Hierarchical state-space estimation of leatherback turtle navigation ability

Foraging habits of Northwest Atlantic hooded seals over the past 30 years: Future habitat suitability under global warming

The Arctic is a global warming 'hot-spot' that is experiencing rapid increases in air and ocean temperatures and concomitant decreases in sea ice cover. These environmental changes are having major consequences on Arctic ecosystems. All Arctic endemic marine mammals are highly dependent on ice-associated ecosystems for at least part of their life cycle and thus are sensitive to the changes occurring in their habitats. Understanding the biological consequences of changes in these environments is essential for ecosystem management and conservation. However, our ability to study climate change impacts on Arctic marine mammals is generally limited by the lack of sufficiently long data time series. In this study, we took advantage of a unique dataset on hooded seal (Cystophora cristata) movements (and serum samples) that spans more than 30 years in the Northwest Atlantic to (i) investigate foraging (distribution and habitat use) and dietary (trophic level of prey and location) habits over the last three decades and (ii) predict future locations of suitable habitat given a projected global warming scenario. We found that, despite a change in isotopic signatures that might suggest prey changes over the 30-year period, hooded seals from the Northwest Atlantic appeared to target similar oceanographic characteristics throughout the study period. However, over decades, they have moved northward to find food. Somewhat surprisingly, foraging habits differed between seals breeding in the Gulf of St Lawrence vs those breeding at the "Front" (off Newfoundland). Seals from the Gulf favoured colder waters while Front seals favoured warmer waters. We predict that foraging habitats for hooded seals will continue to shift northwards and that Front seals are likely to have the greatest resilience. This study shows how hooded seals are responding to rapid environmental change and provides an indication of future trends for the species-information essential for effective ecosystem management and conservation.

Individual and joint estimation of humpback whale migratory patterns and their environmental drivers in the Southwest Atlantic Ocean

Humpback whales (Megaptera novaeangliae) perform seasonal migrations from high latitude feeding grounds to low latitude breeding and calving grounds. Feeding grounds at polar regions are currently experiencing major ecosystem modifications, therefore, quantitatively assessing species responses to habitat characteristics is crucial for understanding how whales might respond to such modifications. We analyzed satellite telemetry data from 22 individual humpback whales in the Southwest Atlantic Ocean (SWA). Tagging effort was divided in two periods, 2003-2012 and 2016-2019. Correlations between whale's movement parameters and environmental variables were used as proxy for inferring behavioral responses to environmental variation. Two versions of a covariate-driven continuous-time correlated random-walk state-space model, were fitted to the data: i) Population-level models (P-models), which assess correlation parameters pooling data across all individuals or groups, and ii) individual-level models (I-models), fitted independently for each tagged whale. Area of Restricted Search behavior (slower and less directionally persistent movement, ARS) was concentrated at cold waters south of the Polar Front (~ 50°S). The best model showed that ARS was expected to occur in coastal areas and over ridges and seamounts. Ice coverage during August of each year was a consistent predictor of ARS across models. Wind stress curl and sea surface temperature anomalies were also correlated with movement parameters but elicited larger inter-individual variation. I-models were consistent with P-models' predictions for the case of females accompanied by calves (mothers), while males and those of undetermined sex (males +) presented more variability as a group. Spatial predictions of humpback whale behavioral responses showed that feeding grounds for this population are concentrated in the complex system of islands, ridges, and rises of the Scotia Sea and the northern Weddell Ridge. More southernly incursions were observed in recent years, suggesting a potential response to increased temperature and large ice coverage reduction observed in the late 2010s. Although, small sample size and differences in tracking duration precluded appropriately testing predictions for such a distributional shift, our modelling framework showed the efficiency of borrowing statistical strength during data pooling, while pinpointing where more complexity should be added in the future as additional data become available.

Autumn movements of fin whales (Balaenoptera physalus) from Svalbard, Norway, revealed by satellite tracking

Insight into animal movements is essential for understanding habitat use by individuals as well as population processes and species life-history strategies. In this study, we instrumented 25 fin whales with ARGOS satellite-transmitters in Svalbard, Norway, to study their movement patterns and behaviour (Area Restricted Search (ARS), transiting or unknown) during boreal autumn/early winter. Ten of the whales stayed in the tagging area (most northerly location: 81.68°N) for their entire tracking periods (max 45 days). The other 15 whales moved in a south-westerly direction; the longest track ended off the coast of northern Africa (> 5000 km from the tagging location) after 96 days. The whales engaged in ARS behaviour intermittently throughout their southward migrations. During transit phases the whales moved quickly; one individual maintained an average horizontal speed of 9.3 km/h (travelling 223 km per day) for a period of a week. This study documents that: (1) some fin whales might remain at high latitudes during winter; (2) the whales that do migrate probably feed along the way; (3) they can maintain high transiting speed for long periods and; (4) one breeding area for this species is likely located in deep, warm water some 100 km west of Morocco.

