A hidden Markov movement model for rapidly identifying behavioral states from animal tracks.

Electronic telemetry is frequently used to document animal movement through time. Methods that can identify underlying behaviors driving specific movement patterns can help us understand how and why animals use available space, thereby aiding conservation and management efforts. For aquatic animal tracking data with significant measurement error, a Bayesian state-space model called the first-Difference Correlated Random Walk with Switching (DCRWS) has often been used for this purpose. However, for aquatic animals, highly accurate tracking data are now becoming more common. We developed a new hidden Markov model (HMM) for identifying behavioral states from animal tracks with negligible error, called the hidden Markov movement model (HMMM). We implemented as the basis for the HMMM the process equation of the DCRWS, but we used the method of maximum likelihood and the R package TMB for rapid model fitting. The HMMM was compared to a modified version of the DCRWS for highly accurate tracks, the DCRWSNOME, and to a common HMM for animal tracks fitted with the R package moveHMM. We show that the HMMM is both accurate and suitable for multiple species by fitting it to real tracks from a grey seal, lake trout, and blue shark, as well as to simulated data. The HMMM is a fast and reliable tool for making meaningful inference from animal movement data that is ideally suited for ecologists who want to use the popular DCRWS implementation and have highly accurate tracking data. It additionally provides a groundwork for development of more complex modeling of animal movement with TMB. To facilitate its uptake, we make it available through the R package swim.

Erratum: Condition-dependent migratory behaviour of endangered Atlantic salmon smolts moving through an inland sea.

[This corrects the article DOI: 10.1093/conphys/cow018.].

Condition-dependent migratory behaviour of endangered Atlantic salmon smolts moving through an inland sea.

The Bras d'Or Lake watershed of Cape Breton Island, Nova Scotia, Canada is a unique inland sea ecosystem, UNESCO Biosphere Reserve and home to a group of regionally distinct Atlantic salmon (Salmo salar) populations. Recent population decreases in this region have raised concern about their long-term persistence. We used acoustic telemetry to track the migrations of juvenile salmon (smolts) from the Middle River into the Bras d'Or Lake and, subsequently, into the Atlantic Ocean. Roughly half of the tagged smolts transited the Bras d'Or Lakes to the Atlantic Ocean, using a migration route that took them through the Gulf of St Lawrence's northern exit at the Strait of Belle Isle (∼650 km from the home river) towards feeding areas in the Labrador Sea and Greenland. However, a significant fraction spent >70 days in the Lakes, suggesting that this population has an alternative resident form, in which smolts limit their migrations within the Bras d'Or. Smolts in good relative condition (as determined from length-to-mass relationships) tended to be residents, whereas fish in poorer condition were ocean migrants. We also found a covarying effect of river temperature that helped to predict residence vs. ocean migration. We discuss these results relative to their bioenergetic implications and provide suggestions for future studies aimed at the conservation of declining salmon populations in Canada.

Fast fitting of non-Gaussian state-space models to animal movement data via Template Model Builder.

State-space models (SSM) are often used for analyzing complex ecological processes that are not observed directly, such as marine animal movement. When outliers are present in the measurements, special care is needed in the analysis to obtain reliable location and process estimates. Here we recommend using the Laplace approximation combined with automatic differentiation (as implemented in the novel R package Template Model Builder; TMB) for the fast fitting of continuous-time multivariate non-Gaussian SSMs. Through Argos satellite tracking data, we demonstrate that the use of continuous-time t-distributed measurement errors for error-prone data is more robust to outliers and improves the location estimation compared to using discretized-time t-distributed errors (implemented with a Gibbs sampler) or using continuous-time Gaussian errors (as with the Kalman filter). Using TMB, we are able to estimate additional parameters compared to previous methods, all without requiring a substantial increase in computational time. The model implementation is made available through the R package argosTrack.