimates location and behavioral mode at regular time intervals, accounting for satellite positional errors and dynamics of the animal movement pattern [41] and is recommended as the best analytical technique for Argos tracking data once post processed by removing land points and adding back in good Argos locations [56].…”

“…The model was described in 2005 [41] and has previously been applied to the movement of marine animals including turtles [1,3,4,42,43,[46][47][48][49][50]56,80]. Location data obtained through satellite transmitters are often received at irregular time intervals and sometimes involve large gaps and positional errors.…”

“…The behavioral mode was binary, defined as ' ARS' or 'migration' as in earlier applications [42,44,45]; however, since we tagged animals during nesting seasons, we defined the behavioral mode as 'residence areas and/or nesting' or 'migration'. We summarized the Argos tracking data for the periods after migration away from nesting beaches, and during time periods with 'residence area' locations.…”

“…SSM has been used to identify locations when turtles show directed movements versus area-restricted search (ARS) patterns-deemed previously as migration and inter-nesting or foraging 'modes' [1,3,4,[40][41][42][43][44][45][46][47][48][49][50]. When combined with MCP (simple polygon created with home-range locations [51,52]) or KDE (a nonparametric method used to identify one or more areas of disproportionately heavy use [i.e.…”

Use of Electronic Tag Data and Associated Analytical Tools to Identify and Predict Habitat Utilization of Marine Mammals

“…First, we will use existing TOPP tracking data to generate overall utilization distributions as well as single species distributions and further categorize track segments by behavioral state using a combination of state spaced models and the fractal landscape method to determine regions of area restricted search (ARS) (Jonsen et al 2003;Jonsen et al 2006;Tremblay et al 2007). Next, we will model the links between oceanographic parameters and animal movement patterns.…”