State-space models' dirty little secrets: even simple linear Gaussian models can have estimation problems

State-space models (SSMs) are increasingly used in ecology to model time-series such as animal movement paths and population dynamics. This type of hierarchical model is often structured to account for two levels of variability: biological stochasticity and measurement error. SSMs are flexible. They can model linear and nonlinear processes using a variety of statistical distributions. Recent ecological SSMs are often complex, with a large number of parameters to estimate. Through a simulation study, we show that even simple linear Gaussian SSMs can suffer from parameter- and state-estimation problems. We demonstrate that these problems occur primarily when measurement error is larger than biological stochasticity, the condition that often drives ecologists to use SSMs. Using an animal movement example, we show how these estimation problems can affect ecological inference. Biased parameter estimates of a SSM describing the movement of polar bears (Ursus maritimus) result in overestimating their energy expenditure. We suggest potential solutions, but show that it often remains difficult to estimate parameters. While SSMs are powerful tools, they can give misleading results and we urge ecologists to assess whether the parameters can be estimated accurately before drawing ecological conclusions from their results.

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.

Path segmentation for beginners: an overview of current methods for detecting changes in animal movement patterns

Increased availability of high-resolution movement data has led to the development of numerous methods for studying changes in animal movement behavior. Path segmentation methods provide basics for detecting movement changes and the behavioral mechanisms driving them. However, available path segmentation methods differ vastly with respect to underlying statistical assumptions and output produced. Consequently, it is currently difficult for researchers new to path segmentation to gain an overview of the different methods, and choose one that is appropriate for their data and research questions. Here, we provide an overview of different methods for segmenting movement paths according to potential changes in underlying behavior. To structure our overview, we outline three broad types of research questions that are commonly addressed through path segmentation: 1) the quantitative description of movement patterns, 2) the detection of significant change-points, and 3) the identification of underlying processes or 'hidden states'. We discuss advantages and limitations of different approaches for addressing these research questions using path-level movement data, and present general guidelines for choosing methods based on data characteristics and questions. Our overview illustrates the large diversity of available path segmentation approaches, highlights the need for studies that compare the utility of different methods, and identifies opportunities for future developments in path-level data analysis.

Foraging behavior links climate variability and reproduction in North Pacific albatrosses

BACKGROUND

Climate-driven environmental change in the North Pacific has been well documented, with marked effects on the habitat and foraging behavior of marine predators. However, the mechanistic linkages connecting climate-driven changes in behavior to predator populations are not well understood. We evaluated the effects of climate-driven environmental variability on the reproductive success and foraging behavior of Laysan and Black-footed albatrosses breeding in the Northwest Hawaiian Islands during both brooding and incubating periods. We assessed foraging trip metrics and reproductive success using data collected from 2002-2012 and 1981-2012, respectively, relative to variability in the location of the Transition Zone Chlorophyll Front (TZCF, an important foraging region for albatrosses), sea surface temperature (SST), Multivariate ENSO Index (MEI), and the North Pacific Gyre Oscillation index (NPGO).

RESULTS

Foraging behavior for both species was influenced by climatic and oceanographic factors. While brooding chicks, both species traveled farther during La Niña conditions, when NPGO was high and when the TZCF was farther north (farther from the breeding site). Models showed that reproductive success for both species showed similar trends, correlating negatively with conditions observed during La Niña events (low MEI, high SST, high NPGO, increased distance to TZCF), but models for Laysan albatrosses explained a higher proportion of the variation. Spatial correlations of Laysan albatross reproductive success and SST anomalies highlighted strong negative correlations (>95 %) between habitat use and SST. Higher trip distance and/or duration during brooding were associated with decreased reproductive success.

CONCLUSIONS

Our findings suggest that during adverse conditions (La Niña conditions, high NPGO, northward displacement of the TZCF), both Laysan and Black-footed albatrosses took longer foraging trips and/or traveled farther during brooding, likely resulting in a lower reproductive success due to increased energetic costs. Our results link climate variability with both albatross behavior and reproductive success, information that is critical for predicting how albatross populations will respond to future climate change.

Assessing performance of Bayesian state-space models fit to Argos satellite telemetry locations processed with Kalman filtering

Argos recently implemented a new algorithm to calculate locations of satellite-tracked animals that uses a Kalman filter (KF). The KF algorithm is reported to increase the number and accuracy of estimated positions over the traditional Least Squares (LS) algorithm, with potential advantages to the application of state-space methods to model animal movement data. We tested the performance of two Bayesian state-space models (SSMs) fitted to satellite tracking data processed with KF algorithm. Tracks from 7 harbour seals (Phoca vitulina) tagged with ARGOS satellite transmitters equipped with Fastloc GPS loggers were used to calculate the error of locations estimated from SSMs fitted to KF and LS data, by comparing those to "true" GPS locations. Data on 6 fin whales (Balaenoptera physalus) were used to investigate consistency in movement parameters, location and behavioural states estimated by switching state-space models (SSSM) fitted to data derived from KF and LS methods. The model fit to KF locations improved the accuracy of seal trips by 27% over the LS model. 82% of locations predicted from the KF model and 73% of locations from the LS model were <5 km from the corresponding interpolated GPS position. Uncertainty in KF model estimates (5.6 ± 5.6 km) was nearly half that of LS estimates (11.6 ± 8.4 km). Accuracy of KF and LS modelled locations was sensitive to precision but not to observation frequency or temporal resolution of raw Argos data. On average, 88% of whale locations estimated by KF models fell within the 95% probability ellipse of paired locations from LS models. Precision of KF locations for whales was generally higher. Whales' behavioural mode inferred by KF models matched the classification from LS models in 94% of the cases. State-space models fit to KF data can improve spatial accuracy of location estimates over LS models and produce equally reliable behavioural estimates.

New Insights into Pelagic Migrations: Implications for Ecology and Conservation

Highly pelagic large marine vertebrates have evolved the capability of moving across large expanses of the marine environment; some species routinely move across entire ocean basins. Our understanding of these movements has been enhanced by new technologies that now allow us to follow their movements over great distances and long time periods in great detail. This technology provides not only detailed information on the movements of a wide variety of marine species, but also detailed characteristics of the habitats they use and clues to their navigation abilities. Advances in electronic tracking technologies have been coupled with rapid development of statistical and analytical techniques. With these developments, conservation of highly migratory species has been aided by providing new information on where uncommon or endangered species go, what behaviors they perform and why, which habitats are critical, and where they range, as well as, in many cases, better estimates of their population size and the interconnectedness of subpopulations. Together these tools are providing critical insights into the ecology of highly pelagic marine vertebrates that are key for their conservation and management.

Incorrect likelihood methods were used to infer scaling laws of marine predator search behaviour

Background

Ecologists are collecting extensive data concerning movements of animals in marine ecosystems. Such data need to be analysed with valid statistical methods to yield meaningful conclusions.

Principal Findings

We demonstrate methodological issues in two recent studies that reached similar conclusions concerning movements of marine animals (Nature 451∶1098; Science 332∶1551). The first study analysed vertical movement data to conclude that diverse marine predators (Atlantic cod, basking sharks, bigeye tuna, leatherback turtles and Magellanic penguins) exhibited “Lévy-walk-like behaviour”, close to a hypothesised optimal foraging strategy. By reproducing the original results for the bigeye tuna data, we show that the likelihood of tested models was calculated from residuals of regression fits (an incorrect method), rather than from the likelihood equations of the actual probability distributions being tested. This resulted in erroneous Akaike Information Criteria, and the testing of models that do not correspond to valid probability distributions. We demonstrate how this led to overwhelming support for a model that has no biological justification and that is statistically spurious because its probability density function goes negative. Re-analysis of the bigeye tuna data, using standard likelihood methods, overturns the original result and conclusion for that data set. The second study observed Lévy walk movement patterns by mussels. We demonstrate several issues concerning the likelihood calculations (including the aforementioned residuals issue). Re-analysis of the data rejects the original Lévy walk conclusion.

Conclusions

We consequently question the claimed existence of scaling laws of the search behaviour of marine predators and mussels, since such conclusions were reached using incorrect methods. We discourage the suggested potential use of “Lévy-like walks” when modelling consequences of fishing and climate change, and caution that any resulting advice to managers of marine ecosystems would be problematic. For reproducibility and future work we provide R source code for all calculations.